code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''mobilenet_v2'''
def __init__( self :Any , _lowerCamelCase :List[str]=3 , _lowerCamelCase :Tuple=2_2_4 , _lowerCamelCase :List[Any]=1.0 , _lowerCamelCase :List[Any]=8 , _lowerCamelCase :Tuple=8 , _lowerCamelCase :Optional[Any]=6 , _lowerCamelCase :Dict=3_2 , _lowerCamelCase :Dict=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :List[str]="relu6" , _lowerCamelCase :Any=True , _lowerCamelCase :Any=0.8 , _lowerCamelCase :List[str]=0.0_2 , _lowerCamelCase :Any=0.0_0_1 , _lowerCamelCase :Union[str, Any]=2_5_5 , **_lowerCamelCase :str , ):
super().__init__(**_lowerCamelCase )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
__SCREAMING_SNAKE_CASE : List[str] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = image_size
__SCREAMING_SNAKE_CASE : Optional[Any] = depth_multiplier
__SCREAMING_SNAKE_CASE : Tuple = depth_divisible_by
__SCREAMING_SNAKE_CASE : List[Any] = min_depth
__SCREAMING_SNAKE_CASE : Any = expand_ratio
__SCREAMING_SNAKE_CASE : str = output_stride
__SCREAMING_SNAKE_CASE : Optional[int] = first_layer_is_expansion
__SCREAMING_SNAKE_CASE : List[str] = finegrained_output
__SCREAMING_SNAKE_CASE : Dict = hidden_act
__SCREAMING_SNAKE_CASE : int = tf_padding
__SCREAMING_SNAKE_CASE : List[str] = classifier_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
__SCREAMING_SNAKE_CASE : List[Any] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = semantic_loss_ignore_index
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})] )
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})] )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return 1e-4
| 674 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 1 |
"""simple docstring"""
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = os.path.dirname(os.path.realpath(lowercase_ ) )
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(lowercase_ , '''triangle.txt''' )
with open(lowercase_ ) as f:
__SCREAMING_SNAKE_CASE : Dict = f.readlines()
__SCREAMING_SNAKE_CASE : Optional[int] = []
for line in triangle:
__SCREAMING_SNAKE_CASE : Union[str, Any] = []
for number in line.strip().split(''' ''' ):
numbers_from_line.append(int(lowercase_ ) )
a.append(lowercase_ )
for i in range(1 , len(lowercase_ ) ):
for j in range(len(a[i] ) ):
__SCREAMING_SNAKE_CASE : Optional[int] = a[i - 1][j] if j != len(a[i - 1] ) else 0
__SCREAMING_SNAKE_CASE : Optional[Any] = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(lowercase_ , lowercase_ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 674 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 1 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class snake_case :
def __init__( self :Any , _lowerCamelCase :List[Any] , _lowerCamelCase :int=2 , _lowerCamelCase :Tuple=8 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Optional[int]=True , _lowerCamelCase :Optional[int]=True , _lowerCamelCase :List[str]=True , _lowerCamelCase :Union[str, Any]=9_9 , _lowerCamelCase :int=1_6 , _lowerCamelCase :str=5 , _lowerCamelCase :List[Any]=2 , _lowerCamelCase :int=3_6 , _lowerCamelCase :Optional[Any]="gelu" , _lowerCamelCase :int=0.0 , _lowerCamelCase :Union[str, Any]=0.0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Any=1_6 , _lowerCamelCase :Any=2 , _lowerCamelCase :Union[str, Any]=0.0_2 , _lowerCamelCase :Union[str, Any]=3 , _lowerCamelCase :Tuple=4 , _lowerCamelCase :Dict=None , ):
__SCREAMING_SNAKE_CASE : Optional[Any] = parent
__SCREAMING_SNAKE_CASE : Optional[Any] = batch_size
__SCREAMING_SNAKE_CASE : Dict = seq_length
__SCREAMING_SNAKE_CASE : Tuple = is_training
__SCREAMING_SNAKE_CASE : List[str] = use_input_mask
__SCREAMING_SNAKE_CASE : Union[str, Any] = use_token_type_ids
__SCREAMING_SNAKE_CASE : Optional[int] = use_labels
__SCREAMING_SNAKE_CASE : Any = vocab_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_size
__SCREAMING_SNAKE_CASE : List[str] = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : List[Any] = max_position_embeddings
__SCREAMING_SNAKE_CASE : int = type_vocab_size
__SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
__SCREAMING_SNAKE_CASE : List[str] = initializer_range
__SCREAMING_SNAKE_CASE : Tuple = num_labels
__SCREAMING_SNAKE_CASE : List[str] = num_choices
__SCREAMING_SNAKE_CASE : List[str] = scope
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : List[str] = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE : int = random_attention_mask([self.batch_size, self.seq_length] )
__SCREAMING_SNAKE_CASE : List[Any] = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
__SCREAMING_SNAKE_CASE : List[Any] = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices )
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE_ ( self :int ):
return MraConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Any = self.get_config()
__SCREAMING_SNAKE_CASE : str = 3_0_0
return config
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) : List[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = True
__SCREAMING_SNAKE_CASE : List[str] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Any , _lowerCamelCase :List[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Any = MraModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase , token_type_ids=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[Any] , _lowerCamelCase :int , _lowerCamelCase :List[Any] , _lowerCamelCase :Any , _lowerCamelCase :List[str] , _lowerCamelCase :int , _lowerCamelCase :Optional[int] , _lowerCamelCase :Any , _lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Optional[Any] = MraModel(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[int] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , encoder_attention_mask=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : List[Any] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :Tuple , _lowerCamelCase :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = MraForMaskedLM(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[Any] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int , _lowerCamelCase :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Any , _lowerCamelCase :List[str] , _lowerCamelCase :List[Any] ):
__SCREAMING_SNAKE_CASE : str = MraForQuestionAnswering(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , start_positions=_lowerCamelCase , end_positions=_lowerCamelCase , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :List[Any] , _lowerCamelCase :str , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.num_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = MraForSequenceClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :List[str] , _lowerCamelCase :str , _lowerCamelCase :List[Any] , _lowerCamelCase :Any , _lowerCamelCase :Any , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : List[Any] = self.num_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = MraForTokenClassification(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[str] , _lowerCamelCase :str , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Any , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : List[Any] = self.num_choices
__SCREAMING_SNAKE_CASE : int = MraForMultipleChoice(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__SCREAMING_SNAKE_CASE : Any = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__SCREAMING_SNAKE_CASE : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__SCREAMING_SNAKE_CASE : Optional[Any] = model(
_lowerCamelCase , attention_mask=_lowerCamelCase , token_type_ids=_lowerCamelCase , labels=_lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Dict = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) : List[str] = config_and_inputs
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = ()
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : str = MraModelTester(self )
__SCREAMING_SNAKE_CASE : Dict = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=3_7 )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__SCREAMING_SNAKE_CASE : str = type
self.model_tester.create_and_check_model(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[Any] = MraModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skip(reason='''MRA does not output attentions''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return
@require_torch
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = MraModel.from_pretrained('''uw-madison/mra-base-512-4''' )
__SCREAMING_SNAKE_CASE : str = torch.arange(2_5_6 ).unsqueeze(0 )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(_lowerCamelCase )[0]
__SCREAMING_SNAKE_CASE : List[Any] = torch.Size((1, 2_5_6, 7_6_8) )
self.assertEqual(output.shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor(
[[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4''' )
__SCREAMING_SNAKE_CASE : str = torch.arange(2_5_6 ).unsqueeze(0 )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(_lowerCamelCase )[0]
__SCREAMING_SNAKE_CASE : Optional[int] = 5_0_2_6_5
__SCREAMING_SNAKE_CASE : str = torch.Size((1, 2_5_6, vocab_size) )
self.assertEqual(output.shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Dict = MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3''' )
__SCREAMING_SNAKE_CASE : Any = torch.arange(4_0_9_6 ).unsqueeze(0 )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(_lowerCamelCase )[0]
__SCREAMING_SNAKE_CASE : str = 5_0_2_6_5
__SCREAMING_SNAKE_CASE : Dict = torch.Size((1, 4_0_9_6, vocab_size) )
self.assertEqual(output.shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowerCamelCase , atol=1e-4 ) )
| 674 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__SCREAMING_SNAKE_CASE : Optional[int] = hex_num[0] == '''-'''
if is_negative:
__SCREAMING_SNAKE_CASE : Optional[int] = hex_num[1:]
try:
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(lowercase_ , 16 )
except ValueError:
raise ValueError('''Invalid value was passed to the function''' )
__SCREAMING_SNAKE_CASE : List[str] = ''''''
while int_num > 0:
__SCREAMING_SNAKE_CASE : Union[str, Any] = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(('''-''' + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowerCAmelCase_ ( lowercase_ : List[Any]="ro" , lowercase_ : Dict="en" , lowercase_ : Optional[Any]="wmt16" , lowercase_ : List[Any]=None ):
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
__SCREAMING_SNAKE_CASE : int = F'''{src_lang}-{tgt_lang}'''
print(F'''Converting {dataset}-{pair}''' )
__SCREAMING_SNAKE_CASE : str = datasets.load_dataset(lowercase_ , lowercase_ )
if save_dir is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = F'''{dataset}-{pair}'''
__SCREAMING_SNAKE_CASE : int = Path(lowercase_ )
save_dir.mkdir(exist_ok=lowercase_ )
for split in ds.keys():
print(F'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
__SCREAMING_SNAKE_CASE : Optional[int] = '''val''' if split == '''validation''' else split
__SCREAMING_SNAKE_CASE : Any = save_dir.joinpath(F'''{fn}.source''' )
__SCREAMING_SNAKE_CASE : Any = save_dir.joinpath(F'''{fn}.target''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = src_path.open('''w+''' )
__SCREAMING_SNAKE_CASE : int = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
__SCREAMING_SNAKE_CASE : Optional[int] = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(F'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 674 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__UpperCAmelCase ):
lowerCamelCase__ = ['''torch''', '''scipy''']
def __init__( self :Optional[int] , *_lowerCamelCase :Tuple , **_lowerCamelCase :int ):
requires_backends(self , ['''torch''', '''scipy'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :int , *_lowerCamelCase :Optional[int] , **_lowerCamelCase :int ):
requires_backends(cls , ['''torch''', '''scipy'''] )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Any , *_lowerCamelCase :Optional[Any] , **_lowerCamelCase :Optional[int] ):
requires_backends(cls , ['''torch''', '''scipy'''] )
| 674 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int = 50 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f'{solution() = }')
| 674 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = XGLMTokenizer
lowerCamelCase__ = XGLMTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE : List[str] = XGLMTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Dict = '''<pad>'''
__SCREAMING_SNAKE_CASE : int = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(len(_lowerCamelCase ) , 1_0_0_8 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_8 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = XGLMTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowerCamelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
__SCREAMING_SNAKE_CASE : Any = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
] , )
__SCREAMING_SNAKE_CASE : str = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return XGLMTokenizer.from_pretrained('''facebook/xglm-564M''' )
def SCREAMING_SNAKE_CASE_ ( self :str ):
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(_lowerCamelCase , f.name )
__SCREAMING_SNAKE_CASE : Optional[int] = XGLMTokenizer(f.name , keep_accents=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = pickle.dumps(_lowerCamelCase )
pickle.loads(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.test_rust_tokenizer:
return
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE : str = '''I was born in 92000, and this is falsé.'''
__SCREAMING_SNAKE_CASE : str = tokenizer.tokenize(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = rust_tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = rust_tokenizer.encode(_lowerCamelCase , add_special_tokens=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE : int = tokenizer.encode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = rust_tokenizer.encode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : str = '''Hello World!'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = [2, 3_1_2_2_7, 4_4_4_7, 3_5]
self.assertListEqual(_lowerCamelCase , self.big_tokenizer.encode(_lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
'''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
''' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'''
)
# fmt: off
__SCREAMING_SNAKE_CASE : Any = [2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
# fmt: on
self.assertListEqual(_lowerCamelCase , self.big_tokenizer.encode(_lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# fmt: off
__SCREAMING_SNAKE_CASE : str = {
'''input_ids''': [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
'''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name='''facebook/xglm-564M''' , padding=_lowerCamelCase , )
| 674 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
_lowerCamelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
if isinstance(lowercase_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase_ ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = ['''pixel_values''']
def __init__( self :int , _lowerCamelCase :bool = True , _lowerCamelCase :Dict[str, int] = None , _lowerCamelCase :PILImageResampling = PILImageResampling.BILINEAR , _lowerCamelCase :bool = True , _lowerCamelCase :Dict[str, int] = None , _lowerCamelCase :bool = True , _lowerCamelCase :Union[int, float] = 1 / 2_5_5 , _lowerCamelCase :bool = True , _lowerCamelCase :Optional[Union[float, List[float]]] = None , _lowerCamelCase :Optional[Union[float, List[float]]] = None , **_lowerCamelCase :List[Any] , ):
super().__init__(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = size if size is not None else {'''shortest_edge''': 2_2_4}
__SCREAMING_SNAKE_CASE : Any = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
__SCREAMING_SNAKE_CASE : str = get_size_dict(_lowerCamelCase , param_name='''crop_size''' )
__SCREAMING_SNAKE_CASE : str = do_resize
__SCREAMING_SNAKE_CASE : str = size
__SCREAMING_SNAKE_CASE : Dict = do_center_crop
__SCREAMING_SNAKE_CASE : Tuple = crop_size
__SCREAMING_SNAKE_CASE : Dict = resample
__SCREAMING_SNAKE_CASE : Any = do_rescale
__SCREAMING_SNAKE_CASE : Tuple = rescale_factor
__SCREAMING_SNAKE_CASE : Optional[Any] = do_normalize
__SCREAMING_SNAKE_CASE : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__SCREAMING_SNAKE_CASE : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :np.ndarray , _lowerCamelCase :Dict[str, int] , _lowerCamelCase :PILImageResampling = PILImageResampling.BILINEAR , _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **_lowerCamelCase :Optional[int] , ):
__SCREAMING_SNAKE_CASE : Dict = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase )
if "shortest_edge" in size:
__SCREAMING_SNAKE_CASE : Dict = get_resize_output_image_size(_lowerCamelCase , size['''shortest_edge'''] , default_to_square=_lowerCamelCase )
elif "height" in size and "width" in size:
__SCREAMING_SNAKE_CASE : Tuple = (size['''height'''], size['''width'''])
else:
raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :np.ndarray , _lowerCamelCase :Dict[str, int] , _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **_lowerCamelCase :Union[str, Any] , ):
__SCREAMING_SNAKE_CASE : Optional[Any] = get_size_dict(_lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(_lowerCamelCase , size=(size['''height'''], size['''width''']) , data_format=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :np.ndarray , _lowerCamelCase :Union[int, float] , _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **_lowerCamelCase :Dict , ):
return rescale(_lowerCamelCase , scale=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :np.ndarray , _lowerCamelCase :Union[float, List[float]] , _lowerCamelCase :Union[float, List[float]] , _lowerCamelCase :Optional[Union[str, ChannelDimension]] = None , **_lowerCamelCase :int , ):
return normalize(_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :ImageInput , _lowerCamelCase :bool = None , _lowerCamelCase :Dict[str, int] = None , _lowerCamelCase :PILImageResampling = None , _lowerCamelCase :bool = None , _lowerCamelCase :Dict[str, int] = None , _lowerCamelCase :bool = None , _lowerCamelCase :float = None , _lowerCamelCase :bool = None , _lowerCamelCase :Optional[Union[float, List[float]]] = None , _lowerCamelCase :Optional[Union[float, List[float]]] = None , _lowerCamelCase :Optional[ChannelDimension] = ChannelDimension.FIRST , ):
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE : Dict = to_numpy_array(_lowerCamelCase )
if do_resize:
__SCREAMING_SNAKE_CASE : Tuple = self.resize(image=_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase )
if do_center_crop:
__SCREAMING_SNAKE_CASE : str = self.center_crop(_lowerCamelCase , size=_lowerCamelCase )
if do_rescale:
__SCREAMING_SNAKE_CASE : List[Any] = self.rescale(image=_lowerCamelCase , scale=_lowerCamelCase )
if do_normalize:
__SCREAMING_SNAKE_CASE : List[str] = self.normalize(image=_lowerCamelCase , mean=_lowerCamelCase , std=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = to_channel_dimension_format(_lowerCamelCase , _lowerCamelCase )
return image
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :ImageInput , _lowerCamelCase :bool = None , _lowerCamelCase :Dict[str, int] = None , _lowerCamelCase :PILImageResampling = None , _lowerCamelCase :bool = None , _lowerCamelCase :Dict[str, int] = None , _lowerCamelCase :bool = None , _lowerCamelCase :float = None , _lowerCamelCase :bool = None , _lowerCamelCase :Optional[Union[float, List[float]]] = None , _lowerCamelCase :Optional[Union[float, List[float]]] = None , _lowerCamelCase :Optional[Union[str, TensorType]] = None , _lowerCamelCase :ChannelDimension = ChannelDimension.FIRST , **_lowerCamelCase :Union[str, Any] , ):
__SCREAMING_SNAKE_CASE : int = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE : Dict = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
__SCREAMING_SNAKE_CASE : str = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE : List[str] = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE : Optional[int] = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE : Any = size if size is not None else self.size
__SCREAMING_SNAKE_CASE : Optional[Any] = get_size_dict(_lowerCamelCase , default_to_square=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = crop_size if crop_size is not None else self.crop_size
__SCREAMING_SNAKE_CASE : Dict = get_size_dict(_lowerCamelCase , param_name='''crop_size''' )
if not valid_images(_lowerCamelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
__SCREAMING_SNAKE_CASE : str = make_batched(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = [
[
self._preprocess_image(
image=_lowerCamelCase , do_resize=_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase , do_center_crop=_lowerCamelCase , crop_size=_lowerCamelCase , do_rescale=_lowerCamelCase , rescale_factor=_lowerCamelCase , do_normalize=_lowerCamelCase , image_mean=_lowerCamelCase , image_std=_lowerCamelCase , data_format=_lowerCamelCase , )
for img in video
]
for video in videos
]
__SCREAMING_SNAKE_CASE : List[str] = {'''pixel_values''': videos}
return BatchFeature(data=_lowerCamelCase , tensor_type=_lowerCamelCase )
| 674 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 1 |
"""simple docstring"""
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCamelCase = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase = importlib.util.spec_from_file_location(
'''transformers''',
os.path.join(PATH_TO_TRANSFORMERS, '''__init__.py'''),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
_lowerCamelCase = spec.loader.load_module()
_lowerCamelCase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCamelCase = re.compile('''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
_lowerCamelCase = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = []
for config_class in list(CONFIG_MAPPING.values() ):
__SCREAMING_SNAKE_CASE : Optional[Any] = False
# source code of `config_class`
__SCREAMING_SNAKE_CASE : Any = inspect.getsource(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = _re_checkpoint.findall(lowercase_ )
for checkpoint in checkpoints:
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
__SCREAMING_SNAKE_CASE : Tuple = F'''https://huggingface.co/{ckpt_name}'''
if ckpt_link == ckpt_link_from_name:
__SCREAMING_SNAKE_CASE : Any = True
break
__SCREAMING_SNAKE_CASE : Optional[Any] = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(lowercase_ )
if len(lowercase_ ) > 0:
__SCREAMING_SNAKE_CASE : Optional[int] = '''\n'''.join(sorted(lowercase_ ) )
raise ValueError(F'''The following configurations don\'t contain any valid checkpoint:\n{message}''' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 674 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 1 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = OmegaConf.load(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = torch.load(lowercase_ , map_location='''cpu''' )['''model''']
__SCREAMING_SNAKE_CASE : List[str] = list(state_dict.keys() )
# extract state_dict for VQVAE
__SCREAMING_SNAKE_CASE : Union[str, Any] = {}
__SCREAMING_SNAKE_CASE : List[Any] = '''first_stage_model.'''
for key in keys:
if key.startswith(lowercase_ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict[key]
# extract state_dict for UNetLDM
__SCREAMING_SNAKE_CASE : List[Any] = {}
__SCREAMING_SNAKE_CASE : List[str] = '''model.diffusion_model.'''
for key in keys:
if key.startswith(lowercase_ ):
__SCREAMING_SNAKE_CASE : Optional[int] = state_dict[key]
__SCREAMING_SNAKE_CASE : Dict = config.model.params.first_stage_config.params
__SCREAMING_SNAKE_CASE : Union[str, Any] = config.model.params.unet_config.params
__SCREAMING_SNAKE_CASE : Optional[Any] = VQModel(**lowercase_ ).eval()
vqvae.load_state_dict(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = UNetLDMModel(**lowercase_ ).eval()
unet.load_state_dict(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule='''scaled_linear''' , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=lowercase_ , )
__SCREAMING_SNAKE_CASE : Tuple = LDMPipeline(lowercase_ , lowercase_ , lowercase_ )
pipeline.save_pretrained(lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--checkpoint_path''', type=str, required=True)
parser.add_argument('''--config_path''', type=str, required=True)
parser.add_argument('''--output_path''', type=str, required=True)
_lowerCamelCase = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 674 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = ''''''
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return data[1:] + data[0]
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : List[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = ''''''
for i in range(len(lowercase_ ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = int('''0b''' + data[0] + data[-1] , 2 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int('''0b''' + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = message[:4]
__SCREAMING_SNAKE_CASE : Any = message[4:]
__SCREAMING_SNAKE_CASE : int = apply_table(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : str = xor(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = apply_sbox(lowercase_ , temp[:4] ) # noqa: E741
__SCREAMING_SNAKE_CASE : Tuple = apply_sbox(lowercase_ , temp[4:] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''0''' * (2 - len(lowercase_ )) + l # noqa: E741
__SCREAMING_SNAKE_CASE : Tuple = '''0''' * (2 - len(lowercase_ )) + r
__SCREAMING_SNAKE_CASE : Any = apply_table(l + r , lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = xor(lowercase_ , lowercase_ )
return temp + right
if __name__ == "__main__":
_lowerCamelCase = input('''Enter 10 bit key: ''')
_lowerCamelCase = input('''Enter 8 bit message: ''')
_lowerCamelCase = [6, 3, 7, 4, 8, 5, 10, 9]
_lowerCamelCase = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
_lowerCamelCase = [2, 4, 3, 1]
_lowerCamelCase = [2, 6, 3, 1, 4, 8, 5, 7]
_lowerCamelCase = [4, 1, 3, 5, 7, 2, 8, 6]
_lowerCamelCase = [4, 1, 2, 3, 2, 3, 4, 1]
_lowerCamelCase = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
_lowerCamelCase = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
_lowerCamelCase = apply_table(key, paa_table)
_lowerCamelCase = temp[:5]
_lowerCamelCase = temp[5:]
_lowerCamelCase = left_shift(left)
_lowerCamelCase = left_shift(right)
_lowerCamelCase = apply_table(left + right, pa_table)
_lowerCamelCase = left_shift(left)
_lowerCamelCase = left_shift(right)
_lowerCamelCase = left_shift(left)
_lowerCamelCase = left_shift(right)
_lowerCamelCase = apply_table(left + right, pa_table)
# encryption
_lowerCamelCase = apply_table(message, IP)
_lowerCamelCase = function(expansion, sa, sa, keya, temp)
_lowerCamelCase = temp[4:] + temp[:4]
_lowerCamelCase = function(expansion, sa, sa, keya, temp)
_lowerCamelCase = apply_table(temp, IP_inv)
print('''Cipher text is:''', CT)
# decryption
_lowerCamelCase = apply_table(CT, IP)
_lowerCamelCase = function(expansion, sa, sa, keya, temp)
_lowerCamelCase = temp[4:] + temp[:4]
_lowerCamelCase = function(expansion, sa, sa, keya, temp)
_lowerCamelCase = apply_table(temp, IP_inv)
print('''Plain text after decypting is:''', PT)
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 1 |
"""simple docstring"""
import os
def lowerCAmelCase_ ( lowercase_ : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowercase_ ) , lowercase_ ) ) as input_file:
__SCREAMING_SNAKE_CASE : Tuple = [
[int(lowercase_ ) for element in line.split(''',''' )]
for line in input_file.readlines()
]
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = len(matrix[0] )
__SCREAMING_SNAKE_CASE : Any = [[-1 for _ in range(lowercase_ )] for _ in range(lowercase_ )]
for i in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = matrix[i][0]
for j in range(1 , lowercase_ ):
for i in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : Dict = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , lowercase_ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] )
for i in range(rows - 2 , -1 , -1 ):
__SCREAMING_SNAKE_CASE : Optional[int] = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] )
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )
if __name__ == "__main__":
print(f'{solution() = }')
| 674 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int = 400_0000 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = []
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowercase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = b, a + b
return sum(lowercase_ )
if __name__ == "__main__":
print(f'{solution() = }')
| 674 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def lowerCAmelCase_ ( lowercase_ : Dict ):
'''simple docstring'''
return x + 2
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : int = '''x = 3'''
__SCREAMING_SNAKE_CASE : Tuple = {}
__SCREAMING_SNAKE_CASE : Optional[int] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
assert result == 3
self.assertDictEqual(_lowerCamelCase , {'''x''': 3} )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''x = y'''
__SCREAMING_SNAKE_CASE : List[Any] = {'''y''': 5}
__SCREAMING_SNAKE_CASE : Optional[Any] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_lowerCamelCase , {'''x''': 5, '''y''': 5} )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = '''y = add_two(x)'''
__SCREAMING_SNAKE_CASE : List[Any] = {'''x''': 3}
__SCREAMING_SNAKE_CASE : Optional[Any] = evaluate(_lowerCamelCase , {'''add_two''': add_two} , state=_lowerCamelCase )
assert result == 5
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''y''': 5} )
# Won't work without the tool
with CaptureStdout() as out:
__SCREAMING_SNAKE_CASE : List[str] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
assert result is None
assert "tried to execute add_two" in out.out
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = '''x = 3'''
__SCREAMING_SNAKE_CASE : List[str] = {}
__SCREAMING_SNAKE_CASE : List[Any] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
assert result == 3
self.assertDictEqual(_lowerCamelCase , {'''x''': 3} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : int = '''test_dict = {\'x\': x, \'y\': add_two(x)}'''
__SCREAMING_SNAKE_CASE : List[str] = {'''x''': 3}
__SCREAMING_SNAKE_CASE : Optional[Any] = evaluate(_lowerCamelCase , {'''add_two''': add_two} , state=_lowerCamelCase )
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''y''': 5} )
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Any = '''x = 3\ny = 5'''
__SCREAMING_SNAKE_CASE : int = {}
__SCREAMING_SNAKE_CASE : str = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''y''': 5} )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = '''text = f\'This is x: {x}.\''''
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''x''': 3}
__SCREAMING_SNAKE_CASE : Any = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''text''': '''This is x: 3.'''} )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''if x <= 3:\n y = 2\nelse:\n y = 5'''
__SCREAMING_SNAKE_CASE : int = {'''x''': 3}
__SCREAMING_SNAKE_CASE : List[Any] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''y''': 2} )
__SCREAMING_SNAKE_CASE : List[Any] = {'''x''': 8}
__SCREAMING_SNAKE_CASE : int = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(_lowerCamelCase , {'''x''': 8, '''y''': 5} )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : int = '''test_list = [x, add_two(x)]'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''x''': 3}
__SCREAMING_SNAKE_CASE : Optional[Any] = evaluate(_lowerCamelCase , {'''add_two''': add_two} , state=_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , [3, 5] )
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''test_list''': [3, 5]} )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''y = x'''
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''x''': 3}
__SCREAMING_SNAKE_CASE : Optional[int] = evaluate(_lowerCamelCase , {} , state=_lowerCamelCase )
assert result == 3
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''y''': 3} )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = '''test_list = [x, add_two(x)]\ntest_list[1]'''
__SCREAMING_SNAKE_CASE : List[str] = {'''x''': 3}
__SCREAMING_SNAKE_CASE : Optional[int] = evaluate(_lowerCamelCase , {'''add_two''': add_two} , state=_lowerCamelCase )
assert result == 5
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''test_list''': [3, 5]} )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
__SCREAMING_SNAKE_CASE : Dict = {'''x''': 3}
__SCREAMING_SNAKE_CASE : int = evaluate(_lowerCamelCase , {'''add_two''': add_two} , state=_lowerCamelCase )
assert result == 5
self.assertDictEqual(_lowerCamelCase , {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Any = '''x = 0\nfor i in range(3):\n x = i'''
__SCREAMING_SNAKE_CASE : Tuple = {}
__SCREAMING_SNAKE_CASE : str = evaluate(_lowerCamelCase , {'''range''': range} , state=_lowerCamelCase )
assert result == 2
self.assertDictEqual(_lowerCamelCase , {'''x''': 2, '''i''': 2} )
| 674 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 1 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 674 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 1 |
"""simple docstring"""
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowerCamelCase = get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
_lowerCamelCase = json.load(f)
@require_torch
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict ):
return FSMTTokenizer.from_pretrained(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Tuple = FSMTForConditionalGeneration.from_pretrained(_lowerCamelCase ).to(_lowerCamelCase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['''en-ru''', 2_6.0],
['''ru-en''', 2_2.0],
['''en-de''', 2_2.0],
['''de-en''', 2_9.0],
] )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :Any , _lowerCamelCase :Dict ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
__SCREAMING_SNAKE_CASE : Dict = f'''facebook/wmt19-{pair}'''
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = self.get_model(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = bleu_data[pair]['''src''']
__SCREAMING_SNAKE_CASE : int = bleu_data[pair]['''tgt''']
__SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(_lowerCamelCase , return_tensors='''pt''' , truncation=_lowerCamelCase , padding='''longest''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
__SCREAMING_SNAKE_CASE : Any = tokenizer.batch_decode(
_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = calculate_bleu(_lowerCamelCase , _lowerCamelCase )
print(_lowerCamelCase )
self.assertGreaterEqual(scores['''bleu'''] , _lowerCamelCase )
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 1 |
"""simple docstring"""
from __future__ import annotations
_lowerCamelCase = 8.988e9 # units = N * m^s * C^-2
def lowerCAmelCase_ ( lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
__SCREAMING_SNAKE_CASE : str = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
__SCREAMING_SNAKE_CASE : Optional[Any] = abs(lowercase_ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
__SCREAMING_SNAKE_CASE : Dict = abs(lowercase_ ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
__SCREAMING_SNAKE_CASE : int = (COULOMBS_CONSTANT * charge_product / abs(lowercase_ )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class snake_case :
def __init__( self :str , _lowerCamelCase :Tuple=2 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Tuple=6_4 , _lowerCamelCase :List[Any]=None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = np.random.default_rng(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = length
__SCREAMING_SNAKE_CASE : Optional[Any] = rng.normal(size=(length,) ).astype(np.floataa )
__SCREAMING_SNAKE_CASE : Optional[Any] = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self :Optional[int] ):
return self.length
def __getitem__( self :Tuple , _lowerCamelCase :Any ):
return {"x": self.x[i], "y": self.y[i]}
class snake_case ( torch.nn.Module ):
def __init__( self :int , _lowerCamelCase :Optional[Any]=0 , _lowerCamelCase :Union[str, Any]=0 , _lowerCamelCase :Dict=False ):
super().__init__()
__SCREAMING_SNAKE_CASE : str = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
__SCREAMING_SNAKE_CASE : List[Any] = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
__SCREAMING_SNAKE_CASE : Optional[int] = True
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int]=None ):
if self.first_batch:
print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
__SCREAMING_SNAKE_CASE : List[Any] = False
return x * self.a[0] + self.b[0]
class snake_case ( torch.nn.Module ):
def __init__( self :int , _lowerCamelCase :int=0 , _lowerCamelCase :Any=0 , _lowerCamelCase :int=False ):
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.nn.Parameter(torch.tensor(_lowerCamelCase ).float() )
__SCREAMING_SNAKE_CASE : str = torch.nn.Parameter(torch.tensor(_lowerCamelCase ).float() )
__SCREAMING_SNAKE_CASE : Tuple = True
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :List[str]=None ):
if self.first_batch:
print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}''' )
__SCREAMING_SNAKE_CASE : List[str] = False
return x * self.a + self.b
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : int = 16 ):
'''simple docstring'''
from datasets import load_dataset
from transformers import AutoTokenizer
__SCREAMING_SNAKE_CASE : List[str] = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__SCREAMING_SNAKE_CASE : List[Any] = {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
__SCREAMING_SNAKE_CASE : int = load_dataset('''csv''' , data_files=lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = datasets['''train'''].unique('''label''' )
__SCREAMING_SNAKE_CASE : int = {v: i for i, v in enumerate(lowercase_ )}
def tokenize_function(lowercase_ : List[str] ):
# max_length=None => use the model max length (it's actually the default)
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer(
examples['''sentence1'''] , examples['''sentence2'''] , truncation=lowercase_ , max_length=lowercase_ , padding='''max_length''' )
if "label" in examples:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__SCREAMING_SNAKE_CASE : Optional[Any] = datasets.map(
lowercase_ , batched=lowercase_ , remove_columns=['''sentence1''', '''sentence2''', '''label'''] , )
def collate_fn(lowercase_ : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowercase_ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(lowercase_ , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
__SCREAMING_SNAKE_CASE : List[Any] = DataLoader(tokenized_datasets['''train'''] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=2 )
__SCREAMING_SNAKE_CASE : Optional[Any] = DataLoader(tokenized_datasets['''validation'''] , shuffle=lowercase_ , collate_fn=lowercase_ , batch_size=1 )
return train_dataloader, eval_dataloader
| 674 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (IPNDMScheduler,)
lowerCamelCase__ = (('''num_inference_steps''', 50),)
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , **_lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''num_train_timesteps''': 1_0_0_0}
config.update(**_lowerCamelCase )
return config
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[Any]=0 , **_lowerCamelCase :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : int = dict(self.forward_default_kwargs )
__SCREAMING_SNAKE_CASE : List[Any] = kwargs.pop('''num_inference_steps''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_sample
__SCREAMING_SNAKE_CASE : Optional[int] = 0.1 * sample
__SCREAMING_SNAKE_CASE : Optional[Any] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__SCREAMING_SNAKE_CASE : List[str] = self.get_scheduler_config(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residuals
__SCREAMING_SNAKE_CASE : Union[str, Any] = dummy_past_residuals[:]
if time_step is None:
__SCREAMING_SNAKE_CASE : Tuple = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = scheduler_class.from_pretrained(_lowerCamelCase )
new_scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residuals
__SCREAMING_SNAKE_CASE : Any = dummy_past_residuals[:]
__SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__SCREAMING_SNAKE_CASE : Optional[Any] = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__SCREAMING_SNAKE_CASE : Any = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__SCREAMING_SNAKE_CASE : Tuple = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Any=0 , **_lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = dict(self.forward_default_kwargs )
__SCREAMING_SNAKE_CASE : Any = kwargs.pop('''num_inference_steps''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.dummy_sample
__SCREAMING_SNAKE_CASE : List[Any] = 0.1 * sample
__SCREAMING_SNAKE_CASE : Optional[Any] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
for scheduler_class in self.scheduler_classes:
__SCREAMING_SNAKE_CASE : Dict = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE : int = scheduler_class(**_lowerCamelCase )
scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residuals (must be after setting timesteps)
__SCREAMING_SNAKE_CASE : Any = dummy_past_residuals[:]
if time_step is None:
__SCREAMING_SNAKE_CASE : str = scheduler.timesteps[len(scheduler.timesteps ) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = scheduler_class.from_pretrained(_lowerCamelCase )
# copy over dummy past residuals
new_scheduler.set_timesteps(_lowerCamelCase )
# copy over dummy past residual (must be after setting timesteps)
__SCREAMING_SNAKE_CASE : Any = dummy_past_residuals[:]
__SCREAMING_SNAKE_CASE : Union[str, Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__SCREAMING_SNAKE_CASE : Union[str, Any] = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
__SCREAMING_SNAKE_CASE : int = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__SCREAMING_SNAKE_CASE : Dict = new_scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self :Dict , **_lowerCamelCase :Optional[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = self.scheduler_classes[0]
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_scheduler_config(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = scheduler_class(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1_0
__SCREAMING_SNAKE_CASE : Any = self.dummy_model()
__SCREAMING_SNAKE_CASE : Optional[int] = self.dummy_sample_deter
scheduler.set_timesteps(_lowerCamelCase )
for i, t in enumerate(scheduler.timesteps ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ).prev_sample
for i, t in enumerate(scheduler.timesteps ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ).prev_sample
return sample
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[int] = dict(self.forward_default_kwargs )
__SCREAMING_SNAKE_CASE : int = kwargs.pop('''num_inference_steps''' , _lowerCamelCase )
for scheduler_class in self.scheduler_classes:
__SCREAMING_SNAKE_CASE : List[Any] = self.get_scheduler_config()
__SCREAMING_SNAKE_CASE : str = scheduler_class(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = self.dummy_sample
__SCREAMING_SNAKE_CASE : Dict = 0.1 * sample
if num_inference_steps is not None and hasattr(_lowerCamelCase , '''set_timesteps''' ):
scheduler.set_timesteps(_lowerCamelCase )
elif num_inference_steps is not None and not hasattr(_lowerCamelCase , '''set_timesteps''' ):
__SCREAMING_SNAKE_CASE : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__SCREAMING_SNAKE_CASE : List[str] = [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
__SCREAMING_SNAKE_CASE : Dict = dummy_past_residuals[:]
__SCREAMING_SNAKE_CASE : Any = scheduler.timesteps[5]
__SCREAMING_SNAKE_CASE : Optional[int] = scheduler.timesteps[6]
__SCREAMING_SNAKE_CASE : List[str] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__SCREAMING_SNAKE_CASE : Dict = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__SCREAMING_SNAKE_CASE : List[Any] = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
__SCREAMING_SNAKE_CASE : Dict = scheduler.step(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=_lowerCamelCase , time_step=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
for t, num_inference_steps in zip([1, 5, 1_0] , [1_0, 5_0, 1_0_0] ):
self.check_over_forward(num_inference_steps=_lowerCamelCase , time_step=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Dict = self.full_loop()
__SCREAMING_SNAKE_CASE : Dict = torch.mean(torch.abs(_lowerCamelCase ) )
assert abs(result_mean.item() - 2_5_4_0_5_2_9 ) < 1_0
| 674 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 1 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 1 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_lowerCamelCase = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def lowerCAmelCase_ ( lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = test_results.split(''' ''' )
__SCREAMING_SNAKE_CASE : Any = 0
__SCREAMING_SNAKE_CASE : Optional[Any] = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
__SCREAMING_SNAKE_CASE : Union[str, Any] = expressions[-2] if '''=''' in expressions[-1] else expressions[-1]
for i, expression in enumerate(lowercase_ ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = {}
__SCREAMING_SNAKE_CASE : Tuple = None
__SCREAMING_SNAKE_CASE : str = False
for line in failures_short_lines.split('''\n''' ):
if re.search(r'''_ \[doctest\]''' , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = True
__SCREAMING_SNAKE_CASE : str = line.split(''' ''' )[2]
elif in_error and not line.split(''' ''' )[0].isdigit():
__SCREAMING_SNAKE_CASE : List[str] = line
__SCREAMING_SNAKE_CASE : List[Any] = False
return failures
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = title
__SCREAMING_SNAKE_CASE : Union[str, Any] = doc_test_results['''time_spent'''].split(''',''' )[0]
__SCREAMING_SNAKE_CASE : Dict = doc_test_results['''success''']
__SCREAMING_SNAKE_CASE : Union[str, Any] = doc_test_results['''failures''']
__SCREAMING_SNAKE_CASE : Optional[int] = self.n_success + self.n_failures
# Failures and success of the modeling tests
__SCREAMING_SNAKE_CASE : List[str] = doc_test_results
@property
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : List[str] = [self._time_spent]
__SCREAMING_SNAKE_CASE : Any = 0
for time in time_spent:
__SCREAMING_SNAKE_CASE : int = time.split(''':''' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(_lowerCamelCase ) == 1:
__SCREAMING_SNAKE_CASE : str = [0, 0, time_parts[0]]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3_6_0_0 + minutes * 6_0 + seconds
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = total_secs // 3_6_0_0, (total_secs % 3_6_0_0) // 6_0, total_secs % 6_0
return f'''{int(_lowerCamelCase )}h{int(_lowerCamelCase )}m{int(_lowerCamelCase )}s'''
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
f''' {self.time}.'''
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 4_0
__SCREAMING_SNAKE_CASE : Union[str, Any] = {k: v['''failed'''] for k, v in doc_test_results.items() if isinstance(_lowerCamelCase , _lowerCamelCase )}
__SCREAMING_SNAKE_CASE : Tuple = ''''''
for category, failures in category_failures.items():
if len(_lowerCamelCase ) == 0:
continue
if report != "":
report += "\n\n"
report += f'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(_lowerCamelCase )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'''The following examples had failures:\n\n\n{report}\n''',
},
}
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( ):
__SCREAMING_SNAKE_CASE : List[Any] = [
{
'''type''': '''section''',
'''text''': {
'''type''': '''plain_text''',
'''text''': '''There was an issue running the tests.''',
},
'''accessory''': {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''Check Action results''', '''emoji''': True},
'''url''': f'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
]
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(_lowerCamelCase )} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text='''There was an issue running the tests.''' , blocks=_lowerCamelCase , )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
print('''Sending the following payload''' )
print(json.dumps({'''blocks''': json.loads(self.payload )} ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = f'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else '''All tests passed.'''
__SCREAMING_SNAKE_CASE : int = client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , blocks=self.payload , text=_lowerCamelCase , )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[str] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Dict = ''''''
for key, value in failures.items():
__SCREAMING_SNAKE_CASE : int = value[:2_0_0] + ''' [Truncated]''' if len(_lowerCamelCase ) > 2_5_0 else value
failures_text += f'''*{key}*\n_{value}_\n\n'''
__SCREAMING_SNAKE_CASE : Optional[Any] = job_name
__SCREAMING_SNAKE_CASE : Optional[int] = {'''type''': '''section''', '''text''': {'''type''': '''mrkdwn''', '''text''': text}}
if job_link is not None:
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''type''': '''button''',
'''text''': {'''type''': '''plain_text''', '''text''': '''GitHub Action job''', '''emoji''': True},
'''url''': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if self.thread_ts is None:
raise ValueError('''Can only post reply if a post has been made.''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.doc_test_results.pop('''job_link''' )
self.doc_test_results.pop('''failures''' )
self.doc_test_results.pop('''success''' )
self.doc_test_results.pop('''time_spent''' )
__SCREAMING_SNAKE_CASE : Any = sorted(self.doc_test_results.items() , key=lambda _lowerCamelCase : t[0] )
for job, job_result in sorted_dict:
if len(job_result['''failures'''] ):
__SCREAMING_SNAKE_CASE : Dict = f'''*Num failures* :{len(job_result['failed'] )} \n'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = job_result['''failures''']
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_reply_blocks(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , text=_lowerCamelCase )
print('''Sending the following reply''' )
print(json.dumps({'''blocks''': blocks} ) )
client.chat_postMessage(
channel=os.environ['''CI_SLACK_CHANNEL_ID_DAILY'''] , text=f'''Results for {job}''' , blocks=_lowerCamelCase , thread_ts=self.thread_ts['''ts'''] , )
time.sleep(1 )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = os.environ['''GITHUB_RUN_ID''']
__SCREAMING_SNAKE_CASE : str = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = requests.get(lowercase_ ).json()
__SCREAMING_SNAKE_CASE : int = {}
try:
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
__SCREAMING_SNAKE_CASE : str = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : List[Any] = requests.get(url + F'''&page={i + 2}''' ).json()
jobs.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']} )
return jobs
except Exception as e:
print('''Unknown error, could not fetch links.''' , lowercase_ )
return {}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {}
if os.path.exists(lowercase_ ):
__SCREAMING_SNAKE_CASE : List[str] = os.listdir(lowercase_ )
for file in files:
try:
with open(os.path.join(lowercase_ , lowercase_ ) , encoding='''utf-8''' ) as f:
__SCREAMING_SNAKE_CASE : Any = f.read()
except UnicodeDecodeError as e:
raise ValueError(F'''Could not open {os.path.join(lowercase_ , lowercase_ )}.''' ) from e
return _artifact
def lowerCAmelCase_ ( ):
'''simple docstring'''
class snake_case :
def __init__( self :Union[str, Any] , _lowerCamelCase :str ):
__SCREAMING_SNAKE_CASE : Optional[int] = name
__SCREAMING_SNAKE_CASE : Dict = []
def __str__( self :Tuple ):
return self.name
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :str ):
self.paths.append({'''name''': self.name, '''path''': path} )
__SCREAMING_SNAKE_CASE : Dict[str, Artifact] = {}
__SCREAMING_SNAKE_CASE : int = filter(os.path.isdir , os.listdir() )
for directory in directories:
__SCREAMING_SNAKE_CASE : List[Any] = directory
if artifact_name not in _available_artifacts:
__SCREAMING_SNAKE_CASE : List[str] = Artifact(lowercase_ )
_available_artifacts[artifact_name].add_path(lowercase_ )
return _available_artifacts
if __name__ == "__main__":
_lowerCamelCase = get_job_links()
_lowerCamelCase = retrieve_available_artifacts()
_lowerCamelCase = collections.OrderedDict(
[
('''*.py''', '''API Examples'''),
('''*.md''', '''MD Examples'''),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
_lowerCamelCase = {
v: {
'''failed''': [],
'''failures''': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
_lowerCamelCase = github_actions_job_links.get('''run_doctests''')
_lowerCamelCase = available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
_lowerCamelCase = retrieve_artifact(artifact_path['''name'''])
if "stats" in artifact:
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase = handle_test_results(artifact['''stats'''])
_lowerCamelCase = failed
_lowerCamelCase = success
_lowerCamelCase = time_spent[1:-1] + ''', '''
_lowerCamelCase = extract_first_line_failure(artifact['''failures_short'''])
for line in artifact["summary_short"].split('''\n'''):
if re.search('''FAILED''', line):
_lowerCamelCase = line.replace('''FAILED ''', '''''')
_lowerCamelCase = line.split()[0].replace('''\n''', '''''')
if "::" in line:
_lowerCamelCase , _lowerCamelCase = line.split('''::''')
else:
_lowerCamelCase , _lowerCamelCase = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
_lowerCamelCase = docs[file_regex]
doc_test_results[category]["failed"].append(test)
_lowerCamelCase = all_failures[test] if test in all_failures else '''N/A'''
_lowerCamelCase = failure
break
_lowerCamelCase = Message('''🤗 Results of the doc tests.''', doc_test_results)
message.post()
message.post_reply()
| 674 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 1 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :List[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Any=None , _lowerCamelCase :Union[str, Any]=None ):
__SCREAMING_SNAKE_CASE : Any = start
__SCREAMING_SNAKE_CASE : List[Any] = end
__SCREAMING_SNAKE_CASE : str = val
__SCREAMING_SNAKE_CASE : str = (start + end) // 2
__SCREAMING_SNAKE_CASE : Any = left
__SCREAMING_SNAKE_CASE : Dict = right
def __repr__( self :List[Any] ):
return f'''SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'''
class snake_case :
def __init__( self :int , _lowerCamelCase :Sequence , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = collection
__SCREAMING_SNAKE_CASE : List[str] = function
if self.collection:
__SCREAMING_SNAKE_CASE : List[str] = self._build_tree(0 , len(_lowerCamelCase ) - 1 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] , _lowerCamelCase :str ):
self._update_tree(self.root , _lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Tuple , _lowerCamelCase :int ):
return self._query_range(self.root , _lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :List[Any] ):
if start == end:
return SegmentTreeNode(_lowerCamelCase , _lowerCamelCase , self.collection[start] )
__SCREAMING_SNAKE_CASE : str = (start + end) // 2
__SCREAMING_SNAKE_CASE : str = self._build_tree(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self._build_tree(mid + 1 , _lowerCamelCase )
return SegmentTreeNode(_lowerCamelCase , _lowerCamelCase , self.fn(left.val , right.val ) , _lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Dict , _lowerCamelCase :Dict , _lowerCamelCase :Optional[Any] ):
if node.start == i and node.end == i:
__SCREAMING_SNAKE_CASE : Any = val
return
if i <= node.mid:
self._update_tree(node.left , _lowerCamelCase , _lowerCamelCase )
else:
self._update_tree(node.right , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = self.fn(node.left.val , node.right.val )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Union[str, Any] ):
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , _lowerCamelCase , _lowerCamelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _lowerCamelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , _lowerCamelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , _lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
if self.root is not None:
__SCREAMING_SNAKE_CASE : Any = Queue()
queue.put(self.root )
while not queue.empty():
__SCREAMING_SNAKE_CASE : Dict = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print('''*''' * 50)
_lowerCamelCase = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 674 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = 42
class snake_case ( nn.Module ):
def __init__( self :List[str] , _lowerCamelCase :List[Any]=3 , _lowerCamelCase :Tuple=3 , _lowerCamelCase :List[Any]=("DownEncoderBlock2D",) , _lowerCamelCase :Any=(6_4,) , _lowerCamelCase :Tuple=2 , _lowerCamelCase :Any=3_2 , _lowerCamelCase :Optional[Any]="silu" , _lowerCamelCase :Any=True , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = layers_per_block
__SCREAMING_SNAKE_CASE : Tuple = torch.nn.Convad(
_lowerCamelCase , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : str = nn.ModuleList([] )
# down
__SCREAMING_SNAKE_CASE : Tuple = block_out_channels[0]
for i, down_block_type in enumerate(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Dict = output_channel
__SCREAMING_SNAKE_CASE : List[Any] = block_out_channels[i]
__SCREAMING_SNAKE_CASE : List[Any] = i == len(_lowerCamelCase ) - 1
__SCREAMING_SNAKE_CASE : int = get_down_block(
_lowerCamelCase , num_layers=self.layers_per_block , in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=_lowerCamelCase , resnet_groups=_lowerCamelCase , attention_head_dim=_lowerCamelCase , temb_channels=_lowerCamelCase , )
self.down_blocks.append(_lowerCamelCase )
# mid
__SCREAMING_SNAKE_CASE : Optional[int] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=_lowerCamelCase , output_scale_factor=1 , resnet_time_scale_shift='''default''' , attention_head_dim=block_out_channels[-1] , resnet_groups=_lowerCamelCase , temb_channels=_lowerCamelCase , )
# out
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=_lowerCamelCase , eps=1e-6 )
__SCREAMING_SNAKE_CASE : Dict = nn.SiLU()
__SCREAMING_SNAKE_CASE : Union[str, Any] = 2 * out_channels if double_z else out_channels
__SCREAMING_SNAKE_CASE : Tuple = nn.Convad(block_out_channels[-1] , _lowerCamelCase , 3 , padding=1 )
__SCREAMING_SNAKE_CASE : Tuple = False
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = x
__SCREAMING_SNAKE_CASE : Tuple = self.conv_in(_lowerCamelCase )
if self.training and self.gradient_checkpointing:
def create_custom_forward(_lowerCamelCase :Any ):
def custom_forward(*_lowerCamelCase :List[Any] ):
return module(*_lowerCamelCase )
return custom_forward
# down
if is_torch_version('''>=''' , '''1.11.0''' ):
for down_block in self.down_blocks:
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(_lowerCamelCase ) , _lowerCamelCase , use_reentrant=_lowerCamelCase )
# middle
__SCREAMING_SNAKE_CASE : List[str] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , _lowerCamelCase , use_reentrant=_lowerCamelCase )
else:
for down_block in self.down_blocks:
__SCREAMING_SNAKE_CASE : Dict = torch.utils.checkpoint.checkpoint(create_custom_forward(_lowerCamelCase ) , _lowerCamelCase )
# middle
__SCREAMING_SNAKE_CASE : int = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , _lowerCamelCase )
else:
# down
for down_block in self.down_blocks:
__SCREAMING_SNAKE_CASE : Union[str, Any] = down_block(_lowerCamelCase )
# middle
__SCREAMING_SNAKE_CASE : Optional[int] = self.mid_block(_lowerCamelCase )
# post-process
__SCREAMING_SNAKE_CASE : Any = self.conv_norm_out(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.conv_act(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.conv_out(_lowerCamelCase )
return sample
class snake_case ( nn.Module ):
def __init__( self :Any , _lowerCamelCase :Optional[Any]=3 , _lowerCamelCase :List[str]=3 , _lowerCamelCase :Tuple=("UpDecoderBlock2D",) , _lowerCamelCase :str=(6_4,) , _lowerCamelCase :List[str]=2 , _lowerCamelCase :str=3_2 , _lowerCamelCase :Optional[Any]="silu" , _lowerCamelCase :str="group" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : List[str] = layers_per_block
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.Convad(
_lowerCamelCase , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
__SCREAMING_SNAKE_CASE : str = None
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.ModuleList([] )
__SCREAMING_SNAKE_CASE : List[str] = in_channels if norm_type == '''spatial''' else None
# mid
__SCREAMING_SNAKE_CASE : int = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=_lowerCamelCase , output_scale_factor=1 , resnet_time_scale_shift='''default''' if norm_type == '''group''' else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=_lowerCamelCase , temb_channels=_lowerCamelCase , )
# up
__SCREAMING_SNAKE_CASE : int = list(reversed(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = reversed_block_out_channels[0]
for i, up_block_type in enumerate(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : List[Any] = output_channel
__SCREAMING_SNAKE_CASE : List[Any] = reversed_block_out_channels[i]
__SCREAMING_SNAKE_CASE : Tuple = i == len(_lowerCamelCase ) - 1
__SCREAMING_SNAKE_CASE : List[str] = get_up_block(
_lowerCamelCase , num_layers=self.layers_per_block + 1 , in_channels=_lowerCamelCase , out_channels=_lowerCamelCase , prev_output_channel=_lowerCamelCase , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=_lowerCamelCase , resnet_groups=_lowerCamelCase , attention_head_dim=_lowerCamelCase , temb_channels=_lowerCamelCase , resnet_time_scale_shift=_lowerCamelCase , )
self.up_blocks.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = output_channel
# out
if norm_type == "spatial":
__SCREAMING_SNAKE_CASE : Union[str, Any] = SpatialNorm(block_out_channels[0] , _lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=_lowerCamelCase , eps=1e-6 )
__SCREAMING_SNAKE_CASE : int = nn.SiLU()
__SCREAMING_SNAKE_CASE : Any = nn.Convad(block_out_channels[0] , _lowerCamelCase , 3 , padding=1 )
__SCREAMING_SNAKE_CASE : List[str] = False
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Tuple , _lowerCamelCase :Any=None ):
__SCREAMING_SNAKE_CASE : List[str] = z
__SCREAMING_SNAKE_CASE : Dict = self.conv_in(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(_lowerCamelCase :str ):
def custom_forward(*_lowerCamelCase :Union[str, Any] ):
return module(*_lowerCamelCase )
return custom_forward
if is_torch_version('''>=''' , '''1.11.0''' ):
# middle
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , _lowerCamelCase , _lowerCamelCase , use_reentrant=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = sample.to(_lowerCamelCase )
# up
for up_block in self.up_blocks:
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(_lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , use_reentrant=_lowerCamelCase )
else:
# middle
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = sample.to(_lowerCamelCase )
# up
for up_block in self.up_blocks:
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.utils.checkpoint.checkpoint(create_custom_forward(_lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase )
else:
# middle
__SCREAMING_SNAKE_CASE : List[str] = self.mid_block(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = sample.to(_lowerCamelCase )
# up
for up_block in self.up_blocks:
__SCREAMING_SNAKE_CASE : Dict = up_block(_lowerCamelCase , _lowerCamelCase )
# post-process
if latent_embeds is None:
__SCREAMING_SNAKE_CASE : int = self.conv_norm_out(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Tuple = self.conv_norm_out(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = self.conv_act(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = self.conv_out(_lowerCamelCase )
return sample
class snake_case ( nn.Module ):
def __init__( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int , _lowerCamelCase :Tuple=None , _lowerCamelCase :List[str]="random" , _lowerCamelCase :Dict=False , _lowerCamelCase :str=True ):
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = n_e
__SCREAMING_SNAKE_CASE : int = vq_embed_dim
__SCREAMING_SNAKE_CASE : Tuple = beta
__SCREAMING_SNAKE_CASE : Any = legacy
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
__SCREAMING_SNAKE_CASE : Union[str, Any] = remap
if self.remap is not None:
self.register_buffer('''used''' , torch.tensor(np.load(self.remap ) ) )
__SCREAMING_SNAKE_CASE : str = self.used.shape[0]
__SCREAMING_SNAKE_CASE : int = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__SCREAMING_SNAKE_CASE : Optional[int] = self.re_embed
__SCREAMING_SNAKE_CASE : Dict = self.re_embed + 1
print(
f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
f'''Using {self.unknown_index} for unknown indices.''' )
else:
__SCREAMING_SNAKE_CASE : List[str] = n_e
__SCREAMING_SNAKE_CASE : Tuple = sane_index_shape
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :str ):
__SCREAMING_SNAKE_CASE : Tuple = inds.shape
assert len(_lowerCamelCase ) > 1
__SCREAMING_SNAKE_CASE : int = inds.reshape(ishape[0] , -1 )
__SCREAMING_SNAKE_CASE : Optional[int] = self.used.to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = (inds[:, :, None] == used[None, None, ...]).long()
__SCREAMING_SNAKE_CASE : Dict = match.argmax(-1 )
__SCREAMING_SNAKE_CASE : Any = match.sum(2 ) < 1
if self.unknown_index == "random":
__SCREAMING_SNAKE_CASE : int = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.unknown_index
return new.reshape(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : List[Any] = inds.shape
assert len(_lowerCamelCase ) > 1
__SCREAMING_SNAKE_CASE : Optional[Any] = inds.reshape(ishape[0] , -1 )
__SCREAMING_SNAKE_CASE : Tuple = self.used.to(_lowerCamelCase )
if self.re_embed > self.used.shape[0]: # extra token
__SCREAMING_SNAKE_CASE : Dict = 0 # simply set to zero
__SCREAMING_SNAKE_CASE : Optional[int] = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , _lowerCamelCase )
return back.reshape(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Union[str, Any] ):
# reshape z -> (batch, height, width, channel) and flatten
__SCREAMING_SNAKE_CASE : Tuple = z.permute(0 , 2 , 3 , 1 ).contiguous()
__SCREAMING_SNAKE_CASE : Optional[int] = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__SCREAMING_SNAKE_CASE : Optional[int] = torch.argmin(torch.cdist(_lowerCamelCase , self.embedding.weight ) , dim=1 )
__SCREAMING_SNAKE_CASE : Tuple = self.embedding(_lowerCamelCase ).view(z.shape )
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : List[Any] = None
# compute loss for embedding
if not self.legacy:
__SCREAMING_SNAKE_CASE : Optional[int] = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__SCREAMING_SNAKE_CASE : Dict = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__SCREAMING_SNAKE_CASE : Optional[Any] = z + (z_q - z).detach()
# reshape back to match original input shape
__SCREAMING_SNAKE_CASE : Tuple = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
__SCREAMING_SNAKE_CASE : List[str] = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
__SCREAMING_SNAKE_CASE : Optional[int] = self.remap_to_used(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
__SCREAMING_SNAKE_CASE : Any = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :List[str] , _lowerCamelCase :List[Any] ):
# shape specifying (batch, height, width, channel)
if self.remap is not None:
__SCREAMING_SNAKE_CASE : Tuple = indices.reshape(shape[0] , -1 ) # add batch axis
__SCREAMING_SNAKE_CASE : Optional[Any] = self.unmap_to_all(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__SCREAMING_SNAKE_CASE : str = self.embedding(_lowerCamelCase )
if shape is not None:
__SCREAMING_SNAKE_CASE : Any = z_q.view(_lowerCamelCase )
# reshape back to match original input shape
__SCREAMING_SNAKE_CASE : Tuple = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class snake_case ( __UpperCAmelCase ):
def __init__( self :Any , _lowerCamelCase :List[Any] , _lowerCamelCase :Dict=False ):
__SCREAMING_SNAKE_CASE : List[str] = parameters
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = torch.chunk(_lowerCamelCase , 2 , dim=1 )
__SCREAMING_SNAKE_CASE : List[str] = torch.clamp(self.logvar , -3_0.0 , 2_0.0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = deterministic
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.exp(0.5 * self.logvar )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.exp(self.logvar )
if self.deterministic:
__SCREAMING_SNAKE_CASE : str = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[torch.Generator] = None ):
# make sure sample is on the same device as the parameters and has same dtype
__SCREAMING_SNAKE_CASE : Dict = randn_tensor(
self.mean.shape , generator=_lowerCamelCase , device=self.parameters.device , dtype=self.parameters.dtype )
__SCREAMING_SNAKE_CASE : Any = self.mean + self.std * sample
return x
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :int=None ):
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :Union[str, Any]=[1, 2, 3] ):
if self.deterministic:
return torch.Tensor([0.0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.mean
| 674 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 1 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
_lowerCamelCase = logging.get_logger(__name__)
class snake_case ( __UpperCAmelCase ):
def __init__( self :str , _lowerCamelCase :Optional[Any]=None , **_lowerCamelCase :Optional[int] ):
warnings.warn(
'''`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '''
'''instead.''' , _lowerCamelCase , )
super().__init__(args=_lowerCamelCase , **_lowerCamelCase )
| 674 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 1 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = 42
lowerCamelCase__ = jnp.floataa
lowerCamelCase__ = True
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
super().setup()
__SCREAMING_SNAKE_CASE : List[Any] = nn.Dense(5 , dtype=self.dtype )
def __call__( self :Tuple , *_lowerCamelCase :Tuple , **_lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Any = super().__call__(*_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = FlaxBigBirdForNaturalQuestionsModule
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Any , lowercase_ : Any , lowercase_ : Dict ):
'''simple docstring'''
def cross_entropy(lowercase_ : List[str] , lowercase_ : Optional[Any] , lowercase_ : Dict=None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = logits.shape[-1]
__SCREAMING_SNAKE_CASE : int = (labels[..., None] == jnp.arange(lowercase_ )[None]).astype('''f4''' )
__SCREAMING_SNAKE_CASE : int = jax.nn.log_softmax(lowercase_ , axis=-1 )
__SCREAMING_SNAKE_CASE : Dict = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
__SCREAMING_SNAKE_CASE : Any = reduction(lowercase_ )
return loss
__SCREAMING_SNAKE_CASE : Optional[Any] = partial(lowercase_ , reduction=jnp.mean )
__SCREAMING_SNAKE_CASE : Optional[Any] = cross_entropy(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = cross_entropy(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = cross_entropy(lowercase_ , lowercase_ )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class snake_case :
lowerCamelCase__ = "google/bigbird-roberta-base"
lowerCamelCase__ = 3000
lowerCamelCase__ = 10500
lowerCamelCase__ = 128
lowerCamelCase__ = 3
lowerCamelCase__ = 1
lowerCamelCase__ = 5
# tx_args
lowerCamelCase__ = 3e-5
lowerCamelCase__ = 0.0
lowerCamelCase__ = 20000
lowerCamelCase__ = 0.0_0_9_5
lowerCamelCase__ = "bigbird-roberta-natural-questions"
lowerCamelCase__ = "training-expt"
lowerCamelCase__ = "data/nq-training.jsonl"
lowerCamelCase__ = "data/nq-validation.jsonl"
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
os.makedirs(self.base_dir , exist_ok=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = os.path.join(self.base_dir , self.save_dir )
__SCREAMING_SNAKE_CASE : Dict = self.batch_size_per_device * jax.device_count()
@dataclass
class snake_case :
lowerCamelCase__ = 42
lowerCamelCase__ = 4096 # no dynamic padding on TPUs
def __call__( self :List[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : Any = self.collate_fn(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = jax.tree_util.tree_map(_lowerCamelCase , _lowerCamelCase )
return batch
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.fetch_inputs(features['''input_ids'''] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''input_ids''': jnp.array(_lowerCamelCase , dtype=jnp.intaa ),
'''attention_mask''': jnp.array(_lowerCamelCase , dtype=jnp.intaa ),
'''start_labels''': jnp.array(features['''start_token'''] , dtype=jnp.intaa ),
'''end_labels''': jnp.array(features['''end_token'''] , dtype=jnp.intaa ),
'''pooled_labels''': jnp.array(features['''category'''] , dtype=jnp.intaa ),
}
return batch
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :list ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self._fetch_inputs(_lowerCamelCase ) for ids in input_ids]
return zip(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :list ):
__SCREAMING_SNAKE_CASE : List[Any] = [1 for _ in range(len(_lowerCamelCase ) )]
while len(_lowerCamelCase ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : Optional[int]=None ):
'''simple docstring'''
if seed is not None:
__SCREAMING_SNAKE_CASE : Optional[int] = dataset.shuffle(seed=lowercase_ )
for i in range(len(lowercase_ ) // batch_size ):
__SCREAMING_SNAKE_CASE : str = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(lowercase_ )
@partial(jax.pmap , axis_name='''batch''' )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Tuple , **lowercase_ : int ):
'''simple docstring'''
def loss_fn(lowercase_ : List[Any] ):
__SCREAMING_SNAKE_CASE : Any = model_inputs.pop('''start_labels''' )
__SCREAMING_SNAKE_CASE : List[str] = model_inputs.pop('''end_labels''' )
__SCREAMING_SNAKE_CASE : List[Any] = model_inputs.pop('''pooled_labels''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = state.apply_fn(**lowercase_ , params=lowercase_ , dropout_rng=lowercase_ , train=lowercase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = outputs
return state.loss_fn(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = jax.random.split(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = jax.value_and_grad(lowercase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = grad_fn(state.params )
__SCREAMING_SNAKE_CASE : Any = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
__SCREAMING_SNAKE_CASE : Optional[int] = jax.lax.pmean(lowercase_ , '''batch''' )
__SCREAMING_SNAKE_CASE : Optional[int] = state.apply_gradients(grads=lowercase_ )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name='''batch''' )
def lowerCAmelCase_ ( lowercase_ : str , **lowercase_ : Dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = model_inputs.pop('''start_labels''' )
__SCREAMING_SNAKE_CASE : Tuple = model_inputs.pop('''end_labels''' )
__SCREAMING_SNAKE_CASE : List[Any] = model_inputs.pop('''pooled_labels''' )
__SCREAMING_SNAKE_CASE : Any = state.apply_fn(**lowercase_ , params=state.params , train=lowercase_ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = outputs
__SCREAMING_SNAKE_CASE : List[Any] = state.loss_fn(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : str = jax.lax.pmean({'''loss''': loss} , axis_name='''batch''' )
return metrics
class snake_case ( train_state.TrainState ):
lowerCamelCase__ = struct.field(pytree_node=__UpperCAmelCase )
@dataclass
class snake_case :
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Dict , _lowerCamelCase :List[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Union[str, Any]=None ):
__SCREAMING_SNAKE_CASE : int = model.params
__SCREAMING_SNAKE_CASE : Union[str, Any] = TrainState.create(
apply_fn=model.__call__ , params=_lowerCamelCase , tx=_lowerCamelCase , loss_fn=_lowerCamelCase , )
if ckpt_dir is not None:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = restore_checkpoint(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = {
'''lr''': args.lr,
'''init_lr''': args.init_lr,
'''warmup_steps''': args.warmup_steps,
'''num_train_steps''': num_train_steps,
'''weight_decay''': args.weight_decay,
}
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = build_tx(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = train_state.TrainState(
step=_lowerCamelCase , apply_fn=model.__call__ , params=_lowerCamelCase , tx=_lowerCamelCase , opt_state=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Any = args
__SCREAMING_SNAKE_CASE : Any = data_collator
__SCREAMING_SNAKE_CASE : int = lr
__SCREAMING_SNAKE_CASE : Tuple = params
__SCREAMING_SNAKE_CASE : int = jax_utils.replicate(_lowerCamelCase )
return state
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.args
__SCREAMING_SNAKE_CASE : Any = len(_lowerCamelCase ) // args.batch_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jax.random.split(_lowerCamelCase , jax.device_count() )
for epoch in range(args.max_epochs ):
__SCREAMING_SNAKE_CASE : Dict = jnp.array(0 , dtype=jnp.floataa )
__SCREAMING_SNAKE_CASE : str = get_batched_dataset(_lowerCamelCase , args.batch_size , seed=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = 0
for batch in tqdm(_lowerCamelCase , total=_lowerCamelCase , desc=f'''Running EPOCH-{epoch}''' ):
__SCREAMING_SNAKE_CASE : List[str] = self.data_collator(_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = self.train_step_fn(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
if i % args.logging_steps == 0:
__SCREAMING_SNAKE_CASE : int = jax_utils.unreplicate(state.step )
__SCREAMING_SNAKE_CASE : Optional[Any] = running_loss.item() / i
__SCREAMING_SNAKE_CASE : List[Any] = self.scheduler_fn(state_step - 1 )
__SCREAMING_SNAKE_CASE : List[str] = self.evaluate(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = {
'''step''': state_step.item(),
'''eval_loss''': eval_loss.item(),
'''tr_loss''': tr_loss,
'''lr''': lr.item(),
}
tqdm.write(str(_lowerCamelCase ) )
self.logger.log(_lowerCamelCase , commit=_lowerCamelCase )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}''' , state=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : Dict = get_batched_dataset(_lowerCamelCase , self.args.batch_size )
__SCREAMING_SNAKE_CASE : List[Any] = len(_lowerCamelCase ) // self.args.batch_size
__SCREAMING_SNAKE_CASE : List[Any] = jnp.array(0 , dtype=jnp.floataa )
__SCREAMING_SNAKE_CASE : Optional[int] = 0
for batch in tqdm(_lowerCamelCase , total=_lowerCamelCase , desc='''Evaluating ... ''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.data_collator(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = self.val_step_fn(_lowerCamelCase , **_lowerCamelCase )
running_loss += jax_utils.unreplicate(metrics['''loss'''] )
i += 1
return running_loss / i
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :str , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = jax_utils.unreplicate(_lowerCamelCase )
print(f'''SAVING CHECKPOINT IN {save_dir}''' , end=''' ... ''' )
self.model_save_fn(_lowerCamelCase , params=state.params )
with open(os.path.join(_lowerCamelCase , '''opt_state.msgpack''' ) , '''wb''' ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(_lowerCamelCase , '''args.joblib''' ) )
joblib.dump(self.data_collator , os.path.join(_lowerCamelCase , '''data_collator.joblib''' ) )
with open(os.path.join(_lowerCamelCase , '''training_state.json''' ) , '''w''' ) as f:
json.dump({'''step''': state.step.item()} , _lowerCamelCase )
print('''DONE''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
'''simple docstring'''
print(F'''RESTORING CHECKPOINT FROM {save_dir}''' , end=''' ... ''' )
with open(os.path.join(lowercase_ , '''flax_model.msgpack''' ) , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = from_bytes(state.params , f.read() )
with open(os.path.join(lowercase_ , '''opt_state.msgpack''' ) , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : Optional[Any] = from_bytes(state.opt_state , f.read() )
__SCREAMING_SNAKE_CASE : Dict = joblib.load(os.path.join(lowercase_ , '''args.joblib''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = joblib.load(os.path.join(lowercase_ , '''data_collator.joblib''' ) )
with open(os.path.join(lowercase_ , '''training_state.json''' ) , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Any = json.load(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = training_state['''step''']
print('''DONE''' )
return params, opt_state, step, args, data_collator
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = num_train_steps - warmup_steps
__SCREAMING_SNAKE_CASE : Optional[int] = optax.linear_schedule(init_value=lowercase_ , end_value=lowercase_ , transition_steps=lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = optax.linear_schedule(init_value=lowercase_ , end_value=1E-7 , transition_steps=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : int , lowercase_ : List[str] ):
'''simple docstring'''
def weight_decay_mask(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : str = traverse_util.flatten_dict(lowercase_ )
__SCREAMING_SNAKE_CASE : Any = {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
return traverse_util.unflatten_dict(lowercase_ )
__SCREAMING_SNAKE_CASE : int = scheduler_fn(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = optax.adamw(learning_rate=lowercase_ , weight_decay=lowercase_ , mask=lowercase_ )
return tx, lr
| 674 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : Any , lowercase_ : Dict , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = StableDiffusionPipeline.from_pretrained(lowercase_ , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
__SCREAMING_SNAKE_CASE : List[str] = load_file(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
__SCREAMING_SNAKE_CASE : Union[str, Any] = key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' )
__SCREAMING_SNAKE_CASE : List[Any] = pipeline.text_encoder
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' )
__SCREAMING_SNAKE_CASE : Optional[int] = pipeline.unet
# find the target layer
__SCREAMING_SNAKE_CASE : Tuple = layer_infos.pop(0 )
while len(lowercase_ ) > -1:
try:
__SCREAMING_SNAKE_CASE : Dict = curr_layer.__getattr__(lowercase_ )
if len(lowercase_ ) > 0:
__SCREAMING_SNAKE_CASE : List[Any] = layer_infos.pop(0 )
elif len(lowercase_ ) == 0:
break
except Exception:
if len(lowercase_ ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
__SCREAMING_SNAKE_CASE : Dict = layer_infos.pop(0 )
__SCREAMING_SNAKE_CASE : List[Any] = []
if "lora_down" in key:
pair_keys.append(key.replace('''lora_down''' , '''lora_up''' ) )
pair_keys.append(lowercase_ )
else:
pair_keys.append(lowercase_ )
pair_keys.append(key.replace('''lora_up''' , '''lora_down''' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
__SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
__SCREAMING_SNAKE_CASE : Optional[Any] = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ ).unsqueeze(2 ).unsqueeze(3 )
else:
__SCREAMING_SNAKE_CASE : Tuple = state_dict[pair_keys[0]].to(torch.floataa )
__SCREAMING_SNAKE_CASE : List[Any] = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase_ , lowercase_ )
# update visited list
for item in pair_keys:
visited.append(lowercase_ )
return pipeline
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
)
parser.add_argument(
'''--lora_prefix_text_encoder''',
default='''lora_te''',
type=str,
help='''The prefix of text encoder weight in safetensors''',
)
parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
parser.add_argument(
'''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
)
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
_lowerCamelCase = parser.parse_args()
_lowerCamelCase = args.base_model_path
_lowerCamelCase = args.checkpoint_path
_lowerCamelCase = args.dump_path
_lowerCamelCase = args.lora_prefix_unet
_lowerCamelCase = args.lora_prefix_text_encoder
_lowerCamelCase = args.alpha
_lowerCamelCase = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_lowerCamelCase = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 674 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
_lowerCamelCase = TypeVar('''T''')
class snake_case ( Generic[T] ):
def __init__( self :List[str] , _lowerCamelCase :T ):
__SCREAMING_SNAKE_CASE : Optional[Any] = data
__SCREAMING_SNAKE_CASE : List[Any] = self
__SCREAMING_SNAKE_CASE : Dict = 0
class snake_case ( Generic[T] ):
def __init__( self :Union[str, Any] ):
# map from node name to the node object
__SCREAMING_SNAKE_CASE : dict[T, DisjointSetTreeNode[T]] = {}
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :T ):
# create a new set with x as its member
__SCREAMING_SNAKE_CASE : List[str] = DisjointSetTreeNode(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :T ):
# find the set x belongs to (with path-compression)
__SCREAMING_SNAKE_CASE : List[Any] = self.map[data]
if elem_ref != elem_ref.parent:
__SCREAMING_SNAKE_CASE : Dict = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :DisjointSetTreeNode[T] , _lowerCamelCase :DisjointSetTreeNode[T] ):
# helper function for union operation
if nodea.rank > nodea.rank:
__SCREAMING_SNAKE_CASE : Optional[Any] = nodea
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :T , _lowerCamelCase :T ):
# merge 2 disjoint sets
self.link(self.find_set(_lowerCamelCase ) , self.find_set(_lowerCamelCase ) )
class snake_case ( Generic[T] ):
def __init__( self :str ):
# connections: map from the node to the neighbouring nodes (with weights)
__SCREAMING_SNAKE_CASE : dict[T, dict[T, int]] = {}
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :T ):
# add a node ONLY if its not present in the graph
if node not in self.connections:
__SCREAMING_SNAKE_CASE : Tuple = {}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :T , _lowerCamelCase :T , _lowerCamelCase :int ):
# add an edge with the given weight
self.add_node(_lowerCamelCase )
self.add_node(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = weight
__SCREAMING_SNAKE_CASE : Any = weight
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : int = []
__SCREAMING_SNAKE_CASE : Tuple = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda _lowerCamelCase : x[2] )
# creating the disjoint set
__SCREAMING_SNAKE_CASE : Optional[int] = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(_lowerCamelCase )
# MST generation
__SCREAMING_SNAKE_CASE : Dict = 0
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
__SCREAMING_SNAKE_CASE : Optional[int] = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = edges[index]
index += 1
__SCREAMING_SNAKE_CASE : Optional[Any] = disjoint_set.find_set(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = disjoint_set.find_set(_lowerCamelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
disjoint_set.union(_lowerCamelCase , _lowerCamelCase )
return graph
| 674 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int = 10**9 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 1
__SCREAMING_SNAKE_CASE : Optional[int] = 2
__SCREAMING_SNAKE_CASE : List[Any] = 0
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
__SCREAMING_SNAKE_CASE : int = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__SCREAMING_SNAKE_CASE : Tuple = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f'{solution() = }')
| 674 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class snake_case :
lowerCamelCase__ = field(
metadata={'''help''': '''The output directory where the model will be written.'''} , )
lowerCamelCase__ = field(
metadata={
'''help''': (
'''The encoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train an encoder model from scratch.'''
)
} , )
lowerCamelCase__ = field(
metadata={
'''help''': (
'''The decoder model checkpoint for weights initialization.'''
'''Don\'t set if you want to train a decoder model from scratch.'''
)
} , )
lowerCamelCase__ = field(
default=__UpperCAmelCase , metadata={'''help''': '''Pretrained encoder config name or path if not the same as encoder_model_name'''} )
lowerCamelCase__ = field(
default=__UpperCAmelCase , metadata={'''help''': '''Pretrained decoder config name or path if not the same as decoder_model_name'''} )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = HfArgumentParser((ModelArguments,) )
((__SCREAMING_SNAKE_CASE) , ) : List[Any] = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
__SCREAMING_SNAKE_CASE : List[Any] = True
__SCREAMING_SNAKE_CASE : Dict = True
__SCREAMING_SNAKE_CASE : Dict = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=lowercase_ , decoder_config=lowercase_ , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
__SCREAMING_SNAKE_CASE : str = decoder_config.decoder_start_token_id
__SCREAMING_SNAKE_CASE : int = decoder_config.pad_token_id
if decoder_start_token_id is None:
__SCREAMING_SNAKE_CASE : List[str] = decoder_config.bos_token_id
if pad_token_id is None:
__SCREAMING_SNAKE_CASE : List[Any] = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
__SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_config.eos_token_id
__SCREAMING_SNAKE_CASE : str = decoder_start_token_id
__SCREAMING_SNAKE_CASE : Optional[int] = pad_token_id
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
__SCREAMING_SNAKE_CASE : Dict = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
__SCREAMING_SNAKE_CASE : Any = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 674 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 1 |
"""simple docstring"""
_lowerCamelCase = {}
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
__SCREAMING_SNAKE_CASE : List[Any] = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
__SCREAMING_SNAKE_CASE : Optional[Any] = _calculate(days - 1 , lowercase_ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
__SCREAMING_SNAKE_CASE : Optional[Any] = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
__SCREAMING_SNAKE_CASE : List[str] = _calculate(days - 1 , lowercase_ , 0 )
__SCREAMING_SNAKE_CASE : Optional[int] = state_late + state_absent + state_ontime
__SCREAMING_SNAKE_CASE : Any = prizestrings
return prizestrings
def lowerCAmelCase_ ( lowercase_ : int = 30 ):
'''simple docstring'''
return _calculate(lowercase_ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 674 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : int = 2 , lowercase_ : int = 1 , lowercase_ : int = 3 , ):
'''simple docstring'''
if num < 2:
raise ValueError('''The input value cannot be less than 2''' )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(lowercase_ : int , lowercase_ : int , lowercase_ : int ) -> int:
return (pow(lowercase_ , 2 ) + step) % modulus
for _ in range(lowercase_ ):
# These track the position within the cycle detection logic.
__SCREAMING_SNAKE_CASE : Any = seed
__SCREAMING_SNAKE_CASE : str = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
__SCREAMING_SNAKE_CASE : List[str] = rand_fn(lowercase_ , lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = rand_fn(lowercase_ , lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Any = rand_fn(lowercase_ , lowercase_ , lowercase_ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
__SCREAMING_SNAKE_CASE : Union[str, Any] = gcd(hare - tortoise , lowercase_ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
__SCREAMING_SNAKE_CASE : Optional[int] = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''num''',
type=int,
help='''The value to find a divisor of''',
)
parser.add_argument(
'''--attempts''',
type=int,
default=3,
help='''The number of attempts before giving up''',
)
_lowerCamelCase = parser.parse_args()
_lowerCamelCase = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(f'{args.num} is probably prime')
else:
_lowerCamelCase = args.num // divisor
print(f'{args.num} = {divisor} * {quotient}')
| 674 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
_lowerCamelCase = '''0.12''' # assumed parallelism: 8
if is_torch_available():
import torch
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int]=None ):
'''simple docstring'''
if rng is None:
__SCREAMING_SNAKE_CASE : Dict = random.Random()
__SCREAMING_SNAKE_CASE : List[str] = 1
for dim in shape:
total_dims *= dim
__SCREAMING_SNAKE_CASE : Tuple = []
for _ in range(lowercase_ ):
values.append(rng.randint(0 , vocab_size - 1 ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.array(lowercase_ , dtype=jnp.intaa ).reshape(lowercase_ )
return output
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Dict=None ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor(lowercase_ , vocab_size=2 , rng=lowercase_ )
# make sure that at least one token is attended to for each batch
__SCREAMING_SNAKE_CASE : Tuple = 1
return attn_mask
@require_flax
class snake_case :
lowerCamelCase__ = None
lowerCamelCase__ = ()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
__SCREAMING_SNAKE_CASE : int = 2
__SCREAMING_SNAKE_CASE : Any = inputs['''input_ids'''].shape[-1] // 2
__SCREAMING_SNAKE_CASE : Optional[Any] = inputs['''input_ids'''][:max_batch_size, :sequence_length]
__SCREAMING_SNAKE_CASE : List[str] = jnp.ones_like(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
__SCREAMING_SNAKE_CASE : Union[str, Any] = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
__SCREAMING_SNAKE_CASE : Optional[int] = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Optional[Any] = max_length
__SCREAMING_SNAKE_CASE : List[Any] = 0
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : Optional[Any] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
__SCREAMING_SNAKE_CASE : Optional[int] = getattr(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = pt_model_class(_lowerCamelCase ).eval()
__SCREAMING_SNAKE_CASE : Any = load_flax_weights_in_pytorch_model(_lowerCamelCase , flax_model.params )
__SCREAMING_SNAKE_CASE : Any = flax_model.generate(_lowerCamelCase ).sequences
__SCREAMING_SNAKE_CASE : str = pt_model.generate(torch.tensor(_lowerCamelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
__SCREAMING_SNAKE_CASE : Optional[Any] = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : List[str] = False
__SCREAMING_SNAKE_CASE : Dict = max_length
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = jit(model.generate )
__SCREAMING_SNAKE_CASE : List[str] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : Optional[Any] = True
__SCREAMING_SNAKE_CASE : List[str] = max_length
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : List[Any] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = jit(model.generate )
__SCREAMING_SNAKE_CASE : Any = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : str = max_length
__SCREAMING_SNAKE_CASE : List[Any] = 2
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : List[str] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = jit(model.generate )
__SCREAMING_SNAKE_CASE : Optional[int] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : List[Any] = False
__SCREAMING_SNAKE_CASE : Optional[Any] = max_length
__SCREAMING_SNAKE_CASE : Dict = 2
__SCREAMING_SNAKE_CASE : int = 2
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : Any = True
__SCREAMING_SNAKE_CASE : int = max_length
__SCREAMING_SNAKE_CASE : Dict = 0.8
__SCREAMING_SNAKE_CASE : int = 1_0
__SCREAMING_SNAKE_CASE : List[str] = 0.3
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 8
__SCREAMING_SNAKE_CASE : Optional[int] = 9
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : List[str] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = jit(model.generate )
__SCREAMING_SNAKE_CASE : str = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : Tuple = max_length
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : str = 8
__SCREAMING_SNAKE_CASE : Optional[int] = 9
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : Optional[int] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = jit(model.generate )
__SCREAMING_SNAKE_CASE : str = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self._get_input_ids_and_config()
__SCREAMING_SNAKE_CASE : List[str] = max_length
__SCREAMING_SNAKE_CASE : List[Any] = 2
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : int = 8
__SCREAMING_SNAKE_CASE : int = 9
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jit(model.generate )
__SCREAMING_SNAKE_CASE : Optional[int] = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self._get_input_ids_and_config()
# pad attention mask on the left
__SCREAMING_SNAKE_CASE : Optional[int] = attention_mask.at[(0, 0)].set(0 )
__SCREAMING_SNAKE_CASE : Tuple = False
__SCREAMING_SNAKE_CASE : Optional[Any] = max_length
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : List[str] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = jit(model.generate )
__SCREAMING_SNAKE_CASE : Optional[Any] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self._get_input_ids_and_config()
# pad attention mask on the left
__SCREAMING_SNAKE_CASE : List[Any] = attention_mask.at[(0, 0)].set(0 )
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : List[str] = max_length
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : List[Any] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = jit(model.generate )
__SCREAMING_SNAKE_CASE : List[str] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self._get_input_ids_and_config()
# pad attention mask on the left
__SCREAMING_SNAKE_CASE : Dict = attention_mask.at[(0, 0)].set(0 )
__SCREAMING_SNAKE_CASE : Tuple = 2
__SCREAMING_SNAKE_CASE : Tuple = max_length
for model_class in self.all_generative_model_classes:
__SCREAMING_SNAKE_CASE : List[Any] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = jit(model.generate )
__SCREAMING_SNAKE_CASE : Optional[Any] = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Any = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert''' )
__SCREAMING_SNAKE_CASE : Dict = FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
__SCREAMING_SNAKE_CASE : Dict = '''Hello world'''
__SCREAMING_SNAKE_CASE : int = tokenizer(_lowerCamelCase , return_tensors='''np''' ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(_lowerCamelCase , '''do_samples''' ):
model.generate(_lowerCamelCase , do_samples=_lowerCamelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(_lowerCamelCase , '''foo''' ):
__SCREAMING_SNAKE_CASE : int = {'''foo''': '''bar'''}
model.generate(_lowerCamelCase , **_lowerCamelCase )
| 674 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :Optional[int] , _lowerCamelCase :Any , _lowerCamelCase :Dict=7 , _lowerCamelCase :Tuple=3 , _lowerCamelCase :List[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Any=True , _lowerCamelCase :str=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :List[Any]=1 / 2_5_5 , _lowerCamelCase :Dict=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :int=[0.5, 0.5, 0.5] , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : str = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : Dict = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : int = num_channels
__SCREAMING_SNAKE_CASE : int = min_resolution
__SCREAMING_SNAKE_CASE : int = max_resolution
__SCREAMING_SNAKE_CASE : Optional[int] = do_resize
__SCREAMING_SNAKE_CASE : List[str] = size
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : List[str] = rescale_factor
__SCREAMING_SNAKE_CASE : Tuple = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : str = image_std
__SCREAMING_SNAKE_CASE : List[str] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :str ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :int , _lowerCamelCase :Union[str, Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : Optional[int] = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : Optional[int] = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : Dict = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Tuple = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : Optional[Any] = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[Any] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[int] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : Any = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = DetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = DetrImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_rescale''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''rescale_factor''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_pad''' ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : Dict = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : Any = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : int = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Tuple = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Any = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : str = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50''' )
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : str = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : str = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : int = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : int = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Tuple = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Tuple = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : Tuple = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : str = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Union[str, Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : str = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : float , lowercase_ : list[float] ):
'''simple docstring'''
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
__SCREAMING_SNAKE_CASE : Any = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(lowercase_ ) )
return round(lowercase_ , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = StableDiffusionInstructPixaPixPipeline
lowerCamelCase__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase__ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE_ ( self :int ):
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Any = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
__SCREAMING_SNAKE_CASE : Dict = PNDMScheduler(skip_prk_steps=_lowerCamelCase )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : str = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
__SCREAMING_SNAKE_CASE : Dict = CLIPTextModel(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__SCREAMING_SNAKE_CASE : List[Any] = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[Any] , _lowerCamelCase :Dict=0 ):
__SCREAMING_SNAKE_CASE : Optional[Any] = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(_lowerCamelCase ) ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__SCREAMING_SNAKE_CASE : int = Image.fromarray(np.uinta(_lowerCamelCase ) ).convert('''RGB''' )
if str(_lowerCamelCase ).startswith('''mps''' ):
__SCREAMING_SNAKE_CASE : int = torch.manual_seed(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Dict = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_components()
__SCREAMING_SNAKE_CASE : Optional[int] = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.get_dummy_inputs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = sd_pipe(**_lowerCamelCase ).images
__SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__SCREAMING_SNAKE_CASE : Tuple = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE : Dict = self.get_dummy_components()
__SCREAMING_SNAKE_CASE : List[str] = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = '''french fries'''
__SCREAMING_SNAKE_CASE : List[str] = sd_pipe(**_lowerCamelCase , negative_prompt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = output.images
__SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
__SCREAMING_SNAKE_CASE : Dict = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Any = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_components()
__SCREAMING_SNAKE_CASE : str = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = self.get_dummy_inputs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = [inputs['''prompt''']] * 2
__SCREAMING_SNAKE_CASE : int = np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
__SCREAMING_SNAKE_CASE : List[str] = torch.from_numpy(_lowerCamelCase ).unsqueeze(0 ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = image / 2 + 0.5
__SCREAMING_SNAKE_CASE : Any = image.permute(0 , 3 , 1 , 2 )
__SCREAMING_SNAKE_CASE : int = image.repeat(2 , 1 , 1 , 1 )
__SCREAMING_SNAKE_CASE : int = sd_pipe(**_lowerCamelCase ).images
__SCREAMING_SNAKE_CASE : Any = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
__SCREAMING_SNAKE_CASE : Optional[Any] = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Tuple = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__SCREAMING_SNAKE_CASE : Any = self.get_dummy_components()
__SCREAMING_SNAKE_CASE : Optional[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = sd_pipe.to(_lowerCamelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = sd_pipe(**_lowerCamelCase ).images
__SCREAMING_SNAKE_CASE : Optional[Any] = image[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE : Optional[Any] = [round(_lowerCamelCase , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(_lowerCamelCase ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
__SCREAMING_SNAKE_CASE : Dict = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.get_dummy_components()
__SCREAMING_SNAKE_CASE : Tuple = StableDiffusionInstructPixaPixPipeline(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = VaeImageProcessor(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = pipe(**self.get_dummy_inputs_by_type(_lowerCamelCase , input_image_type='''pt''' ) )[0]
__SCREAMING_SNAKE_CASE : Optional[int] = components['''vae''']
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_inputs_by_type(_lowerCamelCase , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__SCREAMING_SNAKE_CASE : Union[str, Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
__SCREAMING_SNAKE_CASE : Optional[int] = pipe(**_lowerCamelCase )[0]
__SCREAMING_SNAKE_CASE : List[Any] = np.abs(out - out_latents_inputs ).max()
self.assertLess(_lowerCamelCase , 1e-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Tuple=0 ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.manual_seed(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
__SCREAMING_SNAKE_CASE : Tuple = {
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_lowerCamelCase )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_inputs()
__SCREAMING_SNAKE_CASE : List[str] = pipe(**_lowerCamelCase ).images
__SCREAMING_SNAKE_CASE : Tuple = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE : str = self.get_inputs()
__SCREAMING_SNAKE_CASE : Dict = pipe(**_lowerCamelCase ).images
__SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : Dict = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE : List[str] = self.get_inputs()
__SCREAMING_SNAKE_CASE : Dict = pipe(**_lowerCamelCase ).images
__SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : str = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = 0
def callback_fn(_lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :torch.FloatTensor ) -> None:
__SCREAMING_SNAKE_CASE : Optional[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__SCREAMING_SNAKE_CASE : List[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
__SCREAMING_SNAKE_CASE : str = latents[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE : Optional[int] = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__SCREAMING_SNAKE_CASE : int = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
__SCREAMING_SNAKE_CASE : Optional[Any] = latents[0, -3:, -3:, -1]
__SCREAMING_SNAKE_CASE : Tuple = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__SCREAMING_SNAKE_CASE : int = False
__SCREAMING_SNAKE_CASE : List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_lowerCamelCase , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE : Tuple = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_inputs()
pipe(**_lowerCamelCase , callback=_lowerCamelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def SCREAMING_SNAKE_CASE_ ( self :Any ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__SCREAMING_SNAKE_CASE : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=_lowerCamelCase , torch_dtype=torch.floataa )
__SCREAMING_SNAKE_CASE : List[str] = pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__SCREAMING_SNAKE_CASE : List[str] = self.get_inputs()
__SCREAMING_SNAKE_CASE : List[Any] = pipe(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__SCREAMING_SNAKE_CASE : Optional[int] = inputs['''image'''].resize((5_0_4, 5_0_4) )
__SCREAMING_SNAKE_CASE : Tuple = '''timbrooks/instruct-pix2pix'''
__SCREAMING_SNAKE_CASE : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_lowerCamelCase , safety_checker=_lowerCamelCase , )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
pipe.enable_attention_slicing()
__SCREAMING_SNAKE_CASE : Optional[Any] = pipe(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = output.images[0]
__SCREAMING_SNAKE_CASE : str = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
__SCREAMING_SNAKE_CASE : List[str] = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 674 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : list[list[int]] , lowercase_ : int , lowercase_ : int , lowercase_ : list[int] ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex in path )
def lowerCAmelCase_ ( lowercase_ : list[list[int]] , lowercase_ : list[int] , lowercase_ : int ):
'''simple docstring'''
if curr_ind == len(lowercase_ ):
# return whether path exists between current and starting vertices
return graph[path[curr_ind - 1]][path[0]] == 1
# Recursive Step
for next_ver in range(0 , len(lowercase_ ) ):
if valid_connection(lowercase_ , lowercase_ , lowercase_ , lowercase_ ):
# Insert current vertex into path as next transition
__SCREAMING_SNAKE_CASE : List[str] = next_ver
# Validate created path
if util_hamilton_cycle(lowercase_ , lowercase_ , curr_ind + 1 ):
return True
# Backtrack
__SCREAMING_SNAKE_CASE : List[str] = -1
return False
def lowerCAmelCase_ ( lowercase_ : list[list[int]] , lowercase_ : int = 0 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = [-1] * (len(lowercase_ ) + 1)
# initialize start and end of path with starting index
__SCREAMING_SNAKE_CASE : str = start_index
# evaluate and if we find answer return path either return empty array
return path if util_hamilton_cycle(lowercase_ , lowercase_ , 1 ) else []
| 674 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 1 |
"""simple docstring"""
_lowerCamelCase = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_lowerCamelCase = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowerCamelCase = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 674 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 1 |
"""simple docstring"""
from typing import List
import numpy as np
def lowerCAmelCase_ ( lowercase_ : dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = {key: len(lowercase_ ) for key, value in gen_kwargs.items() if isinstance(lowercase_ , lowercase_ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'''Sharding is ambiguous for this dataset: '''
+ '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
+ '''\n'''.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items() )
+ '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
+ '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
) )
__SCREAMING_SNAKE_CASE : Dict = max(lists_lengths.values() , default=0 )
return max(1 , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = []
for group_idx in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : List[str] = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
__SCREAMING_SNAKE_CASE : Optional[int] = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
__SCREAMING_SNAKE_CASE : Any = range(lowercase_ , start + num_shards_to_add )
shards_indices_per_group.append(lowercase_ )
return shards_indices_per_group
def lowerCAmelCase_ ( lowercase_ : dict , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = _number_of_shards_in_gen_kwargs(lowercase_ )
if num_shards == 1:
return [dict(lowercase_ )]
else:
__SCREAMING_SNAKE_CASE : List[Any] = _distribute_shards(num_shards=lowercase_ , max_num_jobs=lowercase_ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(lowercase_ , lowercase_ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(lowercase_ ) )
]
def lowerCAmelCase_ ( lowercase_ : List[dict] ):
'''simple docstring'''
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , lowercase_ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def lowerCAmelCase_ ( lowercase_ : np.random.Generator , lowercase_ : dict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = {len(lowercase_ ) for value in gen_kwargs.values() if isinstance(lowercase_ , lowercase_ )}
__SCREAMING_SNAKE_CASE : Dict = {}
for size in list_sizes:
__SCREAMING_SNAKE_CASE : int = list(range(lowercase_ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
__SCREAMING_SNAKE_CASE : Union[str, Any] = dict(lowercase_ )
for key, value in shuffled_kwargs.items():
if isinstance(lowercase_ , lowercase_ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = [value[i] for i in indices_per_size[len(lowercase_ )]]
return shuffled_kwargs
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :str , _lowerCamelCase :int=3 , _lowerCamelCase :List[Any]=3_2 , _lowerCamelCase :int=3 , _lowerCamelCase :Dict=1_0 , _lowerCamelCase :int=[8, 1_6, 3_2, 6_4] , _lowerCamelCase :List[Any]=[1, 1, 2, 1] , _lowerCamelCase :Tuple=True , _lowerCamelCase :List[str]=True , _lowerCamelCase :str="relu" , _lowerCamelCase :List[Any]=3 , _lowerCamelCase :Optional[int]=None , _lowerCamelCase :Optional[int]=["stage2", "stage3", "stage4"] , _lowerCamelCase :Optional[Any]=[2, 3, 4] , _lowerCamelCase :Tuple=1 , ):
__SCREAMING_SNAKE_CASE : Dict = parent
__SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
__SCREAMING_SNAKE_CASE : List[Any] = image_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
__SCREAMING_SNAKE_CASE : Any = embeddings_size
__SCREAMING_SNAKE_CASE : Any = hidden_sizes
__SCREAMING_SNAKE_CASE : int = depths
__SCREAMING_SNAKE_CASE : Any = is_training
__SCREAMING_SNAKE_CASE : List[str] = use_labels
__SCREAMING_SNAKE_CASE : Any = hidden_act
__SCREAMING_SNAKE_CASE : str = num_labels
__SCREAMING_SNAKE_CASE : Tuple = scope
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = out_features
__SCREAMING_SNAKE_CASE : Any = out_indices
__SCREAMING_SNAKE_CASE : int = num_groups
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE : List[str] = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : Any = ids_tensor([self.batch_size] , self.num_labels )
__SCREAMING_SNAKE_CASE : Dict = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = BitModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[Any] = model(_lowerCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Any , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = self.num_labels
__SCREAMING_SNAKE_CASE : int = BitForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Dict = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :List[Any] , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :str ):
__SCREAMING_SNAKE_CASE : Tuple = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__SCREAMING_SNAKE_CASE : Tuple = None
__SCREAMING_SNAKE_CASE : Tuple = BitBackbone(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : Tuple = model(_lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = config_and_inputs
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = BitModelTester(self )
__SCREAMING_SNAKE_CASE : Optional[Any] = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
return
@unittest.skip(reason='''Bit does not output attentions''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : str = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : str = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : List[Any] = model_class(config=_lowerCamelCase )
for name, module in model.named_modules():
if isinstance(_lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
def check_hidden_states_output(_lowerCamelCase :Optional[int] , _lowerCamelCase :Tuple , _lowerCamelCase :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(**self._prepare_for_class(_lowerCamelCase , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.num_stages
self.assertEqual(len(_lowerCamelCase ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE : List[Any] = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__SCREAMING_SNAKE_CASE : Optional[int] = layer_type
__SCREAMING_SNAKE_CASE : Any = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__SCREAMING_SNAKE_CASE : Tuple = True
check_hidden_states_output(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = BitModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Any = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : Any = image_processor(images=_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Any = model(**_lowerCamelCase )
# verify the logits
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4 ) )
@require_torch
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (BitBackbone,) if is_torch_available() else ()
lowerCamelCase__ = BitConfig
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = BitModelTester(self )
| 674 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_lowerCamelCase = data_utils.TransfoXLTokenizer
_lowerCamelCase = data_utils.TransfoXLCorpus
_lowerCamelCase = data_utils
_lowerCamelCase = data_utils
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : str , lowercase_ : List[Any] , lowercase_ : Tuple ):
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(lowercase_ , '''rb''' ) as fp:
__SCREAMING_SNAKE_CASE : Tuple = pickle.load(lowercase_ , encoding='''latin1''' )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
__SCREAMING_SNAKE_CASE : Optional[int] = pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
print(F'''Save vocabulary to {pytorch_vocab_dump_path}''' )
__SCREAMING_SNAKE_CASE : Any = corpus.vocab.__dict__
torch.save(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : List[Any] = corpus.__dict__
corpus_dict_no_vocab.pop('''vocab''' , lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = pytorch_dump_folder_path + '''/''' + CORPUS_NAME
print(F'''Save dataset to {pytorch_dataset_dump_path}''' )
torch.save(lowercase_ , lowercase_ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(lowercase_ )
__SCREAMING_SNAKE_CASE : List[Any] = os.path.abspath(lowercase_ )
print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
__SCREAMING_SNAKE_CASE : Dict = TransfoXLConfig()
else:
__SCREAMING_SNAKE_CASE : List[Any] = TransfoXLConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : str = TransfoXLLMHeadModel(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = load_tf_weights_in_transfo_xl(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = os.path.join(lowercase_ , lowercase_ )
print(F'''Save PyTorch model to {os.path.abspath(lowercase_ )}''' )
torch.save(model.state_dict() , lowercase_ )
print(F'''Save configuration file to {os.path.abspath(lowercase_ )}''' )
with open(lowercase_ , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
)
parser.add_argument(
'''--tf_checkpoint_path''',
default='''''',
type=str,
help='''An optional path to a TensorFlow checkpoint path to be converted.''',
)
parser.add_argument(
'''--transfo_xl_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--transfo_xl_dataset_file''',
default='''''',
type=str,
help='''An optional dataset file to be converted in a vocabulary.''',
)
_lowerCamelCase = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 674 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 1 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 1 |
"""simple docstring"""
_lowerCamelCase = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
_lowerCamelCase = {value: key for key, value in MORSE_CODE_DICT.items()}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return "".join(REVERSE_DICT[char] for char in message.split() )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''Morse code here!'''
print(lowercase_ )
__SCREAMING_SNAKE_CASE : List[Any] = encrypt(lowercase_ )
print(lowercase_ )
__SCREAMING_SNAKE_CASE : str = decrypt(lowercase_ )
print(lowercase_ )
if __name__ == "__main__":
main()
| 674 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 1 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class snake_case ( __UpperCAmelCase ):
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
__SCREAMING_SNAKE_CASE : Optional[int] = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
__SCREAMING_SNAKE_CASE : Optional[int] = '''
import socket
def offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
__SCREAMING_SNAKE_CASE : List[str] = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(_lowerCamelCase )
BertModel.from_pretrained(_lowerCamelCase )
BertTokenizer.from_pretrained(_lowerCamelCase )
pipeline(task='''fill-mask''' , model=_lowerCamelCase )
# baseline - just load from_pretrained with normal network
__SCREAMING_SNAKE_CASE : Dict = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
__SCREAMING_SNAKE_CASE : Tuple = self.get_env()
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__SCREAMING_SNAKE_CASE : List[Any] = '''1'''
__SCREAMING_SNAKE_CASE : Tuple = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__SCREAMING_SNAKE_CASE : Dict = '''
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
'''
__SCREAMING_SNAKE_CASE : List[str] = '''
mname = "hf-internal-testing/tiny-random-bert"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
BertTokenizer.from_pretrained(mname)
pipe = pipeline(task="fill-mask", model=mname)
print("success")
'''
__SCREAMING_SNAKE_CASE : List[str] = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")
socket.socket = offline_socket
'''
# Force fetching the files so that we can use the cache
__SCREAMING_SNAKE_CASE : Dict = '''hf-internal-testing/tiny-random-bert'''
BertConfig.from_pretrained(_lowerCamelCase )
BertModel.from_pretrained(_lowerCamelCase )
BertTokenizer.from_pretrained(_lowerCamelCase )
pipeline(task='''fill-mask''' , model=_lowerCamelCase )
# baseline - just load from_pretrained with normal network
__SCREAMING_SNAKE_CASE : Optional[Any] = [sys.executable, '''-c''', '''\n'''.join([load, run, mock] )]
# should succeed
__SCREAMING_SNAKE_CASE : Tuple = self.get_env()
__SCREAMING_SNAKE_CASE : List[Any] = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
# while running an external program
# python one-liner segments
# this must be loaded before socket.socket is monkey-patched
__SCREAMING_SNAKE_CASE : int = '''
from transformers import BertConfig, BertModel, BertTokenizer
'''
__SCREAMING_SNAKE_CASE : List[Any] = '''
mname = "hf-internal-testing/tiny-random-bert-sharded"
BertConfig.from_pretrained(mname)
BertModel.from_pretrained(mname)
print("success")
'''
__SCREAMING_SNAKE_CASE : str = '''
import socket
def offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")
socket.socket = offline_socket
'''
# baseline - just load from_pretrained with normal network
__SCREAMING_SNAKE_CASE : Dict = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_env()
__SCREAMING_SNAKE_CASE : Optional[int] = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# next emulate no network
__SCREAMING_SNAKE_CASE : int = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
# env["TRANSFORMERS_OFFLINE"] = "0"
# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
# self.assertEqual(result.returncode, 1, result.stderr)
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''1'''
__SCREAMING_SNAKE_CASE : Dict = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : List[str] = '''
from transformers import pipeline
'''
__SCREAMING_SNAKE_CASE : List[Any] = '''
mname = "hf-internal-testing/tiny-random-bert"
pipe = pipeline(model=mname)
'''
__SCREAMING_SNAKE_CASE : Tuple = '''
import socket
def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
socket.socket = offline_socket
'''
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_env()
__SCREAMING_SNAKE_CASE : Optional[Any] = '''1'''
__SCREAMING_SNAKE_CASE : Optional[int] = [sys.executable, '''-c''', '''\n'''.join([load, mock, run] )]
__SCREAMING_SNAKE_CASE : Dict = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 1 , result.stderr )
self.assertIn(
'''You cannot infer task automatically within `pipeline` when using offline mode''' , result.stderr.decode().replace('''\n''' , '''''' ) , )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Dict = '''
from transformers import AutoModel
'''
__SCREAMING_SNAKE_CASE : Dict = '''
mname = "hf-internal-testing/test_dynamic_model"
AutoModel.from_pretrained(mname, trust_remote_code=True)
print("success")
'''
# baseline - just load from_pretrained with normal network
__SCREAMING_SNAKE_CASE : Any = [sys.executable, '''-c''', '''\n'''.join([load, run] )]
# should succeed
__SCREAMING_SNAKE_CASE : int = self.get_env()
__SCREAMING_SNAKE_CASE : Any = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
__SCREAMING_SNAKE_CASE : Optional[Any] = '''1'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = subprocess.run(_lowerCamelCase , env=_lowerCamelCase , check=_lowerCamelCase , capture_output=_lowerCamelCase )
self.assertEqual(result.returncode , 0 , result.stderr )
self.assertIn('''success''' , result.stdout.decode() )
| 674 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_lowerCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
_lowerCamelCase = '''
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)["depth"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline("depth-estimation")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to("cuda")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> img = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
>>> prompt = "A robot, 4k photo"
>>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
>>> generator = torch.Generator(device="cuda").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save("robot_cat.png")
```
'''
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : Tuple=8 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
__SCREAMING_SNAKE_CASE : int = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class snake_case ( __UpperCAmelCase ):
def __init__( self :List[Any] , _lowerCamelCase :UNetaDConditionModel , _lowerCamelCase :DDPMScheduler , _lowerCamelCase :VQModel , ):
super().__init__()
self.register_modules(
unet=_lowerCamelCase , scheduler=_lowerCamelCase , movq=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Optional[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :Tuple , _lowerCamelCase :str , _lowerCamelCase :List[str] , _lowerCamelCase :Optional[int] , _lowerCamelCase :str , _lowerCamelCase :str ):
if latents is None:
__SCREAMING_SNAKE_CASE : Any = randn_tensor(_lowerCamelCase , generator=_lowerCamelCase , device=_lowerCamelCase , dtype=_lowerCamelCase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
__SCREAMING_SNAKE_CASE : Any = latents.to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = latents * scheduler.init_noise_sigma
return latents
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int]=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
__SCREAMING_SNAKE_CASE : str = torch.device(f'''cuda:{gpu_id}''' )
__SCREAMING_SNAKE_CASE : Tuple = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :str=0 ):
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
__SCREAMING_SNAKE_CASE : List[str] = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=_lowerCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
__SCREAMING_SNAKE_CASE : Dict = None
for cpu_offloaded_model in [self.unet, self.movq]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = cpu_offload_with_hook(_lowerCamelCase , _lowerCamelCase , prev_module_hook=_lowerCamelCase )
# We'll offload the last model manually.
__SCREAMING_SNAKE_CASE : List[Any] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowerCamelCase , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowerCamelCase )
def __call__( self :List[Any] , _lowerCamelCase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowerCamelCase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowerCamelCase :torch.FloatTensor , _lowerCamelCase :int = 5_1_2 , _lowerCamelCase :int = 5_1_2 , _lowerCamelCase :int = 1_0_0 , _lowerCamelCase :float = 4.0 , _lowerCamelCase :int = 1 , _lowerCamelCase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowerCamelCase :Optional[torch.FloatTensor] = None , _lowerCamelCase :Optional[str] = "pil" , _lowerCamelCase :bool = True , ):
__SCREAMING_SNAKE_CASE : Dict = self._execution_device
__SCREAMING_SNAKE_CASE : List[str] = guidance_scale > 1.0
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE : int = torch.cat(_lowerCamelCase , dim=0 )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Any = torch.cat(_lowerCamelCase , dim=0 )
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.cat(_lowerCamelCase , dim=0 )
__SCREAMING_SNAKE_CASE : Any = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE : Any = image_embeds.repeat_interleave(_lowerCamelCase , dim=0 )
__SCREAMING_SNAKE_CASE : str = negative_image_embeds.repeat_interleave(_lowerCamelCase , dim=0 )
__SCREAMING_SNAKE_CASE : Dict = hint.repeat_interleave(_lowerCamelCase , dim=0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=_lowerCamelCase )
self.scheduler.set_timesteps(_lowerCamelCase , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = self.scheduler.timesteps
__SCREAMING_SNAKE_CASE : str = self.movq.config.latent_channels
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = downscale_height_and_width(_lowerCamelCase , _lowerCamelCase , self.movq_scale_factor )
# create initial latent
__SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
__SCREAMING_SNAKE_CASE : Optional[int] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_embeds''': image_embeds, '''hint''': hint}
__SCREAMING_SNAKE_CASE : Any = self.unet(
sample=_lowerCamelCase , timestep=_lowerCamelCase , encoder_hidden_states=_lowerCamelCase , added_cond_kwargs=_lowerCamelCase , return_dict=_lowerCamelCase , )[0]
if do_classifier_free_guidance:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = noise_pred.split(latents.shape[1] , dim=1 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = noise_pred.chunk(2 )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = variance_pred.chunk(2 )
__SCREAMING_SNAKE_CASE : Optional[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
__SCREAMING_SNAKE_CASE : List[Any] = self.scheduler.step(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , generator=_lowerCamelCase , )[0]
# post-processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.movq.decode(_lowerCamelCase , force_not_quantize=_lowerCamelCase )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = image * 0.5 + 0.5
__SCREAMING_SNAKE_CASE : List[Any] = image.clamp(0 , 1 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
__SCREAMING_SNAKE_CASE : str = self.numpy_to_pil(_lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowerCamelCase )
| 674 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_lowerCamelCase = '''platform'''
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Optional[int] , lowercase_ : Optional[int]=None , lowercase_ : List[str]=None , lowercase_ : Optional[int]=None , lowercase_ : str=None , lowercase_ : Optional[int]=None , lowercase_ : Optional[int]=None , ):
'''simple docstring'''
if attention_mask is None:
__SCREAMING_SNAKE_CASE : int = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
__SCREAMING_SNAKE_CASE : Any = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
__SCREAMING_SNAKE_CASE : str = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__SCREAMING_SNAKE_CASE : int = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__SCREAMING_SNAKE_CASE : Any = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class snake_case :
def __init__( self :Union[str, Any] , _lowerCamelCase :str , _lowerCamelCase :int=1_3 , _lowerCamelCase :Optional[Any]=7 , _lowerCamelCase :List[Any]=True , _lowerCamelCase :List[str]=False , _lowerCamelCase :Dict=9_9 , _lowerCamelCase :Union[str, Any]=1_6 , _lowerCamelCase :Union[str, Any]=2 , _lowerCamelCase :List[Any]=4 , _lowerCamelCase :List[Any]=4 , _lowerCamelCase :Optional[int]="gelu" , _lowerCamelCase :Optional[Any]=0.1 , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :Tuple=3_2 , _lowerCamelCase :Any=2 , _lowerCamelCase :Union[str, Any]=1 , _lowerCamelCase :Optional[int]=0 , _lowerCamelCase :Optional[int]=0.0_2 , ):
__SCREAMING_SNAKE_CASE : Optional[Any] = parent
__SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = seq_length
__SCREAMING_SNAKE_CASE : Optional[int] = is_training
__SCREAMING_SNAKE_CASE : Tuple = use_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[str] = num_hidden_layers
__SCREAMING_SNAKE_CASE : str = num_attention_heads
__SCREAMING_SNAKE_CASE : int = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : Dict = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : int = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[Any] = eos_token_id
__SCREAMING_SNAKE_CASE : Optional[int] = pad_token_id
__SCREAMING_SNAKE_CASE : Tuple = bos_token_id
__SCREAMING_SNAKE_CASE : List[Any] = initializer_range
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
__SCREAMING_SNAKE_CASE : Dict = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
__SCREAMING_SNAKE_CASE : int = shift_tokens_right(_lowerCamelCase , 1 , 2 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_blenderbot_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.prepare_config_and_inputs()
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = 2_0
__SCREAMING_SNAKE_CASE : List[str] = model_class_name(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = model.encode(inputs_dict['''input_ids'''] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__SCREAMING_SNAKE_CASE : Dict = model.init_cache(decoder_input_ids.shape[0] , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='''i4''' )
__SCREAMING_SNAKE_CASE : List[str] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__SCREAMING_SNAKE_CASE : Optional[int] = model.decode(
decoder_input_ids[:, :-1] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : str = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__SCREAMING_SNAKE_CASE : Dict = model.decode(
decoder_input_ids[:, -1:] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Dict = model.decode(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = 2_0
__SCREAMING_SNAKE_CASE : Optional[int] = model_class_name(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model.encode(inputs_dict['''input_ids'''] )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = (
inputs_dict['''decoder_input_ids'''],
inputs_dict['''decoder_attention_mask'''],
)
__SCREAMING_SNAKE_CASE : List[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
__SCREAMING_SNAKE_CASE : List[str] = model.init_cache(decoder_input_ids.shape[0] , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__SCREAMING_SNAKE_CASE : str = model.decode(
decoder_input_ids[:, :-1] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Any = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='''i4''' )
__SCREAMING_SNAKE_CASE : int = model.decode(
decoder_input_ids[:, -1:] , _lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Dict = model.decode(_lowerCamelCase , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class snake_case ( unittest.TestCase ):
lowerCamelCase__ = 99
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = np.array(
[
[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
[6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
[5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
[7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
[8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
[5_5, 1_3, 1_6, 5_8, 5, 2, 1], # note padding
[6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
[5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
[4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
[2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
[2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
[4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
[7_0, 7_0, 5_0, 9, 2_8, 0, 2],
] , dtype=np.intaa , )
__SCREAMING_SNAKE_CASE : Tuple = input_ids.shape[0]
__SCREAMING_SNAKE_CASE : Optional[Any] = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self._get_config_and_data()
__SCREAMING_SNAKE_CASE : List[Any] = FlaxBlenderbotForConditionalGeneration(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = lm_model(input_ids=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
__SCREAMING_SNAKE_CASE : str = FlaxBlenderbotForConditionalGeneration(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : int = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : int = lm_model(input_ids=_lowerCamelCase , decoder_input_ids=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['''logits'''].shape , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : int = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Any = shift_tokens_right(_lowerCamelCase , 1 , 2 )
__SCREAMING_SNAKE_CASE : List[str] = np.equal(_lowerCamelCase , 1 ).astype(np.floataa ).sum()
__SCREAMING_SNAKE_CASE : List[Any] = np.equal(_lowerCamelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(_lowerCamelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class snake_case ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
lowerCamelCase__ = True
lowerCamelCase__ = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
lowerCamelCase__ = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxBlenderbotModelTester(self )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = model_class(_lowerCamelCase )
@jax.jit
def encode_jitted(_lowerCamelCase :List[str] , _lowerCamelCase :Optional[int]=None , **_lowerCamelCase :Union[str, Any] ):
return model.encode(input_ids=_lowerCamelCase , attention_mask=_lowerCamelCase )
with self.subTest('''JIT Enabled''' ):
__SCREAMING_SNAKE_CASE : Dict = encode_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE : Optional[int] = encode_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__SCREAMING_SNAKE_CASE : Optional[Any] = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = model.encode(inputs_dict['''input_ids'''] , inputs_dict['''attention_mask'''] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
'''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
'''encoder_outputs''': encoder_outputs,
}
@jax.jit
def decode_jitted(_lowerCamelCase :int , _lowerCamelCase :Any , _lowerCamelCase :Optional[int] ):
return model.decode(
decoder_input_ids=_lowerCamelCase , decoder_attention_mask=_lowerCamelCase , encoder_outputs=_lowerCamelCase , )
with self.subTest('''JIT Enabled''' ):
__SCREAMING_SNAKE_CASE : int = decode_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
__SCREAMING_SNAKE_CASE : Optional[int] = decode_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_class_name in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Optional[int] = model_class_name.from_pretrained('''facebook/blenderbot-400M-distill''' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.ones((1, 1) ) * model.config.eos_token_id
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skipUnless(jax_device != '''cpu''' , '''3B test too slow on CPU.''' )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : int = {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 1_5, '''max_length''': 2_5}
__SCREAMING_SNAKE_CASE : List[str] = {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}
__SCREAMING_SNAKE_CASE : Any = FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''' , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''Sam''']
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase , return_tensors='''jax''' )
__SCREAMING_SNAKE_CASE : List[Any] = model.generate(**_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''Sam is a great name. It means "sun" in Gaelic.'''
__SCREAMING_SNAKE_CASE : str = tokenizer.batch_decode(_lowerCamelCase , **_lowerCamelCase )
assert generated_txt[0].strip() == tgt_text
| 674 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
raise Exception('''requires gluonnlp == 0.8.3''')
if version.parse(mx.__version__) != version.parse('''1.5.0'''):
raise Exception('''requires mxnet == 1.5.0''')
logging.set_verbosity_info()
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = '''The Nymphenburg Palace is a beautiful palace in Munich!'''
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = {
'''attention_cell''': '''multi_head''',
'''num_layers''': 4,
'''units''': 1024,
'''hidden_size''': 768,
'''max_length''': 512,
'''num_heads''': 8,
'''scaled''': True,
'''dropout''': 0.1,
'''use_residual''': True,
'''embed_size''': 1024,
'''embed_dropout''': 0.1,
'''word_embed''': None,
'''layer_norm_eps''': 1E-5,
'''token_type_vocab_size''': 2,
}
__SCREAMING_SNAKE_CASE : Optional[int] = bort_4_8_768_1024_hparams
# Let's construct the original Bort model here
# Taken from official BERT implementation, see:
# https://github.com/alexa/bort/blob/master/bort/bort.py
__SCREAMING_SNAKE_CASE : Dict = BERTEncoder(
attention_cell=predefined_args['''attention_cell'''] , num_layers=predefined_args['''num_layers'''] , units=predefined_args['''units'''] , hidden_size=predefined_args['''hidden_size'''] , max_length=predefined_args['''max_length'''] , num_heads=predefined_args['''num_heads'''] , scaled=predefined_args['''scaled'''] , dropout=predefined_args['''dropout'''] , output_attention=lowercase_ , output_all_encodings=lowercase_ , use_residual=predefined_args['''use_residual'''] , activation=predefined_args.get('''activation''' , '''gelu''' ) , layer_norm_eps=predefined_args.get('''layer_norm_eps''' , lowercase_ ) , )
# Vocab information needs to be fetched first
# It's the same as RoBERTa, so RobertaTokenizer can be used later
__SCREAMING_SNAKE_CASE : Tuple = '''openwebtext_ccnews_stories_books_cased'''
# Specify download folder to Gluonnlp's vocab
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(get_home_dir() , '''models''' )
__SCREAMING_SNAKE_CASE : Dict = _load_vocab(lowercase_ , lowercase_ , lowercase_ , cls=lowercase_ )
__SCREAMING_SNAKE_CASE : List[Any] = nlp.model.BERTModel(
lowercase_ , len(lowercase_ ) , units=predefined_args['''units'''] , embed_size=predefined_args['''embed_size'''] , embed_dropout=predefined_args['''embed_dropout'''] , word_embed=predefined_args['''word_embed'''] , use_pooler=lowercase_ , use_token_type_embed=lowercase_ , token_type_vocab_size=predefined_args['''token_type_vocab_size'''] , use_classifier=lowercase_ , use_decoder=lowercase_ , )
original_bort.load_parameters(lowercase_ , cast_dtype=lowercase_ , ignore_extra=lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = original_bort._collect_params_with_prefix()
# Build our config 🤗
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''architectures''': ['''BertForMaskedLM'''],
'''attention_probs_dropout_prob''': predefined_args['''dropout'''],
'''hidden_act''': '''gelu''',
'''hidden_dropout_prob''': predefined_args['''dropout'''],
'''hidden_size''': predefined_args['''embed_size'''],
'''initializer_range''': 0.02,
'''intermediate_size''': predefined_args['''hidden_size'''],
'''layer_norm_eps''': predefined_args['''layer_norm_eps'''],
'''max_position_embeddings''': predefined_args['''max_length'''],
'''model_type''': '''bort''',
'''num_attention_heads''': predefined_args['''num_heads'''],
'''num_hidden_layers''': predefined_args['''num_layers'''],
'''pad_token_id''': 1, # 2 = BERT, 1 = RoBERTa
'''type_vocab_size''': 1, # 2 = BERT, 1 = RoBERTa
'''vocab_size''': len(lowercase_ ),
}
__SCREAMING_SNAKE_CASE : Optional[Any] = BertConfig.from_dict(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForMaskedLM(lowercase_ )
hf_bort_model.eval()
# Parameter mapping table (Gluonnlp to Transformers)
# * denotes layer index
#
# | Gluon Parameter | Transformers Parameter
# | -------------------------------------------------------------- | ----------------------
# | `encoder.layer_norm.beta` | `bert.embeddings.LayerNorm.bias`
# | `encoder.layer_norm.gamma` | `bert.embeddings.LayerNorm.weight`
# | `encoder.position_weight` | `bert.embeddings.position_embeddings.weight`
# | `word_embed.0.weight` | `bert.embeddings.word_embeddings.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_key.bias` | `bert.encoder.layer.*.attention.self.key.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_key.weight` | `bert.encoder.layer.*.attention.self.key.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_query.bias` | `bert.encoder.layer.*.attention.self.query.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
# | `encoder.transformer_cells.*.attention_cell.proj_value.bias` | `bert.encoder.layer.*.attention.self.value.bias`
# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
# | `encoder.transformer_cells.*.ffn.ffn_2.bias` | `bert.encoder.layer.*.attention.output.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_2.weight` | `bert.encoder.layer.*.attention.output.dense.weight`
# | `encoder.transformer_cells.*.layer_norm.beta` | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.layer_norm.gamma` | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.ffn.ffn_1.bias` | `bert.encoder.layer.*.intermediate.dense.bias`
# | `encoder.transformer_cells.*.ffn.ffn_1.weight` | `bert.encoder.layer.*.intermediate.dense.weight`
# | `encoder.transformer_cells.*.ffn.layer_norm.beta` | `bert.encoder.layer.*.output.LayerNorm.bias`
# | `encoder.transformer_cells.*.ffn.layer_norm.gamma` | `bert.encoder.layer.*.output.LayerNorm.weight`
# | `encoder.transformer_cells.*.proj.bias` | `bert.encoder.layer.*.output.dense.bias`
# | `encoder.transformer_cells.*.proj.weight` | `bert.encoder.layer.*.output.dense.weight`
# Helper function to convert MXNET Arrays to PyTorch
def to_torch(lowercase_ : Union[str, Any] ) -> nn.Parameter:
return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy() ) )
# Check param shapes and map new HF param back
def check_and_map_params(lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = hf_param.shape
__SCREAMING_SNAKE_CASE : List[Any] = to_torch(params[gluon_param] )
__SCREAMING_SNAKE_CASE : Tuple = gluon_param.shape
assert (
shape_hf == shape_gluon
), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''
return gluon_param
__SCREAMING_SNAKE_CASE : Optional[int] = check_and_map_params(
hf_bort_model.bert.embeddings.word_embeddings.weight , '''word_embed.0.weight''' )
__SCREAMING_SNAKE_CASE : List[Any] = check_and_map_params(
hf_bort_model.bert.embeddings.position_embeddings.weight , '''encoder.position_weight''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.bias , '''encoder.layer_norm.beta''' )
__SCREAMING_SNAKE_CASE : Any = check_and_map_params(
hf_bort_model.bert.embeddings.LayerNorm.weight , '''encoder.layer_norm.gamma''' )
# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.zeros_like(
hf_bort_model.bert.embeddings.token_type_embeddings.weight.data )
for i in range(hf_bort_config.num_hidden_layers ):
__SCREAMING_SNAKE_CASE : BertLayer = hf_bort_model.bert.encoder.layer[i]
# self attention
__SCREAMING_SNAKE_CASE : BertSelfAttention = layer.attention.self
__SCREAMING_SNAKE_CASE : str = check_and_map_params(
self_attn.key.bias.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias''' )
__SCREAMING_SNAKE_CASE : Optional[int] = check_and_map_params(
self_attn.key.weight.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight''' )
__SCREAMING_SNAKE_CASE : int = check_and_map_params(
self_attn.query.bias.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias''' )
__SCREAMING_SNAKE_CASE : Optional[int] = check_and_map_params(
self_attn.query.weight.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight''' )
__SCREAMING_SNAKE_CASE : int = check_and_map_params(
self_attn.value.bias.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias''' )
__SCREAMING_SNAKE_CASE : List[Any] = check_and_map_params(
self_attn.value.weight.data , F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight''' )
# self attention output
__SCREAMING_SNAKE_CASE : BertSelfOutput = layer.attention.output
__SCREAMING_SNAKE_CASE : Any = check_and_map_params(
self_output.dense.bias , F'''encoder.transformer_cells.{i}.proj.bias''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = check_and_map_params(
self_output.dense.weight , F'''encoder.transformer_cells.{i}.proj.weight''' )
__SCREAMING_SNAKE_CASE : Any = check_and_map_params(
self_output.LayerNorm.bias , F'''encoder.transformer_cells.{i}.layer_norm.beta''' )
__SCREAMING_SNAKE_CASE : Tuple = check_and_map_params(
self_output.LayerNorm.weight , F'''encoder.transformer_cells.{i}.layer_norm.gamma''' )
# intermediate
__SCREAMING_SNAKE_CASE : BertIntermediate = layer.intermediate
__SCREAMING_SNAKE_CASE : Optional[Any] = check_and_map_params(
intermediate.dense.bias , F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = check_and_map_params(
intermediate.dense.weight , F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight''' )
# output
__SCREAMING_SNAKE_CASE : BertOutput = layer.output
__SCREAMING_SNAKE_CASE : str = check_and_map_params(
bert_output.dense.bias , F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias''' )
__SCREAMING_SNAKE_CASE : Optional[int] = check_and_map_params(
bert_output.dense.weight , F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight''' )
__SCREAMING_SNAKE_CASE : Dict = check_and_map_params(
bert_output.LayerNorm.bias , F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta''' )
__SCREAMING_SNAKE_CASE : List[str] = check_and_map_params(
bert_output.LayerNorm.weight , F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma''' )
# Save space and energy 🎄
hf_bort_model.half()
# Compare output of both models
__SCREAMING_SNAKE_CASE : Optional[int] = RobertaTokenizer.from_pretrained('''roberta-base''' )
__SCREAMING_SNAKE_CASE : int = tokenizer.encode_plus(lowercase_ )['''input_ids''']
# Get gluon output
__SCREAMING_SNAKE_CASE : Any = mx.nd.array([input_ids] )
__SCREAMING_SNAKE_CASE : Optional[Any] = original_bort(inputs=lowercase_ , token_types=[] )
# Get Transformer output (save and reload model again)
hf_bort_model.save_pretrained(lowercase_ )
__SCREAMING_SNAKE_CASE : str = BertModel.from_pretrained(lowercase_ )
hf_bort_model.eval()
__SCREAMING_SNAKE_CASE : Dict = tokenizer.encode_plus(lowercase_ , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : List[Any] = hf_bort_model(**lowercase_ )[0]
__SCREAMING_SNAKE_CASE : str = output_gluon[0].asnumpy()
__SCREAMING_SNAKE_CASE : int = output_hf[0].detach().numpy()
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.max(np.abs(hf_layer - gluon_layer ) ).item()
__SCREAMING_SNAKE_CASE : Tuple = np.allclose(lowercase_ , lowercase_ , atol=1E-3 )
if success:
print('''✔️ Both model do output the same tensors''' )
else:
print('''❌ Both model do **NOT** output the same tensors''' )
print('''Absolute difference is:''' , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)
| 674 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 1 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 1 |
"""simple docstring"""
class snake_case :
def __init__( self :List[str] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = n
__SCREAMING_SNAKE_CASE : Tuple = [None] * self.n
__SCREAMING_SNAKE_CASE : Optional[int] = 0 # index of the first element
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : Tuple = 0
def __len__( self :List[str] ):
return self.size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
return self.size == 0
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return False if self.is_empty() else self.array[self.front]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :Any ):
if self.size >= self.n:
raise Exception('''QUEUE IS FULL''' )
__SCREAMING_SNAKE_CASE : Tuple = data
__SCREAMING_SNAKE_CASE : Dict = (self.rear + 1) % self.n
self.size += 1
return self
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
if self.size == 0:
raise Exception('''UNDERFLOW''' )
__SCREAMING_SNAKE_CASE : Optional[int] = self.array[self.front]
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : List[Any] = (self.front + 1) % self.n
self.size -= 1
return temp
| 674 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 1 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 1 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Optional[int]="attention" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
__SCREAMING_SNAKE_CASE : Optional[int] = params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
__SCREAMING_SNAKE_CASE : List[Any] = params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
__SCREAMING_SNAKE_CASE : str = params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
return k, o, q, v
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int]=False ):
'''simple docstring'''
if split_mlp_wi:
__SCREAMING_SNAKE_CASE : int = params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
__SCREAMING_SNAKE_CASE : Tuple = params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
__SCREAMING_SNAKE_CASE : str = (wi_a, wi_a)
else:
__SCREAMING_SNAKE_CASE : str = params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']
__SCREAMING_SNAKE_CASE : Optional[Any] = params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
return wi, wo
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : int ):
'''simple docstring'''
return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']
def lowerCAmelCase_ ( lowercase_ : dict , *, lowercase_ : int , lowercase_ : bool ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = traverse_util.flatten_dict(variables['''target'''] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''/'''.join(lowercase_ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
__SCREAMING_SNAKE_CASE : Any = '''encoder/layers_0/mlp/wi_0/kernel''' in old
print('''Split MLP:''' , lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = collections.OrderedDict()
# Shared embeddings.
__SCREAMING_SNAKE_CASE : str = old['''token_embedder/embedding''']
# Encoder.
for i in range(lowercase_ ):
# Block i, layer 0 (Self Attention).
__SCREAMING_SNAKE_CASE : List[str] = tax_layer_norm_lookup(lowercase_ , lowercase_ , '''encoder''' , '''pre_attention_layer_norm''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = tax_attention_lookup(lowercase_ , lowercase_ , '''encoder''' , '''attention''' )
__SCREAMING_SNAKE_CASE : Tuple = layer_norm
__SCREAMING_SNAKE_CASE : Union[str, Any] = k.T
__SCREAMING_SNAKE_CASE : List[Any] = o.T
__SCREAMING_SNAKE_CASE : int = q.T
__SCREAMING_SNAKE_CASE : Union[str, Any] = v.T
# Block i, layer 1 (MLP).
__SCREAMING_SNAKE_CASE : Any = tax_layer_norm_lookup(lowercase_ , lowercase_ , '''encoder''' , '''pre_mlp_layer_norm''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = tax_mlp_lookup(lowercase_ , lowercase_ , '''encoder''' , lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = layer_norm
if split_mlp_wi:
__SCREAMING_SNAKE_CASE : int = wi[0].T
__SCREAMING_SNAKE_CASE : Dict = wi[1].T
else:
__SCREAMING_SNAKE_CASE : List[str] = wi.T
__SCREAMING_SNAKE_CASE : Any = wo.T
__SCREAMING_SNAKE_CASE : str = old[
'''encoder/relpos_bias/rel_embedding'''
].T
__SCREAMING_SNAKE_CASE : Any = old['''encoder/encoder_norm/scale''']
if not is_encoder_only:
# Decoder.
for i in range(lowercase_ ):
# Block i, layer 0 (Self Attention).
__SCREAMING_SNAKE_CASE : Union[str, Any] = tax_layer_norm_lookup(lowercase_ , lowercase_ , '''decoder''' , '''pre_self_attention_layer_norm''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = tax_attention_lookup(lowercase_ , lowercase_ , '''decoder''' , '''self_attention''' )
__SCREAMING_SNAKE_CASE : Dict = layer_norm
__SCREAMING_SNAKE_CASE : Any = k.T
__SCREAMING_SNAKE_CASE : List[str] = o.T
__SCREAMING_SNAKE_CASE : Dict = q.T
__SCREAMING_SNAKE_CASE : List[Any] = v.T
# Block i, layer 1 (Cross Attention).
__SCREAMING_SNAKE_CASE : Union[str, Any] = tax_layer_norm_lookup(lowercase_ , lowercase_ , '''decoder''' , '''pre_cross_attention_layer_norm''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = tax_attention_lookup(lowercase_ , lowercase_ , '''decoder''' , '''encoder_decoder_attention''' )
__SCREAMING_SNAKE_CASE : Optional[int] = layer_norm
__SCREAMING_SNAKE_CASE : int = k.T
__SCREAMING_SNAKE_CASE : Tuple = o.T
__SCREAMING_SNAKE_CASE : Union[str, Any] = q.T
__SCREAMING_SNAKE_CASE : str = v.T
# Block i, layer 2 (MLP).
__SCREAMING_SNAKE_CASE : Tuple = tax_layer_norm_lookup(lowercase_ , lowercase_ , '''decoder''' , '''pre_mlp_layer_norm''' )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = tax_mlp_lookup(lowercase_ , lowercase_ , '''decoder''' , lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = layer_norm
if split_mlp_wi:
__SCREAMING_SNAKE_CASE : Tuple = wi[0].T
__SCREAMING_SNAKE_CASE : Optional[Any] = wi[1].T
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = wi.T
__SCREAMING_SNAKE_CASE : List[str] = wo.T
__SCREAMING_SNAKE_CASE : Dict = old['''decoder/decoder_norm/scale''']
__SCREAMING_SNAKE_CASE : List[str] = old[
'''decoder/relpos_bias/rel_embedding'''
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
__SCREAMING_SNAKE_CASE : Dict = old['''decoder/logits_dense/kernel'''].T
return new
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : bool ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
__SCREAMING_SNAKE_CASE : Tuple = state_dict['''shared.weight''']
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
__SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict['''shared.weight''']
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print('''Using shared word embeddings as lm_head.''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = state_dict['''shared.weight''']
return state_dict
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Any , lowercase_ : Dict , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = checkpoints.load_tax_checkpoint(lowercase_ )
__SCREAMING_SNAKE_CASE : Any = convert_tax_to_pytorch(lowercase_ , num_layers=config.num_layers , is_encoder_only=lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = make_state_dict(lowercase_ , lowercase_ )
model.load_state_dict(lowercase_ , strict=lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Any , lowercase_ : Tuple , lowercase_ : bool = False ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = TaConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
__SCREAMING_SNAKE_CASE : int = TaEncoderModel(lowercase_ )
else:
__SCREAMING_SNAKE_CASE : Dict = TaForConditionalGeneration(lowercase_ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(lowercase_ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase_ )
print('''Done''' )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
_lowerCamelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 674 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 1 |
"""simple docstring"""
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')
@require_sentencepiece
@require_tokenizers
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = PegasusTokenizer
lowerCamelCase__ = PegasusTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE : List[Any] = PegasusTokenizer(_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def SCREAMING_SNAKE_CASE_ ( self :Any , **_lowerCamelCase :Any ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Union[str, Any] ):
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = '''</s>'''
__SCREAMING_SNAKE_CASE : Tuple = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase ) , _lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase ) , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''</s>''' )
self.assertEqual(vocab_keys[-1] , '''v''' )
self.assertEqual(len(_lowerCamelCase ) , 1_1_0_3 )
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_1_0_3 )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : List[str] = self.tokenizer_class.from_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Any = (
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
__SCREAMING_SNAKE_CASE : List[Any] = rust_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
__SCREAMING_SNAKE_CASE : Optional[Any] = py_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[int] = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
__SCREAMING_SNAKE_CASE : int = '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
__SCREAMING_SNAKE_CASE : Dict = [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
__SCREAMING_SNAKE_CASE : int = tokenizer([raw_input_str] , return_tensors=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : str = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6_1_0_3
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 1_0_3
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_0_2_4
__SCREAMING_SNAKE_CASE : Dict = '''To ensure a smooth flow of bank resolutions.'''
__SCREAMING_SNAKE_CASE : int = [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer([raw_input_str] , return_tensors=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = ['''This is going to be way too long.''' * 1_5_0, '''short example''']
__SCREAMING_SNAKE_CASE : Tuple = ['''not super long but more than 5 tokens''', '''tiny''']
__SCREAMING_SNAKE_CASE : Dict = self._large_tokenizer(_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : List[str] = self._large_tokenizer(
text_target=_lowerCamelCase , max_length=5 , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 1_0_2_4)
assert batch.attention_mask.shape == (2, 1_0_2_4)
assert targets["input_ids"].shape == (2, 5)
assert len(_lowerCamelCase ) == 2 # input_ids, attention_mask.
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
# fmt: off
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowerCamelCase , model_name='''google/bigbird-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , )
@require_sentencepiece
@require_tokenizers
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = PegasusTokenizer
lowerCamelCase__ = PegasusTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def SCREAMING_SNAKE_CASE_ ( self :Any ):
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE : Any = PegasusTokenizer(_lowerCamelCase , offset=0 , mask_token_sent=_lowerCamelCase , mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
return ("This is a test", "This is a test")
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Any = self.tokenizer_class.from_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Dict = (
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
__SCREAMING_SNAKE_CASE : Optional[Any] = rust_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
__SCREAMING_SNAKE_CASE : List[Any] = py_tokenizer([raw_input_str] , return_tensors=_lowerCamelCase , add_special_tokens=_lowerCamelCase ).input_ids[0]
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : int = ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
__SCREAMING_SNAKE_CASE : int = ['''not super long but more than 5 tokens''', '''tiny''']
__SCREAMING_SNAKE_CASE : Dict = self._large_tokenizer(_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : int = self._large_tokenizer(
text_target=_lowerCamelCase , max_length=5 , padding=_lowerCamelCase , truncation=_lowerCamelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 4_0_9_6)
assert batch.attention_mask.shape == (2, 4_0_9_6)
assert targets["input_ids"].shape == (2, 5)
assert len(_lowerCamelCase ) == 2 # input_ids, attention_mask.
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
__SCREAMING_SNAKE_CASE : List[str] = self._large_tokenizer(_lowerCamelCase ).input_ids
self.assertListEqual(
_lowerCamelCase , [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] , )
| 674 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 1 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''align_text_model'''
def __init__( self :Optional[Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :Optional[Any]=7_6_8 , _lowerCamelCase :Tuple=1_2 , _lowerCamelCase :Any=1_2 , _lowerCamelCase :Union[str, Any]=3_0_7_2 , _lowerCamelCase :Optional[Any]="gelu" , _lowerCamelCase :Optional[Any]=0.1 , _lowerCamelCase :Tuple=0.1 , _lowerCamelCase :Any=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :List[str]=0.0_2 , _lowerCamelCase :Any=1e-12 , _lowerCamelCase :Optional[int]=0 , _lowerCamelCase :Tuple="absolute" , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :Optional[int] , ):
super().__init__(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = vocab_size
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
__SCREAMING_SNAKE_CASE : int = num_hidden_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : int = intermediate_size
__SCREAMING_SNAKE_CASE : int = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : List[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
__SCREAMING_SNAKE_CASE : Tuple = type_vocab_size
__SCREAMING_SNAKE_CASE : List[Any] = initializer_range
__SCREAMING_SNAKE_CASE : int = layer_norm_eps
__SCREAMING_SNAKE_CASE : str = position_embedding_type
__SCREAMING_SNAKE_CASE : Tuple = use_cache
__SCREAMING_SNAKE_CASE : Tuple = pad_token_id
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Optional[int] , _lowerCamelCase :Union[str, os.PathLike] , **_lowerCamelCase :int ):
cls._set_token_in_kwargs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the text config dict if we are loading from AlignConfig
if config_dict.get('''model_type''' ) == "align":
__SCREAMING_SNAKE_CASE : Optional[int] = config_dict['''text_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''align_vision_model'''
def __init__( self :Tuple , _lowerCamelCase :int = 3 , _lowerCamelCase :int = 6_0_0 , _lowerCamelCase :float = 2.0 , _lowerCamelCase :float = 3.1 , _lowerCamelCase :int = 8 , _lowerCamelCase :List[int] = [3, 3, 5, 3, 5, 5, 3] , _lowerCamelCase :List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , _lowerCamelCase :List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , _lowerCamelCase :List[int] = [] , _lowerCamelCase :List[int] = [1, 2, 2, 2, 1, 2, 1] , _lowerCamelCase :List[int] = [1, 2, 2, 3, 3, 4, 1] , _lowerCamelCase :List[int] = [1, 6, 6, 6, 6, 6, 6] , _lowerCamelCase :float = 0.2_5 , _lowerCamelCase :str = "swish" , _lowerCamelCase :int = 2_5_6_0 , _lowerCamelCase :str = "mean" , _lowerCamelCase :float = 0.0_2 , _lowerCamelCase :float = 0.0_0_1 , _lowerCamelCase :float = 0.9_9 , _lowerCamelCase :float = 0.2 , **_lowerCamelCase :List[Any] , ):
super().__init__(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = num_channels
__SCREAMING_SNAKE_CASE : str = image_size
__SCREAMING_SNAKE_CASE : Any = width_coefficient
__SCREAMING_SNAKE_CASE : List[str] = depth_coefficient
__SCREAMING_SNAKE_CASE : List[Any] = depth_divisor
__SCREAMING_SNAKE_CASE : Optional[Any] = kernel_sizes
__SCREAMING_SNAKE_CASE : Tuple = in_channels
__SCREAMING_SNAKE_CASE : Optional[Any] = out_channels
__SCREAMING_SNAKE_CASE : Optional[Any] = depthwise_padding
__SCREAMING_SNAKE_CASE : Optional[int] = strides
__SCREAMING_SNAKE_CASE : Tuple = num_block_repeats
__SCREAMING_SNAKE_CASE : Union[str, Any] = expand_ratios
__SCREAMING_SNAKE_CASE : str = squeeze_expansion_ratio
__SCREAMING_SNAKE_CASE : Dict = hidden_act
__SCREAMING_SNAKE_CASE : int = hidden_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = pooling_type
__SCREAMING_SNAKE_CASE : Tuple = initializer_range
__SCREAMING_SNAKE_CASE : List[str] = batch_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = batch_norm_momentum
__SCREAMING_SNAKE_CASE : List[Any] = drop_connect_rate
__SCREAMING_SNAKE_CASE : List[str] = sum(_lowerCamelCase ) * 4
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Optional[Any] , _lowerCamelCase :Union[str, os.PathLike] , **_lowerCamelCase :Union[str, Any] ):
cls._set_token_in_kwargs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get('''model_type''' ) == "align":
__SCREAMING_SNAKE_CASE : Optional[int] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''align'''
lowerCamelCase__ = True
def __init__( self :Union[str, Any] , _lowerCamelCase :Optional[int]=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[str]=6_4_0 , _lowerCamelCase :Any=1.0 , _lowerCamelCase :Dict=0.0_2 , **_lowerCamelCase :List[str] , ):
super().__init__(**_lowerCamelCase )
if text_config is None:
__SCREAMING_SNAKE_CASE : List[Any] = {}
logger.info('''text_config is None. Initializing the AlignTextConfig with default values.''' )
if vision_config is None:
__SCREAMING_SNAKE_CASE : Tuple = {}
logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.''' )
__SCREAMING_SNAKE_CASE : Optional[int] = AlignTextConfig(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AlignVisionConfig(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = projection_dim
__SCREAMING_SNAKE_CASE : Any = temperature_init_value
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :str , _lowerCamelCase :AlignTextConfig , _lowerCamelCase :AlignVisionConfig , **_lowerCamelCase :Optional[int] ):
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[Any] = copy.deepcopy(self.__dict__ )
__SCREAMING_SNAKE_CASE : str = self.text_config.to_dict()
__SCREAMING_SNAKE_CASE : Optional[int] = self.vision_config.to_dict()
__SCREAMING_SNAKE_CASE : Dict = self.__class__.model_type
return output
| 674 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''gpt_neox_japanese'''
def __init__( self :Union[str, Any] , _lowerCamelCase :str=3_2_0_0_0 , _lowerCamelCase :Any=2_5_6_0 , _lowerCamelCase :Optional[int]=3_2 , _lowerCamelCase :Union[str, Any]=3_2 , _lowerCamelCase :Any=4 , _lowerCamelCase :int="gelu" , _lowerCamelCase :List[str]=1.0_0 , _lowerCamelCase :List[str]=1_0_0_0_0 , _lowerCamelCase :Any=2_0_4_8 , _lowerCamelCase :Optional[Any]=0.0_2 , _lowerCamelCase :List[Any]=1e-5 , _lowerCamelCase :str=True , _lowerCamelCase :Union[str, Any]=3_1_9_9_6 , _lowerCamelCase :int=3_1_9_9_9 , _lowerCamelCase :Optional[Any]=0.1 , _lowerCamelCase :Tuple=0.0 , **_lowerCamelCase :List[str] , ):
super().__init__(bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = vocab_size
__SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
__SCREAMING_SNAKE_CASE : str = num_attention_heads
__SCREAMING_SNAKE_CASE : Optional[int] = intermediate_multiple_size
__SCREAMING_SNAKE_CASE : Any = hidden_act
__SCREAMING_SNAKE_CASE : str = rotary_pct
__SCREAMING_SNAKE_CASE : Optional[Any] = rotary_emb_base
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Any = layer_norm_eps
__SCREAMING_SNAKE_CASE : Any = use_cache
__SCREAMING_SNAKE_CASE : str = attention_dropout
__SCREAMING_SNAKE_CASE : Dict = hidden_dropout
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 1 |
"""simple docstring"""
from math import pi, sqrt
def lowerCAmelCase_ ( lowercase_ : float ):
'''simple docstring'''
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(lowercase_ ) not in (0, 0.5):
raise NotImplementedError('''num must be an integer or a half-integer''' )
elif num == 0.5:
return sqrt(lowercase_ )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowerCAmelCase_ ( ):
'''simple docstring'''
assert gamma(0.5 ) == sqrt(lowercase_ )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowerCamelCase = 1.0
while num:
_lowerCamelCase = float(input('''Gamma of: '''))
print(f'gamma({num}) = {gamma(num)}')
print('''\nEnter 0 to exit...''')
| 674 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 1 |
"""simple docstring"""
import re
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return [char.split() for char in re.split(r'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = split_input(str_ )
return "".join(
[''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : bool , lowercase_ : str ):
'''simple docstring'''
try:
__SCREAMING_SNAKE_CASE : Any = split_input(lowercase_ )
if upper:
__SCREAMING_SNAKE_CASE : Dict = ''''''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
__SCREAMING_SNAKE_CASE : Dict = ''''''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return to_simple_case(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
try:
__SCREAMING_SNAKE_CASE : List[Any] = to_simple_case(lowercase_ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : bool ):
'''simple docstring'''
return to_complex_case(lowercase_ , lowercase_ , '''_''' )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : bool ):
'''simple docstring'''
return to_complex_case(lowercase_ , lowercase_ , '''-''' )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 674 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 1 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Dict = '''ylacombe/bark-small'''
__SCREAMING_SNAKE_CASE : str = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = '''en_speaker_1'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''This is a test string'''
__SCREAMING_SNAKE_CASE : Dict = '''speaker_embeddings_path.json'''
__SCREAMING_SNAKE_CASE : Optional[Any] = '''speaker_embeddings'''
def SCREAMING_SNAKE_CASE_ ( self :Any , **_lowerCamelCase :Any ):
return AutoTokenizer.from_pretrained(self.checkpoint , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = BarkProcessor(tokenizer=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : str = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : str = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : Tuple = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token='''(BOS)''' , eos_token='''(EOS)''' , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
__SCREAMING_SNAKE_CASE : Optional[Any] = 3_5
__SCREAMING_SNAKE_CASE : List[Any] = 2
__SCREAMING_SNAKE_CASE : Any = 8
__SCREAMING_SNAKE_CASE : Dict = {
'''semantic_prompt''': np.ones(_lowerCamelCase ),
'''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len) ),
'''fine_prompt''': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
__SCREAMING_SNAKE_CASE : int = processor(text=self.input_string , voice_preset=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_lowerCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from npz file
__SCREAMING_SNAKE_CASE : Dict = os.path.join(self.tmpdirname , '''file.npz''' )
np.savez(_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=self.input_string , voice_preset=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''history_prompt''']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_lowerCamelCase , np.array([] ) ).tolist() )
# test loading voice preset from the hub
__SCREAMING_SNAKE_CASE : int = processor(text=self.input_string , voice_preset=self.voice_preset )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Dict = BarkProcessor(tokenizer=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = processor(text=self.input_string )
__SCREAMING_SNAKE_CASE : int = tokenizer(
self.input_string , padding='''max_length''' , max_length=2_5_6 , add_special_tokens=_lowerCamelCase , return_attention_mask=_lowerCamelCase , return_token_type_ids=_lowerCamelCase , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 674 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class snake_case ( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : List[str] = ort.SessionOptions()
__SCREAMING_SNAKE_CASE : List[str] = False
return options
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__SCREAMING_SNAKE_CASE : Optional[int] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__SCREAMING_SNAKE_CASE : List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy''' )
# using the PNDM scheduler by default
__SCREAMING_SNAKE_CASE : Optional[int] = OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , revision='''onnx''' , safety_checker=_lowerCamelCase , feature_extractor=_lowerCamelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = '''A red cat sitting on a park bench'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.random.RandomState(0 )
__SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=_lowerCamelCase , image=_lowerCamelCase , mask_image=_lowerCamelCase , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=1_5 , generator=_lowerCamelCase , output_type='''np''' , )
__SCREAMING_SNAKE_CASE : Optional[Any] = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 1e-2
| 674 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_lowerCamelCase = False
class snake_case ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__SCREAMING_SNAKE_CASE : List[str] = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = pipe.dual_guided(
prompt='''first prompt''' , image=_lowerCamelCase , text_to_image_strength=0.7_5 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = VersatileDiffusionPipeline.from_pretrained(_lowerCamelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = generator.manual_seed(0 )
__SCREAMING_SNAKE_CASE : int = pipe.dual_guided(
prompt='''first prompt''' , image=_lowerCamelCase , text_to_image_strength=0.7_5 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='''numpy''' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion''' , torch_dtype=torch.floataa )
pipe.to(_lowerCamelCase )
pipe.set_progress_bar_config(disable=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = '''cyberpunk 2077'''
__SCREAMING_SNAKE_CASE : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
__SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Dict = pipe.dual_guided(
prompt=_lowerCamelCase , image=_lowerCamelCase , text_to_image_strength=0.7_5 , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='''numpy''' , ).images
__SCREAMING_SNAKE_CASE : List[Any] = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : List[str] = np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__SCREAMING_SNAKE_CASE : List[str] = '''A painting of a squirrel eating a burger '''
__SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
__SCREAMING_SNAKE_CASE : Dict = pipe.text_to_image(
prompt=_lowerCamelCase , generator=_lowerCamelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE : Any = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : List[Any] = np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__SCREAMING_SNAKE_CASE : str = pipe.image_variation(_lowerCamelCase , generator=_lowerCamelCase , output_type='''numpy''' ).images
__SCREAMING_SNAKE_CASE : List[Any] = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__SCREAMING_SNAKE_CASE : Tuple = np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 1 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = 42
lowerCamelCase__ = None
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : str=0.999 , lowercase_ : Optional[Any]="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowercase_ : Union[str, Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowercase_ : int ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
__SCREAMING_SNAKE_CASE : str = []
for i in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : int = i / num_diffusion_timesteps
__SCREAMING_SNAKE_CASE : Dict = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowercase_ ) / alpha_bar_fn(lowercase_ ) , lowercase_ ) )
return torch.tensor(lowercase_ , dtype=torch.floataa )
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :Optional[int] , _lowerCamelCase :int = 1_0_0_0 , _lowerCamelCase :str = "fixed_small_log" , _lowerCamelCase :bool = True , _lowerCamelCase :Optional[float] = 1.0 , _lowerCamelCase :str = "epsilon" , _lowerCamelCase :str = "squaredcos_cap_v2" , ):
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'''' )
__SCREAMING_SNAKE_CASE : List[Any] = betas_for_alpha_bar(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = 1.0 - self.betas
__SCREAMING_SNAKE_CASE : Dict = torch.cumprod(self.alphas , dim=0 )
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
__SCREAMING_SNAKE_CASE : Tuple = 1.0
# setable values
__SCREAMING_SNAKE_CASE : Optional[int] = None
__SCREAMING_SNAKE_CASE : Any = torch.from_numpy(np.arange(0 , _lowerCamelCase )[::-1].copy() )
__SCREAMING_SNAKE_CASE : Optional[Any] = variance_type
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :torch.FloatTensor , _lowerCamelCase :Optional[int] = None ):
return sample
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Union[str, torch.device] = None ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = num_inference_steps
__SCREAMING_SNAKE_CASE : Dict = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (np.arange(0 , _lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
__SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(_lowerCamelCase ).to(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Any=None , _lowerCamelCase :Dict=None , _lowerCamelCase :str=None ):
if prev_timestep is None:
__SCREAMING_SNAKE_CASE : Tuple = t - 1
__SCREAMING_SNAKE_CASE : Tuple = self.alphas_cumprod[t]
__SCREAMING_SNAKE_CASE : List[str] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__SCREAMING_SNAKE_CASE : int = 1 - alpha_prod_t
__SCREAMING_SNAKE_CASE : Dict = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.betas[t]
else:
__SCREAMING_SNAKE_CASE : Tuple = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
__SCREAMING_SNAKE_CASE : Dict = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
__SCREAMING_SNAKE_CASE : Optional[int] = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.log(torch.clamp(_lowerCamelCase , min=1e-20 ) )
__SCREAMING_SNAKE_CASE : Any = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
__SCREAMING_SNAKE_CASE : Union[str, Any] = variance.log()
__SCREAMING_SNAKE_CASE : Union[str, Any] = beta.log()
__SCREAMING_SNAKE_CASE : Dict = (predicted_variance + 1) / 2
__SCREAMING_SNAKE_CASE : Optional[Any] = frac * max_log + (1 - frac) * min_log
return variance
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :torch.FloatTensor , _lowerCamelCase :int , _lowerCamelCase :torch.FloatTensor , _lowerCamelCase :Optional[int] = None , _lowerCamelCase :Tuple=None , _lowerCamelCase :bool = True , ):
__SCREAMING_SNAKE_CASE : str = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = torch.split(_lowerCamelCase , sample.shape[1] , dim=1 )
else:
__SCREAMING_SNAKE_CASE : str = None
# 1. compute alphas, betas
if prev_timestep is None:
__SCREAMING_SNAKE_CASE : int = t - 1
__SCREAMING_SNAKE_CASE : Tuple = self.alphas_cumprod[t]
__SCREAMING_SNAKE_CASE : Optional[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
__SCREAMING_SNAKE_CASE : int = 1 - alpha_prod_t
__SCREAMING_SNAKE_CASE : Tuple = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
__SCREAMING_SNAKE_CASE : str = self.betas[t]
__SCREAMING_SNAKE_CASE : Any = self.alphas[t]
else:
__SCREAMING_SNAKE_CASE : Dict = 1 - alpha_prod_t / alpha_prod_t_prev
__SCREAMING_SNAKE_CASE : Optional[Any] = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
__SCREAMING_SNAKE_CASE : Union[str, Any] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
__SCREAMING_SNAKE_CASE : List[Any] = model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
''' for the UnCLIPScheduler.''' )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
__SCREAMING_SNAKE_CASE : List[str] = torch.clamp(
_lowerCamelCase , -self.config.clip_sample_range , self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__SCREAMING_SNAKE_CASE : Any = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
__SCREAMING_SNAKE_CASE : Optional[Any] = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
__SCREAMING_SNAKE_CASE : List[Any] = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
if t > 0:
__SCREAMING_SNAKE_CASE : int = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=_lowerCamelCase , device=model_output.device )
__SCREAMING_SNAKE_CASE : Tuple = self._get_variance(
_lowerCamelCase , predicted_variance=_lowerCamelCase , prev_timestep=_lowerCamelCase , )
if self.variance_type == "fixed_small_log":
__SCREAMING_SNAKE_CASE : List[Any] = variance
elif self.variance_type == "learned_range":
__SCREAMING_SNAKE_CASE : Any = (0.5 * variance).exp()
else:
raise ValueError(
f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
''' for the UnCLIPScheduler.''' )
__SCREAMING_SNAKE_CASE : int = variance * variance_noise
__SCREAMING_SNAKE_CASE : str = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=_lowerCamelCase , pred_original_sample=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :torch.FloatTensor , _lowerCamelCase :torch.FloatTensor , _lowerCamelCase :torch.IntTensor , ):
# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
__SCREAMING_SNAKE_CASE : int = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
__SCREAMING_SNAKE_CASE : Optional[int] = timesteps.to(original_samples.device )
__SCREAMING_SNAKE_CASE : List[str] = alphas_cumprod[timesteps] ** 0.5
__SCREAMING_SNAKE_CASE : List[str] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
__SCREAMING_SNAKE_CASE : List[Any] = sqrt_alpha_prod.unsqueeze(-1 )
__SCREAMING_SNAKE_CASE : Dict = (1 - alphas_cumprod[timesteps]) ** 0.5
__SCREAMING_SNAKE_CASE : Union[str, Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
__SCREAMING_SNAKE_CASE : Optional[Any] = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
__SCREAMING_SNAKE_CASE : Tuple = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 674 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
lowerCamelCase__ = '''convnextv2'''
def __init__( self :Optional[int] , _lowerCamelCase :str=3 , _lowerCamelCase :List[Any]=4 , _lowerCamelCase :Union[str, Any]=4 , _lowerCamelCase :int=None , _lowerCamelCase :str=None , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=0.0 , _lowerCamelCase :Optional[Any]=2_2_4 , _lowerCamelCase :Dict=None , _lowerCamelCase :List[Any]=None , **_lowerCamelCase :Optional[int] , ):
super().__init__(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
__SCREAMING_SNAKE_CASE : Optional[Any] = patch_size
__SCREAMING_SNAKE_CASE : Dict = num_stages
__SCREAMING_SNAKE_CASE : List[str] = [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
__SCREAMING_SNAKE_CASE : Dict = [3, 3, 9, 3] if depths is None else depths
__SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Dict = layer_norm_eps
__SCREAMING_SNAKE_CASE : int = drop_path_rate
__SCREAMING_SNAKE_CASE : int = image_size
__SCREAMING_SNAKE_CASE : List[str] = ['''stem'''] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = get_aligned_output_features_output_indices(
out_features=_lowerCamelCase , out_indices=_lowerCamelCase , stage_names=self.stage_names )
| 674 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 1 |
"""simple docstring"""
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class snake_case :
lowerCamelCase__ = None
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = None
lowerCamelCase__ = None
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = None
lowerCamelCase__ = 1
lowerCamelCase__ = None
lowerCamelCase__ = False
lowerCamelCase__ = None
lowerCamelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
return self.__class__(**{k: copy.deepcopy(_lowerCamelCase ) for k, v in self.__dict__.items()} )
| 674 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
_lowerCamelCase = 25_00_04
_lowerCamelCase = 25_00_20
@require_sentencepiece
@require_tokenizers
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = MBartTokenizer
lowerCamelCase__ = MBartTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def SCREAMING_SNAKE_CASE_ ( self :str ):
super().setUp()
# We have a SentencePiece fixture for testing
__SCREAMING_SNAKE_CASE : Optional[int] = MBartTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MBartTokenizer(_lowerCamelCase , keep_accents=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(_lowerCamelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowerCamelCase ) , [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]] , )
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__SCREAMING_SNAKE_CASE : Tuple = tokenizer.convert_tokens_to_ids(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
value + tokenizer.fairseq_offset
for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__SCREAMING_SNAKE_CASE : str = tokenizer.convert_ids_to_tokens(_lowerCamelCase )
self.assertListEqual(
_lowerCamelCase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__SCREAMING_SNAKE_CASE : List[Any] = (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.rust_tokenizer_class.from_pretrained(_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer_class.from_pretrained(_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer_r.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = tokenizer_p.save_pretrained(_lowerCamelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__SCREAMING_SNAKE_CASE : str = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(_lowerCamelCase , _lowerCamelCase )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = tokenizer_p.from_pretrained(_lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowerCamelCase , _lowerCamelCase ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(_lowerCamelCase )
# Save tokenizer rust, legacy_format=True
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : int = tokenizer_r.save_pretrained(_lowerCamelCase , legacy_format=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer_p.save_pretrained(_lowerCamelCase )
# Checks it save with the same files
self.assertSequenceEqual(_lowerCamelCase , _lowerCamelCase )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE : Tuple = tokenizer_r.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = tokenizer_p.from_pretrained(_lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowerCamelCase , _lowerCamelCase ) )
shutil.rmtree(_lowerCamelCase )
# Save tokenizer rust, legacy_format=False
__SCREAMING_SNAKE_CASE : List[Any] = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : Any = tokenizer_r.save_pretrained(_lowerCamelCase , legacy_format=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer_p.save_pretrained(_lowerCamelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__SCREAMING_SNAKE_CASE : Dict = tokenizer_r.from_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer_p.from_pretrained(_lowerCamelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_lowerCamelCase , _lowerCamelCase ) )
shutil.rmtree(_lowerCamelCase )
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case ( unittest.TestCase ):
lowerCamelCase__ = '''facebook/mbart-large-en-ro'''
lowerCamelCase__ = [
''' UN Chief Says There Is No Military Solution in Syria''',
''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
]
lowerCamelCase__ = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
'''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
]
lowerCamelCase__ = [8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :int ):
__SCREAMING_SNAKE_CASE : MBartTokenizer = MBartTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='''en_XX''' , tgt_lang='''ro_RO''' )
__SCREAMING_SNAKE_CASE : Dict = 1
return cls
def SCREAMING_SNAKE_CASE_ ( self :str ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] , 2_5_0_0_0_1 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] , 2_5_0_0_0_4 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] , 2_5_0_0_2_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Any = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.assertIn(_lowerCamelCase , self.tokenizer.all_special_ids )
__SCREAMING_SNAKE_CASE : str = [RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
__SCREAMING_SNAKE_CASE : Tuple = self.tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
self.assertNotIn(self.tokenizer.eos_token , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Dict = ['''this is gunna be a long sentence ''' * 2_0]
assert isinstance(src_text[0] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = 1_0
__SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer(_lowerCamelCase , max_length=_lowerCamelCase , truncation=_lowerCamelCase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , _lowerCamelCase )
self.assertEqual(len(_lowerCamelCase ) , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] ) , [2_5_0_0_2_6, 2_5_0_0_0_1] )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : List[Any] = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = MBartTokenizer.from_pretrained(_lowerCamelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , _lowerCamelCase )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Any = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : int = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
assert batch.decoder_input_ids[1][-1] == 2
assert batch.labels[1][-2:].tolist() == [2, RO_CODE]
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Any = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=_lowerCamelCase , truncation=_lowerCamelCase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual((2, 1_4) , batch.input_ids.shape )
self.assertEqual((2, 1_4) , batch.attention_mask.shape )
__SCREAMING_SNAKE_CASE : Tuple = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , _lowerCamelCase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, EN_CODE] )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.tokenizer(self.src_text , padding=_lowerCamelCase , truncation=_lowerCamelCase , max_length=3 , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : int = self.tokenizer(
text_target=self.tgt_text , padding=_lowerCamelCase , truncation=_lowerCamelCase , max_length=1_0 , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Optional[int] = targets['''input_ids''']
__SCREAMING_SNAKE_CASE : List[str] = shift_tokens_right(_lowerCamelCase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 1_0 )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''ar_AR''' )
self.assertEqual(
nested_simplify(_lowerCamelCase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
'''attention_mask''': [[1, 1, 1, 1]],
# ar_AR
'''forced_bos_token_id''': 2_5_0_0_0_1,
} , )
| 674 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 1 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase = 0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class snake_case :
def __init__( self :Dict ):
__SCREAMING_SNAKE_CASE : Any = WATERMARK_BITS
__SCREAMING_SNAKE_CASE : List[Any] = WatermarkEncoder()
self.encoder.set_watermark('''bits''' , self.watermark )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :torch.FloatTensor ):
# can't encode images that are smaller than 256
if images.shape[-1] < 2_5_6:
return images
__SCREAMING_SNAKE_CASE : Dict = (2_5_5 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__SCREAMING_SNAKE_CASE : Optional[Any] = [self.encoder.encode(_lowerCamelCase , '''dwtDct''' ) for image in images]
__SCREAMING_SNAKE_CASE : Dict = torch.from_numpy(np.array(_lowerCamelCase ) ).permute(0 , 3 , 1 , 2 )
__SCREAMING_SNAKE_CASE : int = torch.clamp(2 * (images / 2_5_5 - 0.5) , min=-1.0 , max=1.0 )
return images
| 674 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 1 |
"""simple docstring"""
_lowerCamelCase = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
__SCREAMING_SNAKE_CASE : Stack[int] = Stack()
__SCREAMING_SNAKE_CASE : Stack[str] = Stack()
for i in equation:
if i.isdigit():
# RULE 1
operand_stack.push(int(lowercase_ ) )
elif i in operators:
# RULE 2
operator_stack.push(lowercase_ )
elif i == ")":
# RULE 4
__SCREAMING_SNAKE_CASE : Any = operator_stack.peek()
operator_stack.pop()
__SCREAMING_SNAKE_CASE : List[Any] = operand_stack.peek()
operand_stack.pop()
__SCREAMING_SNAKE_CASE : Union[str, Any] = operand_stack.peek()
operand_stack.pop()
__SCREAMING_SNAKE_CASE : List[Any] = operators[opr](lowercase_ , lowercase_ )
operand_stack.push(lowercase_ )
# RULE 5
return operand_stack.peek()
if __name__ == "__main__":
_lowerCamelCase = '''(5 + ((4 * 2) * (2 + 3)))'''
# answer = 45
print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')
| 674 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 1 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_lowerCamelCase = [
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
{'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
{'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
{'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
{'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
{'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
{'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]
def lowerCAmelCase_ ( lowercase_ : int=True ):
'''simple docstring'''
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase ) )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = None
lowerCamelCase__ = None
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :int , _lowerCamelCase :Tuple ):
with TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE : Tuple = dataset_module_factory(_lowerCamelCase , cache_dir=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = import_main_class(dataset_module.module_path , dataset=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : DatasetBuilder = builder_cls(
cache_dir=_lowerCamelCase , config_name=_lowerCamelCase , hash=dataset_module.hash , )
__SCREAMING_SNAKE_CASE : List[str] = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=_lowerCamelCase ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
__SCREAMING_SNAKE_CASE : List[str] = cached_path(_lowerCamelCase , cache_dir=_lowerCamelCase )
self.assertTrue(os.path.exists(_lowerCamelCase ) )
@pytest.mark.integration
def lowerCAmelCase_ ( lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
__SCREAMING_SNAKE_CASE : Dict = dataset_module_factory('''wikipedia''' , cache_dir=lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = import_main_class(dataset_module.module_path )
__SCREAMING_SNAKE_CASE : DatasetBuilder = builder_cls(
cache_dir=lowercase_ , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
__SCREAMING_SNAKE_CASE : Optional[Any] = None
builder_instance.download_and_prepare()
__SCREAMING_SNAKE_CASE : Union[str, Any] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def lowerCAmelCase_ ( lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_module_factory('''wikipedia''' , cache_dir=lowercase_ )
__SCREAMING_SNAKE_CASE : List[Any] = import_main_class(dataset_module.module_path , dataset=lowercase_ )
__SCREAMING_SNAKE_CASE : DatasetBuilder = builder_cls(
cache_dir=lowercase_ , config_name='''20220301.frr''' , hash=dataset_module.hash , )
__SCREAMING_SNAKE_CASE : List[Any] = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(lowercase_ , lowercase_ )
assert "train" in ds
assert isinstance(ds['''train'''] , lowercase_ )
assert next(iter(ds['''train'''] ) )
| 674 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : list[list[float]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : list[list[float]] = []
for data in source_data:
for i, el in enumerate(lowercase_ ):
if len(lowercase_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowercase_ ) )
return data_lists
def lowerCAmelCase_ ( lowercase_ : list[list[float]] , lowercase_ : list[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : list[list[float]] = []
for dlist, weight in zip(lowercase_ , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = min(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = max(lowercase_ )
__SCREAMING_SNAKE_CASE : list[float] = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
__SCREAMING_SNAKE_CASE : Any = F'''Invalid weight of {weight:f} provided'''
raise ValueError(lowercase_ )
score_lists.append(lowercase_ )
return score_lists
def lowerCAmelCase_ ( lowercase_ : list[list[float]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : list[float] = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowercase_ ):
__SCREAMING_SNAKE_CASE : Dict = final_scores[j] + ele
return final_scores
def lowerCAmelCase_ ( lowercase_ : list[list[float]] , lowercase_ : list[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = get_data(lowercase_ )
__SCREAMING_SNAKE_CASE : str = calculate_each_score(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : Any = generate_final_scores(lowercase_ )
# append scores to source data
for i, ele in enumerate(lowercase_ ):
source_data[i].append(lowercase_ )
return source_data
| 674 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 1 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
_lowerCamelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowercase_ : bool , lowercase_ : bool ):
'''simple docstring'''
def run_func(lowercase_ : List[str] ):
@wraps(lowercase_ )
def run_in_eager_mode(*lowercase_ : Optional[Any] , **lowercase_ : List[Any] ):
return func(*lowercase_ , **lowercase_ )
@wraps(lowercase_ )
@tf.function(experimental_compile=lowercase_ )
def run_in_graph_mode(*lowercase_ : List[str] , **lowercase_ : Optional[Any] ):
return func(*lowercase_ , **lowercase_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
'''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.''' )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = random.Random()
__SCREAMING_SNAKE_CASE : Any = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowercase_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = 42
lowerCamelCase__ = 42
lowerCamelCase__ = "TensorFlow"
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return tf.__version__
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :int ):
# initialize GPU on separate process
__SCREAMING_SNAKE_CASE : Tuple = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
__SCREAMING_SNAKE_CASE : Tuple = self._prepare_inference_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_speed(_inference )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
__SCREAMING_SNAKE_CASE : Any = self._prepare_train_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_speed(_train )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :int ):
# initialize GPU on separate process
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = self._prepare_inference_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_memory(_inference )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :int ):
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
__SCREAMING_SNAKE_CASE : str = self._prepare_train_func(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return self._measure_memory(_train )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
__SCREAMING_SNAKE_CASE : Optional[int] = (
hasattr(_lowerCamelCase , '''architectures''' )
and isinstance(config.architectures , _lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
__SCREAMING_SNAKE_CASE : Optional[Any] = '''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
__SCREAMING_SNAKE_CASE : Tuple = __import__('''transformers''' , fromlist=[model_class] )
__SCREAMING_SNAKE_CASE : Any = getattr(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = model_cls(_lowerCamelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = TF_MODEL_MAPPING[config.__class__](_lowerCamelCase )
# encoder-decoder has vocab size saved differently
__SCREAMING_SNAKE_CASE : Any = config.vocab_size if hasattr(_lowerCamelCase , '''vocab_size''' ) else config.encoder.vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = random_input_ids(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(_lowerCamelCase , decoder_input_ids=_lowerCamelCase , training=_lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(_lowerCamelCase , training=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :str , _lowerCamelCase :int , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : Dict = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.''' )
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
__SCREAMING_SNAKE_CASE : Dict = (
hasattr(_lowerCamelCase , '''architectures''' )
and isinstance(config.architectures , _lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
__SCREAMING_SNAKE_CASE : List[str] = '''TF''' + config.architectures[0] # prepend 'TF' for tensorflow model
__SCREAMING_SNAKE_CASE : str = __import__('''transformers''' , fromlist=[model_class] )
__SCREAMING_SNAKE_CASE : Dict = getattr(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = model_cls(_lowerCamelCase )
except ImportError:
raise ImportError(
f'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.''' )
else:
__SCREAMING_SNAKE_CASE : str = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCamelCase )
# encoder-decoder has vocab size saved differently
__SCREAMING_SNAKE_CASE : Optional[int] = config.vocab_size if hasattr(_lowerCamelCase , '''vocab_size''' ) else config.encoder.vocab_size
__SCREAMING_SNAKE_CASE : Optional[Any] = random_input_ids(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
__SCREAMING_SNAKE_CASE : Dict = model(_lowerCamelCase , decoder_input_ids=_lowerCamelCase , labels=_lowerCamelCase , training=_lowerCamelCase )[0]
__SCREAMING_SNAKE_CASE : Dict = tf.gradients(_lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(_lowerCamelCase , labels=_lowerCamelCase , training=_lowerCamelCase )[0]
__SCREAMING_SNAKE_CASE : Any = tf.gradients(_lowerCamelCase , model.trainable_variables )
return gradients
__SCREAMING_SNAKE_CASE : Tuple = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Dict ):
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation''' )
timeit.repeat(_lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
__SCREAMING_SNAKE_CASE : Optional[int] = timeit.repeat(
_lowerCamelCase , repeat=self.args.repeat , number=1_0 , )
return min(_lowerCamelCase ) / 1_0.0
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Callable[[], None] ):
logger.info(
'''Note that TensorFlow allocates more memory than '''
'''it might need to speed up computation. '''
'''The memory reported here corresponds to the memory '''
'''reported by `nvidia-smi`, which can vary depending '''
'''on total available memory on the GPU that is used.''' )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
'''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
''' consumption line by line.''' )
__SCREAMING_SNAKE_CASE : List[str] = start_memory_tracing('''transformers''' )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
'''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
''' with `args.memory=False`''' )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
'''py3nvml not installed, we won\'t log GPU memory usage. '''
'''Install py3nvml (pip install py3nvml) to log information about GPU.''' )
__SCREAMING_SNAKE_CASE : Any = '''N/A'''
else:
logger.info(
'''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
''' running on the same GPU.''' )
# init nvml
nvml.nvmlInit()
func()
__SCREAMING_SNAKE_CASE : Any = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
__SCREAMING_SNAKE_CASE : Optional[int] = nvml.nvmlDeviceGetMemoryInfo(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = meminfo.used
__SCREAMING_SNAKE_CASE : Optional[int] = Memory(_lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
'''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
''' TensorFlow.''' )
__SCREAMING_SNAKE_CASE : Any = None
else:
__SCREAMING_SNAKE_CASE : List[str] = measure_peak_memory_cpu(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = Memory(_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
__SCREAMING_SNAKE_CASE : List[str] = stop_memory_tracing(_lowerCamelCase )
if memory is None:
__SCREAMING_SNAKE_CASE : int = summary.total
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 674 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_lowerCamelCase = abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Dict ):
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
__SCREAMING_SNAKE_CASE : int = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(lowercase_ , id=lowercase_ )
| 674 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 1 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 674 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''instructblip_vision_model'''
def __init__( self :Any , _lowerCamelCase :Dict=1_4_0_8 , _lowerCamelCase :Dict=6_1_4_4 , _lowerCamelCase :Any=3_9 , _lowerCamelCase :Dict=1_6 , _lowerCamelCase :Optional[Any]=2_2_4 , _lowerCamelCase :Optional[Any]=1_4 , _lowerCamelCase :List[str]="gelu" , _lowerCamelCase :List[Any]=1e-6 , _lowerCamelCase :Optional[Any]=0.0 , _lowerCamelCase :List[Any]=1e-10 , _lowerCamelCase :Tuple=True , **_lowerCamelCase :Any , ):
super().__init__(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
__SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
__SCREAMING_SNAKE_CASE : str = num_attention_heads
__SCREAMING_SNAKE_CASE : str = patch_size
__SCREAMING_SNAKE_CASE : str = image_size
__SCREAMING_SNAKE_CASE : int = initializer_range
__SCREAMING_SNAKE_CASE : Any = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
__SCREAMING_SNAKE_CASE : int = hidden_act
__SCREAMING_SNAKE_CASE : Optional[Any] = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :List[Any] , _lowerCamelCase :Union[str, os.PathLike] , **_lowerCamelCase :List[Any] ):
cls._set_token_in_kwargs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__SCREAMING_SNAKE_CASE : Union[str, Any] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''instructblip_qformer'''
def __init__( self :Optional[int] , _lowerCamelCase :Union[str, Any]=3_0_5_2_2 , _lowerCamelCase :List[Any]=7_6_8 , _lowerCamelCase :Dict=1_2 , _lowerCamelCase :Optional[int]=1_2 , _lowerCamelCase :int=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :Optional[int]=0.1 , _lowerCamelCase :str=0.1 , _lowerCamelCase :Optional[Any]=5_1_2 , _lowerCamelCase :Tuple=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :Union[str, Any]=0 , _lowerCamelCase :Tuple="absolute" , _lowerCamelCase :Any=2 , _lowerCamelCase :int=1_4_0_8 , **_lowerCamelCase :str , ):
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = vocab_size
__SCREAMING_SNAKE_CASE : str = hidden_size
__SCREAMING_SNAKE_CASE : int = num_hidden_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
__SCREAMING_SNAKE_CASE : Dict = hidden_act
__SCREAMING_SNAKE_CASE : Optional[int] = intermediate_size
__SCREAMING_SNAKE_CASE : Tuple = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = initializer_range
__SCREAMING_SNAKE_CASE : Optional[int] = layer_norm_eps
__SCREAMING_SNAKE_CASE : List[str] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = cross_attention_frequency
__SCREAMING_SNAKE_CASE : Optional[int] = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :str , _lowerCamelCase :Union[str, os.PathLike] , **_lowerCamelCase :Optional[int] ):
cls._set_token_in_kwargs(_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = cls.get_config_dict(_lowerCamelCase , **_lowerCamelCase )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__SCREAMING_SNAKE_CASE : Dict = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowerCamelCase , **_lowerCamelCase )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''instructblip'''
lowerCamelCase__ = True
def __init__( self :Union[str, Any] , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Any=None , _lowerCamelCase :Optional[int]=None , _lowerCamelCase :Union[str, Any]=3_2 , **_lowerCamelCase :Dict ):
super().__init__(**_lowerCamelCase )
if vision_config is None:
__SCREAMING_SNAKE_CASE : int = {}
logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' )
if qformer_config is None:
__SCREAMING_SNAKE_CASE : List[str] = {}
logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' )
if text_config is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = InstructBlipVisionConfig(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = InstructBlipQFormerConfig(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
__SCREAMING_SNAKE_CASE : List[Any] = CONFIG_MAPPING[text_model_type](**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = self.text_config.tie_word_embeddings
__SCREAMING_SNAKE_CASE : Dict = self.text_config.is_encoder_decoder
__SCREAMING_SNAKE_CASE : Optional[Any] = num_query_tokens
__SCREAMING_SNAKE_CASE : Optional[int] = self.vision_config.hidden_size
__SCREAMING_SNAKE_CASE : Dict = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__SCREAMING_SNAKE_CASE : Optional[int] = 1.0
__SCREAMING_SNAKE_CASE : List[str] = 0.0_2
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :int , _lowerCamelCase :InstructBlipVisionConfig , _lowerCamelCase :InstructBlipQFormerConfig , _lowerCamelCase :PretrainedConfig , **_lowerCamelCase :Union[str, Any] , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_lowerCamelCase , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = copy.deepcopy(self.__dict__ )
__SCREAMING_SNAKE_CASE : Optional[int] = self.vision_config.to_dict()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.qformer_config.to_dict()
__SCREAMING_SNAKE_CASE : List[str] = self.text_config.to_dict()
__SCREAMING_SNAKE_CASE : Dict = self.__class__.model_type
return output
| 674 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 674 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 1 |
"""simple docstring"""
import pprint
import requests
_lowerCamelCase = '''https://zenquotes.io/api'''
def lowerCAmelCase_ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def lowerCAmelCase_ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if __name__ == "__main__":
_lowerCamelCase = random_quotes()
pprint.pprint(response)
| 674 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
_lowerCamelCase = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : Any ):
'''simple docstring'''
for attribute in key.split('''.''' ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
__SCREAMING_SNAKE_CASE : int = '''lm_head'''
__SCREAMING_SNAKE_CASE : Optional[Any] = getattr(lowercase_ , lowercase_ )
if weight_type is not None:
__SCREAMING_SNAKE_CASE : Any = getattr(lowercase_ , lowercase_ ).shape
else:
__SCREAMING_SNAKE_CASE : Tuple = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
__SCREAMING_SNAKE_CASE : Dict = value
elif weight_type == "weight_g":
__SCREAMING_SNAKE_CASE : Optional[Any] = value
elif weight_type == "weight_v":
__SCREAMING_SNAKE_CASE : str = value
elif weight_type == "bias":
__SCREAMING_SNAKE_CASE : List[Any] = value
else:
__SCREAMING_SNAKE_CASE : int = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : List[str] , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Tuple = fairseq_model.state_dict()
__SCREAMING_SNAKE_CASE : Optional[int] = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
__SCREAMING_SNAKE_CASE : List[str] = False
if "conv_layers" in name:
load_conv_layer(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , hf_model.config.feat_extract_norm == '''group''' , )
__SCREAMING_SNAKE_CASE : Any = True
else:
for key, mapped_key in MAPPING.items():
__SCREAMING_SNAKE_CASE : Optional[Any] = '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
__SCREAMING_SNAKE_CASE : Dict = True
if "*" in mapped_key:
__SCREAMING_SNAKE_CASE : Tuple = name.split(lowercase_ )[0].split('''.''' )[-2]
__SCREAMING_SNAKE_CASE : Optional[int] = mapped_key.replace('''*''' , lowercase_ )
if "weight_g" in name:
__SCREAMING_SNAKE_CASE : Dict = '''weight_g'''
elif "weight_v" in name:
__SCREAMING_SNAKE_CASE : Optional[Any] = '''weight_v'''
elif "bias" in name:
__SCREAMING_SNAKE_CASE : int = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__SCREAMING_SNAKE_CASE : Dict = '''weight'''
else:
__SCREAMING_SNAKE_CASE : Tuple = None
set_recursively(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
continue
if not is_used:
unused_weights.append(lowercase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = full_name.split('''conv_layers.''' )[-1]
__SCREAMING_SNAKE_CASE : Tuple = name.split('''.''' )
__SCREAMING_SNAKE_CASE : List[Any] = int(items[0] )
__SCREAMING_SNAKE_CASE : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
__SCREAMING_SNAKE_CASE : str = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
__SCREAMING_SNAKE_CASE : List[Any] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
__SCREAMING_SNAKE_CASE : Optional[int] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(lowercase_ )
@torch.no_grad()
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Any , lowercase_ : Union[str, Any]=None , lowercase_ : Optional[Any]=None , lowercase_ : str=True ):
'''simple docstring'''
if config_path is not None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = UniSpeechConfig.from_pretrained(lowercase_ )
else:
__SCREAMING_SNAKE_CASE : Tuple = UniSpeechConfig()
if is_finetuned:
if dict_path:
__SCREAMING_SNAKE_CASE : Tuple = Dictionary.load_from_json(lowercase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__SCREAMING_SNAKE_CASE : List[str] = target_dict.pad_index
__SCREAMING_SNAKE_CASE : Dict = target_dict.bos_index
__SCREAMING_SNAKE_CASE : Optional[Any] = target_dict.eos_index
__SCREAMING_SNAKE_CASE : List[Any] = len(target_dict.symbols )
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(lowercase_ , '''vocab.json''' )
if not os.path.isdir(lowercase_ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_ ) )
return
os.makedirs(lowercase_ , exist_ok=lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = target_dict.indices
# fairseq has the <pad> and <s> switched
__SCREAMING_SNAKE_CASE : List[Any] = 42
__SCREAMING_SNAKE_CASE : Tuple = 43
with open(lowercase_ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(lowercase_ , lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = WavaVecaPhonemeCTCTokenizer(
lowercase_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=lowercase_ , )
__SCREAMING_SNAKE_CASE : str = True if config.feat_extract_norm == '''layer''' else False
__SCREAMING_SNAKE_CASE : Any = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=lowercase_ , return_attention_mask=lowercase_ , )
__SCREAMING_SNAKE_CASE : Tuple = WavaVecaProcessor(feature_extractor=lowercase_ , tokenizer=lowercase_ )
processor.save_pretrained(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = UniSpeechForCTC(lowercase_ )
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = UniSpeechForPreTraining(lowercase_ )
if is_finetuned:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ), '''w2v_path''': checkpoint_path} )
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__SCREAMING_SNAKE_CASE : Optional[Any] = model[0].eval()
recursively_load_weights(lowercase_ , lowercase_ , lowercase_ )
hf_unispeech.save_pretrained(lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
_lowerCamelCase = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 674 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 1 |
"""simple docstring"""
import os
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(os.path.dirname(lowercase_ ) , '''num.txt''' )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 674 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_lowerCamelCase = TypeVar('''KT''')
_lowerCamelCase = TypeVar('''VT''')
class snake_case ( Generic[KT, VT] ):
def __init__( self :int , _lowerCamelCase :KT | str = "root" , _lowerCamelCase :VT | None = None ):
__SCREAMING_SNAKE_CASE : List[Any] = key
__SCREAMING_SNAKE_CASE : Optional[Any] = value
__SCREAMING_SNAKE_CASE : list[Node[KT, VT]] = []
def __repr__( self :Dict ):
return f'''Node({self.key}: {self.value})'''
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return len(self.forward )
class snake_case ( Generic[KT, VT] ):
def __init__( self :Optional[int] , _lowerCamelCase :float = 0.5 , _lowerCamelCase :int = 1_6 ):
__SCREAMING_SNAKE_CASE : Node[KT, VT] = Node[KT, VT]()
__SCREAMING_SNAKE_CASE : Dict = 0
__SCREAMING_SNAKE_CASE : str = p
__SCREAMING_SNAKE_CASE : List[str] = max_level
def __str__( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Optional[int] = list(self )
if len(_lowerCamelCase ) == 0:
return f'''SkipList(level={self.level})'''
__SCREAMING_SNAKE_CASE : Any = max((len(str(_lowerCamelCase ) ) for item in items) , default=4 )
__SCREAMING_SNAKE_CASE : List[Any] = max(_lowerCamelCase , 4 ) + 4
__SCREAMING_SNAKE_CASE : Tuple = self.head
__SCREAMING_SNAKE_CASE : List[str] = []
__SCREAMING_SNAKE_CASE : List[Any] = node.forward.copy()
lines.append(f'''[{node.key}]'''.ljust(_lowerCamelCase , '''-''' ) + '''* ''' * len(_lowerCamelCase ) )
lines.append(''' ''' * label_size + '''| ''' * len(_lowerCamelCase ) )
while len(node.forward ) != 0:
__SCREAMING_SNAKE_CASE : List[Any] = node.forward[0]
lines.append(
f'''[{node.key}]'''.ljust(_lowerCamelCase , '''-''' )
+ ''' '''.join(str(n.key ) if n.key == node.key else '''|''' for n in forwards ) )
lines.append(''' ''' * label_size + '''| ''' * len(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[str] = node.forward
lines.append('''None'''.ljust(_lowerCamelCase ) + '''* ''' * len(_lowerCamelCase ) )
return f'''SkipList(level={self.level})\n''' + "\n".join(_lowerCamelCase )
def __iter__( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
__SCREAMING_SNAKE_CASE : List[str] = node.forward[0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Any = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : List[str] = []
__SCREAMING_SNAKE_CASE : Any = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
__SCREAMING_SNAKE_CASE : Union[str, Any] = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(_lowerCamelCase )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :KT ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = self._locate_node(_lowerCamelCase )
if node is not None:
for i, update_node in enumerate(_lowerCamelCase ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
__SCREAMING_SNAKE_CASE : List[str] = node.forward[i]
else:
__SCREAMING_SNAKE_CASE : int = update_node.forward[:i]
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :KT , _lowerCamelCase :VT ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self._locate_node(_lowerCamelCase )
if node is not None:
__SCREAMING_SNAKE_CASE : List[str] = value
else:
__SCREAMING_SNAKE_CASE : str = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , _lowerCamelCase ):
update_vector.append(self.head )
__SCREAMING_SNAKE_CASE : List[Any] = level
__SCREAMING_SNAKE_CASE : Tuple = Node(_lowerCamelCase , _lowerCamelCase )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : str = new_node
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :VT ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self._locate_node(_lowerCamelCase )
if node is not None:
return node.value
return None
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = SkipList()
skip_list.insert('''Key1''' , 3 )
skip_list.insert('''Key2''' , 12 )
skip_list.insert('''Key3''' , 41 )
skip_list.insert('''Key4''' , -19 )
__SCREAMING_SNAKE_CASE : int = skip_list.head
__SCREAMING_SNAKE_CASE : Optional[int] = {}
while node.level != 0:
__SCREAMING_SNAKE_CASE : str = node.forward[0]
__SCREAMING_SNAKE_CASE : List[str] = node.value
assert len(lowercase_ ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = SkipList()
skip_list.insert('''Key1''' , 10 )
skip_list.insert('''Key1''' , 12 )
skip_list.insert('''Key5''' , 7 )
skip_list.insert('''Key7''' , 10 )
skip_list.insert('''Key10''' , 5 )
skip_list.insert('''Key7''' , 7 )
skip_list.insert('''Key5''' , 5 )
skip_list.insert('''Key10''' , 10 )
__SCREAMING_SNAKE_CASE : int = skip_list.head
__SCREAMING_SNAKE_CASE : int = {}
while node.level != 0:
__SCREAMING_SNAKE_CASE : Union[str, Any] = node.forward[0]
__SCREAMING_SNAKE_CASE : Dict = node.value
if len(lowercase_ ) != 4:
print()
assert len(lowercase_ ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = SkipList()
assert skip_list.find('''Some key''' ) is None
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = SkipList()
skip_list.insert('''Key2''' , 20 )
assert skip_list.find('''Key2''' ) == 20
skip_list.insert('''Some Key''' , 10 )
skip_list.insert('''Key2''' , 8 )
skip_list.insert('''V''' , 13 )
assert skip_list.find('''Y''' ) is None
assert skip_list.find('''Key2''' ) == 8
assert skip_list.find('''Some Key''' ) == 10
assert skip_list.find('''V''' ) == 13
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = SkipList()
skip_list.delete('''Some key''' )
assert len(skip_list.head.forward ) == 0
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = SkipList()
skip_list.insert('''Key1''' , 12 )
skip_list.insert('''V''' , 13 )
skip_list.insert('''X''' , 14 )
skip_list.insert('''Key2''' , 15 )
skip_list.delete('''V''' )
skip_list.delete('''Key2''' )
assert skip_list.find('''V''' ) is None
assert skip_list.find('''Key2''' ) is None
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = SkipList()
skip_list.insert('''Key1''' , 12 )
skip_list.insert('''V''' , 13 )
skip_list.insert('''X''' , 14 )
skip_list.insert('''Key2''' , 15 )
skip_list.delete('''V''' )
assert skip_list.find('''V''' ) is None
assert skip_list.find('''X''' ) == 14
assert skip_list.find('''Key1''' ) == 12
assert skip_list.find('''Key2''' ) == 15
skip_list.delete('''X''' )
assert skip_list.find('''V''' ) is None
assert skip_list.find('''X''' ) is None
assert skip_list.find('''Key1''' ) == 12
assert skip_list.find('''Key2''' ) == 15
skip_list.delete('''Key1''' )
assert skip_list.find('''V''' ) is None
assert skip_list.find('''X''' ) is None
assert skip_list.find('''Key1''' ) is None
assert skip_list.find('''Key2''' ) == 15
skip_list.delete('''Key2''' )
assert skip_list.find('''V''' ) is None
assert skip_list.find('''X''' ) is None
assert skip_list.find('''Key1''' ) is None
assert skip_list.find('''Key2''' ) is None
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = SkipList()
skip_list.insert('''Key1''' , 12 )
skip_list.insert('''V''' , 13 )
skip_list.insert('''X''' , 142 )
skip_list.insert('''Key2''' , 15 )
skip_list.delete('''X''' )
def traverse_keys(lowercase_ : List[Any] ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(lowercase_ )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def lowerCAmelCase_ ( ):
'''simple docstring'''
def is_sorted(lowercase_ : str ):
return all(next_item >= item for item, next_item in zip(lowercase_ , lst[1:] ) )
__SCREAMING_SNAKE_CASE : str = SkipList()
for i in range(10 ):
skip_list.insert(lowercase_ , lowercase_ )
assert is_sorted(list(lowercase_ ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(lowercase_ ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(lowercase_ ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : str = SkipList()
skip_list.insert(2 , '''2''' )
skip_list.insert(4 , '''4''' )
skip_list.insert(6 , '''4''' )
skip_list.insert(4 , '''5''' )
skip_list.insert(8 , '''4''' )
skip_list.insert(9 , '''4''' )
skip_list.delete(4 )
print(lowercase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 674 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''roc_bert'''
def __init__( self :Union[str, Any] , _lowerCamelCase :Any=3_0_5_2_2 , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Optional[Any]=1_2 , _lowerCamelCase :List[str]=1_2 , _lowerCamelCase :str=3_0_7_2 , _lowerCamelCase :Tuple="gelu" , _lowerCamelCase :List[Any]=0.1 , _lowerCamelCase :List[str]=0.1 , _lowerCamelCase :Optional[int]=5_1_2 , _lowerCamelCase :Dict=2 , _lowerCamelCase :Any=0.0_2 , _lowerCamelCase :Optional[int]=1e-12 , _lowerCamelCase :str=True , _lowerCamelCase :Any=0 , _lowerCamelCase :List[str]="absolute" , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Any=True , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :str=7_6_8 , _lowerCamelCase :Union[str, Any]=9_1_0 , _lowerCamelCase :List[Any]=5_1_2 , _lowerCamelCase :Optional[int]=2_4_8_5_8 , _lowerCamelCase :Union[str, Any]=True , **_lowerCamelCase :str , ):
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : int = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : str = num_hidden_layers
__SCREAMING_SNAKE_CASE : int = num_attention_heads
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = use_cache
__SCREAMING_SNAKE_CASE : str = enable_pronunciation
__SCREAMING_SNAKE_CASE : List[str] = enable_shape
__SCREAMING_SNAKE_CASE : Tuple = pronunciation_embed_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = pronunciation_vocab_size
__SCREAMING_SNAKE_CASE : str = shape_embed_dim
__SCREAMING_SNAKE_CASE : Union[str, Any] = shape_vocab_size
__SCREAMING_SNAKE_CASE : Tuple = concat_input
__SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
__SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=_lowerCamelCase , **_lowerCamelCase )
| 674 | 1 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int = 50 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'{solution() = }')
| 674 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Optional[Any]=True , lowercase_ : Any="pt" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''add_prefix_space''': True} if isinstance(lowercase_ , lowercase_ ) and not line.startswith(''' ''' ) else {}
__SCREAMING_SNAKE_CASE : Optional[int] = padding_side
return tokenizer(
[line] , max_length=lowercase_ , padding='''max_length''' if pad_to_max_length else None , truncation=lowercase_ , return_tensors=lowercase_ , add_special_tokens=lowercase_ , **lowercase_ , )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : List[Any]=None , ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = input_ids.ne(lowercase_ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class snake_case ( __UpperCAmelCase ):
def __init__( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Tuple , _lowerCamelCase :Any="train" , _lowerCamelCase :str=None , _lowerCamelCase :Optional[Any]=None , _lowerCamelCase :List[Any]=None , _lowerCamelCase :Tuple="" , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ).joinpath(type_path + '''.source''' )
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ).joinpath(type_path + '''.target''' )
__SCREAMING_SNAKE_CASE : Any = self.get_char_lens(self.src_file )
__SCREAMING_SNAKE_CASE : List[str] = max_source_length
__SCREAMING_SNAKE_CASE : Dict = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
__SCREAMING_SNAKE_CASE : Dict = tokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = prefix
if n_obs is not None:
__SCREAMING_SNAKE_CASE : Any = self.src_lens[:n_obs]
__SCREAMING_SNAKE_CASE : List[str] = src_lang
__SCREAMING_SNAKE_CASE : str = tgt_lang
def __len__( self :int ):
return len(self.src_lens )
def __getitem__( self :Optional[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = index + 1 # linecache starts at 1
__SCREAMING_SNAKE_CASE : Any = self.prefix + linecache.getline(str(self.src_file ) , _lowerCamelCase ).rstrip('''\n''' )
__SCREAMING_SNAKE_CASE : Dict = linecache.getline(str(self.tgt_file ) , _lowerCamelCase ).rstrip('''\n''' )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowerCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
__SCREAMING_SNAKE_CASE : Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
)
__SCREAMING_SNAKE_CASE : Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer , _lowerCamelCase ) else self.tokenizer
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_source_length , '''right''' )
__SCREAMING_SNAKE_CASE : Dict = encode_line(_lowerCamelCase , _lowerCamelCase , self.max_target_length , '''right''' )
__SCREAMING_SNAKE_CASE : Any = source_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Any = target_inputs['''input_ids'''].squeeze()
__SCREAMING_SNAKE_CASE : Dict = source_inputs['''attention_mask'''].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :Any ):
return [len(_lowerCamelCase ) for x in Path(_lowerCamelCase ).open().readlines()]
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = torch.stack([x['''attention_mask'''] for x in batch] )
__SCREAMING_SNAKE_CASE : int = torch.stack([x['''decoder_input_ids'''] for x in batch] )
__SCREAMING_SNAKE_CASE : str = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowerCamelCase )
else self.tokenizer.pad_token_id
)
__SCREAMING_SNAKE_CASE : List[str] = trim_batch(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = trim_batch(_lowerCamelCase , _lowerCamelCase , attention_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = {
'''input_ids''': source_ids,
'''attention_mask''': source_mask,
'''decoder_input_ids''': y,
}
return batch
_lowerCamelCase = getLogger(__name__)
def lowerCAmelCase_ ( lowercase_ : List[List] ):
'''simple docstring'''
return list(itertools.chain.from_iterable(lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = get_git_info()
save_json(lowercase_ , os.path.join(lowercase_ , '''git_log.json''' ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Optional[int] , lowercase_ : str=4 , **lowercase_ : List[str] ):
'''simple docstring'''
with open(lowercase_ , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=lowercase_ , **lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] ):
'''simple docstring'''
with open(lowercase_ ) as f:
return json.load(lowercase_ )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = git.Repo(search_parent_directories=lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
'''hostname''': str(socket.gethostname() ),
}
return repo_infos
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : Iterable ):
'''simple docstring'''
return list(map(lowercase_ , lowercase_ ) )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : Any ):
'''simple docstring'''
with open(lowercase_ , '''wb''' ) as f:
return pickle.dump(lowercase_ , lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
def remove_articles(lowercase_ : Dict ):
return re.sub(r'''\b(a|an|the)\b''' , ''' ''' , lowercase_ )
def white_space_fix(lowercase_ : Optional[int] ):
return " ".join(text.split() )
def remove_punc(lowercase_ : Any ):
__SCREAMING_SNAKE_CASE : Optional[int] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(lowercase_ : int ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(lowercase_ ) ) ) )
def lowerCAmelCase_ ( lowercase_ : Optional[int] , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Any = normalize_answer(lowercase_ ).split()
__SCREAMING_SNAKE_CASE : Tuple = Counter(lowercase_ ) & Counter(lowercase_ )
__SCREAMING_SNAKE_CASE : Tuple = sum(common.values() )
if num_same == 0:
return 0
__SCREAMING_SNAKE_CASE : Any = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : List[str] = 1.0 * num_same / len(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = (2 * precision * recall) / (precision + recall)
return fa
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Union[str, Any] ):
'''simple docstring'''
return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
assert len(lowercase_ ) == len(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for hypo, pred in zip(lowercase_ , lowercase_ ):
em += exact_match_score(lowercase_ , lowercase_ )
if len(lowercase_ ) > 0:
em /= len(lowercase_ )
return {"em": em}
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
return model_prefix.startswith('''rag''' )
def lowerCAmelCase_ ( lowercase_ : Optional[Any] , lowercase_ : Tuple , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
__SCREAMING_SNAKE_CASE : Any = '''dropout_rate'''
for p in extra_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
if not hasattr(lowercase_ , lowercase_ ) and not hasattr(lowercase_ , equivalent_param[p] ):
logger.info('''config doesn\'t have a `{}` attribute'''.format(lowercase_ ) )
delattr(lowercase_ , lowercase_ )
continue
__SCREAMING_SNAKE_CASE : Optional[int] = p if hasattr(lowercase_ , lowercase_ ) else equivalent_param[p]
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
delattr(lowercase_ , lowercase_ )
return hparams, config
| 674 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
SCREAMING_SNAKE_CASE__ : Optional[Any] = pytest.mark.integration
SCREAMING_SNAKE_CASE__ : Dict = {"""comet"""}
SCREAMING_SNAKE_CASE__ : Dict = importlib.util.find_spec("""fairseq""") is not None
SCREAMING_SNAKE_CASE__ : Tuple = {"""code_eval"""}
SCREAMING_SNAKE_CASE__ : List[Any] = os.name == """nt"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bertscore""", """frugalscore""", """perplexity"""}
SCREAMING_SNAKE_CASE__ : Dict = importlib.util.find_spec("""transformers""") is not None
def __lowercase ( snake_case ):
"""simple docstring"""
@wraps(snake_case )
def wrapper(self, snake_case ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('''"test requires Fairseq"''' )
else:
test_case(self, snake_case )
return wrapper
def __lowercase ( snake_case ):
"""simple docstring"""
@wraps(snake_case )
def wrapper(self, snake_case ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('''"test requires transformers"''' )
else:
test_case(self, snake_case )
return wrapper
def __lowercase ( snake_case ):
"""simple docstring"""
@wraps(snake_case )
def wrapper(self, snake_case ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('''"test not supported on Windows"''' )
else:
test_case(self, snake_case )
return wrapper
def __lowercase ( ):
"""simple docstring"""
__magic_name__ :Optional[int] = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('''./metrics/*/''' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
lowerCamelCase , lowerCamelCase , lowerCamelCase )
@local
class lowerCamelCase_ ( parameterized.TestCase ):
a__ = {}
a__ = None
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning''' )
def A ( self , __lowerCAmelCase ):
"""simple docstring"""
__magic_name__ :Tuple = '''[...]'''
__magic_name__ :int = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('''metrics''' , __lowerCAmelCase ) ).module_path )
__magic_name__ :Any = datasets.load.import_main_class(metric_module.__name__ , dataset=__lowerCAmelCase )
# check parameters
__magic_name__ :Tuple = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(__lowerCAmelCase , metric_module.__name__ ):
with self.use_local_metrics():
try:
__magic_name__ :Any = doctest.testmod(__lowerCAmelCase , verbose=__lowerCAmelCase , raise_on_error=__lowerCAmelCase )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def A ( self , __lowerCAmelCase ):
"""simple docstring"""
__magic_name__ :Union[str, Any] = '''[...]'''
__magic_name__ :Dict = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('''metrics''' , __lowerCAmelCase ) ).module_path )
# run doctest
with self.use_local_metrics():
__magic_name__ :int = doctest.testmod(__lowerCAmelCase , verbose=__lowerCAmelCase , raise_on_error=__lowerCAmelCase )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def A ( self , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](__lowerCAmelCase ):
yield
else:
yield
@contextmanager
def A ( self ):
"""simple docstring"""
def load_local_metric(__lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase ):
return load_metric(os.path.join('''metrics''' , __lowerCAmelCase ) , *__lowerCAmelCase , **__lowerCAmelCase )
with patch('''datasets.load_metric''' ) as mock_load_metric:
__magic_name__ :Dict = load_local_metric
yield
@classmethod
def A ( cls , __lowerCAmelCase ):
"""simple docstring"""
def wrapper(__lowerCAmelCase ):
__magic_name__ :Union[str, Any] = contextmanager(__lowerCAmelCase )
__magic_name__ :Dict = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('''bleurt''' )
def __lowercase ( snake_case ):
"""simple docstring"""
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('''sv''', '''''', '''''' ) # handle pytest cli flags
class lowerCamelCase_ ( lowerCamelCase ):
def A ( self , __lowerCAmelCase ):
"""simple docstring"""
assert len(input_dict['''input_ids'''] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('''bleurt.score._create_predictor''' ) as mock_create_predictor:
__magic_name__ :List[Any] = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('''bertscore''' )
def __lowercase ( snake_case ):
"""simple docstring"""
import torch
def bert_cos_score_idf(snake_case, snake_case, *snake_case, **snake_case ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('''bert_score.scorer.get_model''' ), patch(
'''bert_score.scorer.bert_cos_score_idf''' ) as mock_bert_cos_score_idf:
__magic_name__ :Optional[int] = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('''comet''' )
def __lowercase ( snake_case ):
"""simple docstring"""
def load_from_checkpoint(snake_case ):
class lowerCamelCase_ :
def A ( self , __lowerCAmelCase , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
assert len(__lowerCAmelCase ) == 2
__magic_name__ :int = [0.19, 0.92]
return scores, sum(__lowerCAmelCase ) / len(__lowerCAmelCase )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('''comet.download_model''' ) as mock_download_model:
__magic_name__ :Optional[int] = None
with patch('''comet.load_from_checkpoint''' ) as mock_load_from_checkpoint:
__magic_name__ :Any = load_from_checkpoint
yield
def __lowercase ( ):
"""simple docstring"""
__magic_name__ :Optional[int] = load_metric(os.path.join('''metrics''', '''seqeval''' ) )
__magic_name__ :List[str] = '''ERROR'''
__magic_name__ :str = f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(snake_case, match=re.escape(snake_case ) ):
metric.compute(predictions=[], references=[], scheme=snake_case )
| 0 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = int(np.ceil((x_end - xa) / step_size ) )
__SCREAMING_SNAKE_CASE : Dict = np.zeros((n + 1,) )
__SCREAMING_SNAKE_CASE : List[Any] = ya
__SCREAMING_SNAKE_CASE : Dict = xa
for k in range(lowercase_ ):
__SCREAMING_SNAKE_CASE : str = y[k] + step_size * ode_func(lowercase_ , y[k] )
__SCREAMING_SNAKE_CASE : int = y[k] + (
(step_size / 2) * (ode_func(lowercase_ , y[k] ) + ode_func(x + step_size , lowercase_ ))
)
x += step_size
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
def _A ( _lowercase = 10**9 ) -> int:
"""simple docstring"""
__UpperCamelCase = 1
__UpperCamelCase = 2
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
__UpperCamelCase = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"""{solution() = }""")
| 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
_lowerCamelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 674 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
"""tokenization_roberta""": ["""RobertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ["""RobertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"""ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaForCausalLM""",
"""RobertaForMaskedLM""",
"""RobertaForMultipleChoice""",
"""RobertaForQuestionAnswering""",
"""RobertaForSequenceClassification""",
"""RobertaForTokenClassification""",
"""RobertaModel""",
"""RobertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"""TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaForCausalLM""",
"""TFRobertaForMaskedLM""",
"""TFRobertaForMultipleChoice""",
"""TFRobertaForQuestionAnswering""",
"""TFRobertaForSequenceClassification""",
"""TFRobertaForTokenClassification""",
"""TFRobertaMainLayer""",
"""TFRobertaModel""",
"""TFRobertaPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = [
"""FlaxRobertaForCausalLM""",
"""FlaxRobertaForMaskedLM""",
"""FlaxRobertaForMultipleChoice""",
"""FlaxRobertaForQuestionAnswering""",
"""FlaxRobertaForSequenceClassification""",
"""FlaxRobertaForTokenClassification""",
"""FlaxRobertaModel""",
"""FlaxRobertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 2 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[list[int]] = []
__SCREAMING_SNAKE_CASE : dict[int, int] = {}
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :int , _lowerCamelCase :int ):
self.m_edges.append([u_node, v_node, weight] )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :int ):
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :int ):
if self.m_component[u_node] != u_node:
for k in self.m_component:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.find_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :list[int] , _lowerCamelCase :int , _lowerCamelCase :int ):
if component_size[u_node] <= component_size[v_node]:
__SCREAMING_SNAKE_CASE : List[Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowerCamelCase )
elif component_size[u_node] >= component_size[v_node]:
__SCREAMING_SNAKE_CASE : Dict = self.find_component(_lowerCamelCase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = []
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
__SCREAMING_SNAKE_CASE : str = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = edge
__SCREAMING_SNAKE_CASE : Optional[Any] = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
__SCREAMING_SNAKE_CASE : Optional[Any] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowerCamelCase , _lowerCamelCase ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = edge
__SCREAMING_SNAKE_CASE : Tuple = self.m_component[u]
__SCREAMING_SNAKE_CASE : int = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
__SCREAMING_SNAKE_CASE : Optional[Any] = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 674 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def A_( A : int = 100_0000 , A : int = 10):
UpperCamelCase = defaultdict(A)
for outer_width in range(3 , (t_limit // 4) + 2):
if outer_width * outer_width > t_limit:
UpperCamelCase = max(
ceil(sqrt(outer_width * outer_width - t_limit)) , 1)
else:
UpperCamelCase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(A , outer_width - 1 , 2):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 3 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : Any , lowercase_ : int=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(lowercase_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(lowercase_ )
def lowerCAmelCase_ ( lowercase_ : Tuple , lowercase_ : int , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Optional[int] = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : List[str] , lowercase_ : List[str] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(weights[1] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(weights[2] )
__SCREAMING_SNAKE_CASE : Tuple = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(lowercase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowercase_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(lowercase_ ).view(-1 , lowercase_ ).contiguous().transpose(0 , 1 ) , )
def lowerCAmelCase_ ( lowercase_ : int , lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = weights[0][0][0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(layer_norm_a[0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# lsh weights + output
__SCREAMING_SNAKE_CASE : Tuple = weights[0][1]
if len(lowercase_ ) < 4:
set_layer_weights_in_torch_lsh(lowercase_ , torch_block.attention , lowercase_ )
else:
set_layer_weights_in_torch_local(lowercase_ , torch_block.attention , lowercase_ )
# intermediate weighs
__SCREAMING_SNAKE_CASE : Any = weights[2][0][1][2]
# Chunked Feed Forward
if len(lowercase_ ) == 4:
__SCREAMING_SNAKE_CASE : List[str] = intermediate_weights[2]
# layernorm 2
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(intermediate_weights[0][0] )
__SCREAMING_SNAKE_CASE : Union[str, Any] = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# intermediate dense
__SCREAMING_SNAKE_CASE : int = np.asarray(intermediate_weights[1][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
# intermediate out
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(intermediate_weights[4][0] )
__SCREAMING_SNAKE_CASE : Any = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = torch_model.reformer
# word embeds
__SCREAMING_SNAKE_CASE : int = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowercase_ ) , )
if isinstance(weights[3] , lowercase_ ):
__SCREAMING_SNAKE_CASE : int = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
__SCREAMING_SNAKE_CASE : Dict = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F'''{position_embeddings[emb_idx]} emb does not match'''
__SCREAMING_SNAKE_CASE : str = nn.Parameter(torch.tensor(lowercase_ ) )
__SCREAMING_SNAKE_CASE : List[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
lowercase_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(lowercase_ , lowercase_ , lowercase_ )
# output layer norm
__SCREAMING_SNAKE_CASE : List[str] = np.asarray(weights[7][0] )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(lowercase_ ) , torch.tensor(lowercase_ ) , )
# output embeddings
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(weights[9][0] )
__SCREAMING_SNAKE_CASE : List[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(lowercase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowercase_ ) , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : Any , lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = ReformerConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : List[str] = ReformerModelWithLMHead(lowercase_ )
with open(lowercase_ , '''rb''' ) as f:
__SCREAMING_SNAKE_CASE : int = pickle.load(lowercase_ )['''weights''']
set_model_weights_in_torch(lowercase_ , lowercase_ , config.hidden_size )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained Reformer model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 674 | 0 |
"""simple docstring"""
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = val
lowerCAmelCase = None
lowerCAmelCase = None
def UpperCamelCase__ ( self , _snake_case ):
"""simple docstring"""
if self.val:
if val < self.val:
if self.left is None:
lowerCAmelCase = Node(_snake_case )
else:
self.left.insert(_snake_case )
elif val > self.val:
if self.right is None:
lowerCAmelCase = Node(_snake_case )
else:
self.right.insert(_snake_case )
else:
lowerCAmelCase = val
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : str ):
# Recursive traversal
if root:
inorder(root.left , _UpperCAmelCase )
res.append(root.val )
inorder(root.right , _UpperCAmelCase )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
# Build BST
if len(_UpperCAmelCase ) == 0:
return arr
lowerCAmelCase = Node(arr[0] )
for i in range(1 , len(_UpperCAmelCase ) ):
root.insert(arr[i] )
# Traverse BST in order.
lowerCAmelCase = []
inorder(_UpperCAmelCase , _UpperCAmelCase )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 4 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'''
),
}
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = '''xlm-prophetnet'''
lowerCamelCase__ = ['''past_key_values''']
lowerCamelCase__ = {
'''num_attention_heads''': '''num_encoder_attention_heads''',
}
def __init__( self :List[str] , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[Union[str, Callable]] = "gelu" , _lowerCamelCase :Optional[int] = 3_0_5_2_2 , _lowerCamelCase :Optional[int] = 1_0_2_4 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[int] = 4_0_9_6 , _lowerCamelCase :Optional[int] = 1_2 , _lowerCamelCase :Optional[int] = 1_6 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[float] = 0.1 , _lowerCamelCase :Optional[int] = 5_1_2 , _lowerCamelCase :Optional[float] = 0.0_2 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 2 , _lowerCamelCase :Optional[int] = 3_2 , _lowerCamelCase :Optional[int] = 1_2_8 , _lowerCamelCase :Optional[bool] = False , _lowerCamelCase :Optional[float] = 0.0 , _lowerCamelCase :Optional[bool] = True , _lowerCamelCase :Optional[int] = 0 , _lowerCamelCase :Optional[int] = 1 , _lowerCamelCase :Optional[int] = 2 , **_lowerCamelCase :int , ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = vocab_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
__SCREAMING_SNAKE_CASE : List[Any] = encoder_ffn_dim
__SCREAMING_SNAKE_CASE : str = num_encoder_layers
__SCREAMING_SNAKE_CASE : Optional[Any] = num_encoder_attention_heads
__SCREAMING_SNAKE_CASE : str = decoder_ffn_dim
__SCREAMING_SNAKE_CASE : List[Any] = num_decoder_layers
__SCREAMING_SNAKE_CASE : List[str] = num_decoder_attention_heads
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : Any = init_std # Normal(0, this parameter)
__SCREAMING_SNAKE_CASE : Any = activation_function
# parameters for xlmprophetnet
__SCREAMING_SNAKE_CASE : List[Any] = ngram
__SCREAMING_SNAKE_CASE : int = num_buckets
__SCREAMING_SNAKE_CASE : List[str] = relative_max_distance
__SCREAMING_SNAKE_CASE : str = disable_ngram_loss
__SCREAMING_SNAKE_CASE : Optional[int] = eps
# 3 Types of Dropout
__SCREAMING_SNAKE_CASE : int = attention_dropout
__SCREAMING_SNAKE_CASE : Optional[Any] = activation_dropout
__SCREAMING_SNAKE_CASE : Dict = dropout
__SCREAMING_SNAKE_CASE : Any = use_cache
super().__init__(
pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase , is_encoder_decoder=_lowerCamelCase , add_cross_attention=_lowerCamelCase , decoder_start_token_id=_lowerCamelCase , **_lowerCamelCase , )
@property
def SCREAMING_SNAKE_CASE_ ( self :int ):
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[Any] ):
raise NotImplementedError(
'''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
''' `num_decoder_layers`.''' )
| 674 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""XLNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = ["""XLNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLNetForMultipleChoice""",
"""XLNetForQuestionAnswering""",
"""XLNetForQuestionAnsweringSimple""",
"""XLNetForSequenceClassification""",
"""XLNetForTokenClassification""",
"""XLNetLMHeadModel""",
"""XLNetModel""",
"""XLNetPreTrainedModel""",
"""load_tf_weights_in_xlnet""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
"""TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXLNetForMultipleChoice""",
"""TFXLNetForQuestionAnsweringSimple""",
"""TFXLNetForSequenceClassification""",
"""TFXLNetForTokenClassification""",
"""TFXLNetLMHeadModel""",
"""TFXLNetMainLayer""",
"""TFXLNetModel""",
"""TFXLNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 5 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
_lowerCamelCase = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :bool , _lowerCamelCase :str = None , _lowerCamelCase :list = None ):
__SCREAMING_SNAKE_CASE : List[str] = None
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath('''examples''' )
for item in os.listdir(_lowerCamelCase ):
if item not in EXCLUDE_EXAMPLES:
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
if os.path.isfile(_lowerCamelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCamelCase , feature_script=_lowerCamelCase , tested_section='''main()''' if parser_only else '''training_function()''' , ):
__SCREAMING_SNAKE_CASE : Tuple = compare_against_test(
os.path.join(_lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = '''\n'''.join(_lowerCamelCase )
if special_strings is not None:
for string in special_strings:
__SCREAMING_SNAKE_CASE : List[Any] = diff.replace(_lowerCamelCase , '''''' )
self.assertEqual(_lowerCamelCase , '''''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
self.one_complete_example('''complete_nlp_example.py''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
self.one_complete_example('''complete_cv_example.py''' , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
@mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''1'''} )
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = False
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().setUpClass()
__SCREAMING_SNAKE_CASE : Dict = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__SCREAMING_SNAKE_CASE : List[Any] = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls :Dict ):
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : str = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = f'''
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
'''.split()
__SCREAMING_SNAKE_CASE : Optional[int] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
'''.split()
__SCREAMING_SNAKE_CASE : Any = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Optional[int] = f'''
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
'''.split()
__SCREAMING_SNAKE_CASE : List[str] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE : List[Any] = torch.cuda.device_count()
else:
__SCREAMING_SNAKE_CASE : Optional[int] = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
else:
self.assertIn('''epoch 0:''' , _lowerCamelCase )
self.assertIn('''epoch 1:''' , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = run_command(self._launch_args + testargs , return_stdout=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = re.findall('''({.+})''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = [r for r in results if '''accuracy''' in r][-1]
__SCREAMING_SNAKE_CASE : Tuple = ast.literal_eval(_lowerCamelCase )
self.assertGreaterEqual(results['''accuracy'''] , 0.7_5 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with tempfile.TemporaryDirectory() as tmpdir:
__SCREAMING_SNAKE_CASE : int = f'''
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
'''.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCamelCase , '''tracking''' ) ) )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 674 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class UpperCamelCase_ :
def __init__( self :List[Any] , __A :Dict , __A :Tuple=14 , __A :Optional[int]=7 , __A :Any=True , __A :Tuple=True , __A :List[str]=False , __A :int=True , __A :Optional[Any]=99 , __A :Any=32 , __A :Optional[int]=4 , __A :Optional[Any]=4 , __A :Union[str, Any]=4 , __A :Tuple=37 , __A :Tuple="gelu" , __A :Union[str, Any]=0.1 , __A :List[str]=0.1 , __A :Optional[int]=512 , __A :str=0.0_2 , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = seq_length
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_input_mask
SCREAMING_SNAKE_CASE__ = use_token_type_ids
SCREAMING_SNAKE_CASE__ = use_labels
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = rotary_dim
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = vocab_size - 1
SCREAMING_SNAKE_CASE__ = vocab_size - 1
SCREAMING_SNAKE_CASE__ = vocab_size - 1
def _snake_case ( self :str ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=__A , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def _snake_case ( self :int ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = config_and_inputs
SCREAMING_SNAKE_CASE__ = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def _snake_case ( self :int , __A :List[Any] , __A :int , __A :Optional[Any] , __A :int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 20
SCREAMING_SNAKE_CASE__ = model_class_name(__A )
SCREAMING_SNAKE_CASE__ = model.init_cache(input_ids.shape[0] , __A )
SCREAMING_SNAKE_CASE__ = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE__ = model(
input_ids[:, :-1] , attention_mask=__A , past_key_values=__A , position_ids=__A , )
SCREAMING_SNAKE_CASE__ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ = model(
input_ids[:, -1:] , attention_mask=__A , past_key_values=outputs_cache.past_key_values , position_ids=__A , )
SCREAMING_SNAKE_CASE__ = model(__A )
SCREAMING_SNAKE_CASE__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
def _snake_case ( self :Any , __A :str , __A :int , __A :Any , __A :Any ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 20
SCREAMING_SNAKE_CASE__ = model_class_name(__A )
SCREAMING_SNAKE_CASE__ = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
SCREAMING_SNAKE_CASE__ = model.init_cache(input_ids.shape[0] , __A )
SCREAMING_SNAKE_CASE__ = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE__ = model(
input_ids[:, :-1] , attention_mask=__A , past_key_values=__A , position_ids=__A , )
SCREAMING_SNAKE_CASE__ = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=__A , position_ids=__A , )
SCREAMING_SNAKE_CASE__ = model(__A , attention_mask=__A )
SCREAMING_SNAKE_CASE__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class UpperCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase ):
lowerCamelCase_ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
lowerCamelCase_ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def _snake_case ( self :str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = FlaxGPTJModelTester(self )
def _snake_case ( self :int ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(__A , __A , __A , __A )
def _snake_case ( self :int ) -> Optional[int]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
__A , __A , __A , __A )
@tooslow
def _snake_case ( self :str ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = GPTaTokenizer.from_pretrained("""gpt2""" , pad_token="""<|endoftext|>""" , padding_side="""left""" )
SCREAMING_SNAKE_CASE__ = tokenizer(["""Hello this is a long string""", """Hey"""] , return_tensors="""np""" , padding=__A , truncation=__A )
SCREAMING_SNAKE_CASE__ = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = model.config.eos_token_id
SCREAMING_SNAKE_CASE__ = jax.jit(model.generate )
SCREAMING_SNAKE_CASE__ = jit_generate(
inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , pad_token_id=tokenizer.pad_token_id ).sequences
SCREAMING_SNAKE_CASE__ = tokenizer.batch_decode(__A , skip_special_tokens=__A )
SCREAMING_SNAKE_CASE__ = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(__A , __A )
@is_pt_flax_cross_test
def _snake_case ( self :Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(__A , __A )
SCREAMING_SNAKE_CASE__ = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE__ = getattr(__A , __A )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE__ = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__A ):
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = pt_model_class(__A ).eval()
SCREAMING_SNAKE_CASE__ = model_class(__A , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , __A )
SCREAMING_SNAKE_CASE__ = fx_state
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = pt_model(**__A ).to_tuple()
SCREAMING_SNAKE_CASE__ = fx_model(**__A ).to_tuple()
self.assertEqual(len(__A ) , len(__A ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(__A )
SCREAMING_SNAKE_CASE__ = model_class.from_pretrained(__A , from_pt=__A )
SCREAMING_SNAKE_CASE__ = fx_model_loaded(**__A ).to_tuple()
self.assertEqual(
len(__A ) , len(__A ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@is_pt_flax_cross_test
def _snake_case ( self :Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE__ = self._prepare_for_class(__A , __A )
SCREAMING_SNAKE_CASE__ = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE__ = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE__ = getattr(__A , __A )
SCREAMING_SNAKE_CASE__ = pt_model_class(__A ).eval()
SCREAMING_SNAKE_CASE__ = model_class(__A , dtype=jnp.floataa )
SCREAMING_SNAKE_CASE__ = load_flax_weights_in_pytorch_model(__A , fx_model.params )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE__ = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(__A ):
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = pt_model(**__A ).to_tuple()
SCREAMING_SNAKE_CASE__ = fx_model(**__A ).to_tuple()
self.assertEqual(len(__A ) , len(__A ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(__A )
SCREAMING_SNAKE_CASE__ = pt_model_class.from_pretrained(__A , from_flax=__A )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = pt_model_loaded(**__A ).to_tuple()
self.assertEqual(
len(__A ) , len(__A ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(__A , __A ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4E-2 )
@tooslow
def _snake_case ( self :Optional[Any] ) -> str:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE__ = model(np.ones((1, 1) ) )
self.assertIsNotNone(__A ) | 6 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions
import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate
_lowerCamelCase = trt.Logger(trt.Logger.WARNING)
_lowerCamelCase = absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)
_lowerCamelCase = logging.getLogger(__name__)
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--onnx_model_path''',
default=None,
type=str,
required=True,
help='''Path to ONNX model: ''',
)
parser.add_argument(
'''--output_dir''',
default=None,
type=str,
required=True,
help='''The output directory where the model checkpoints and predictions will be written.''',
)
# Other parameters
parser.add_argument(
'''--tokenizer_name''',
default='''''',
type=str,
required=True,
help='''Pretrained tokenizer name or path if not the same as model_name''',
)
parser.add_argument(
'''--version_2_with_negative''',
action='''store_true''',
help='''If true, the SQuAD examples contain some that do not have an answer.''',
)
parser.add_argument(
'''--null_score_diff_threshold''',
type=float,
default=0.0,
help='''If null_score - best_non_null is greater than the threshold predict null.''',
)
parser.add_argument(
'''--max_seq_length''',
default=3_84,
type=int,
help=(
'''The maximum total input sequence length after WordPiece tokenization. Sequences '''
'''longer than this will be truncated, and sequences shorter than this will be padded.'''
),
)
parser.add_argument(
'''--doc_stride''',
default=1_28,
type=int,
help='''When splitting up a long document into chunks, how much stride to take between chunks.''',
)
parser.add_argument('''--per_device_eval_batch_size''', default=8, type=int, help='''Batch size per GPU/CPU for evaluation.''')
parser.add_argument(
'''--n_best_size''',
default=20,
type=int,
help='''The total number of n-best predictions to generate in the nbest_predictions.json output file.''',
)
parser.add_argument(
'''--max_answer_length''',
default=30,
type=int,
help=(
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
),
)
parser.add_argument('''--seed''', type=int, default=42, help='''random seed for initialization''')
parser.add_argument(
'''--dataset_name''',
type=str,
default=None,
required=True,
help='''The name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--dataset_config_name''',
type=str,
default=None,
help='''The configuration name of the dataset to use (via the datasets library).''',
)
parser.add_argument(
'''--preprocessing_num_workers''', type=int, default=4, help='''A csv or a json file containing the training data.'''
)
parser.add_argument('''--overwrite_cache''', action='''store_true''', help='''Overwrite the cached training and evaluation sets''')
parser.add_argument(
'''--fp16''',
action='''store_true''',
help='''Whether to use 16-bit (mixed) precision instead of 32-bit''',
)
parser.add_argument(
'''--int8''',
action='''store_true''',
help='''Whether to use INT8''',
)
_lowerCamelCase = parser.parse_args()
if args.tokenizer_name:
_lowerCamelCase = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
'''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''
)
logger.info('''Training/evaluation parameters %s''', args)
_lowerCamelCase = args.per_device_eval_batch_size
_lowerCamelCase = (args.eval_batch_size, args.max_seq_length)
# TRT Engine properties
_lowerCamelCase = True
_lowerCamelCase = '''temp_engine/bert-fp32.engine'''
if args.fpaa:
_lowerCamelCase = '''temp_engine/bert-fp16.engine'''
if args.inta:
_lowerCamelCase = '''temp_engine/bert-int8.engine'''
# import ONNX file
if not os.path.exists('''temp_engine'''):
os.makedirs('''temp_engine''')
_lowerCamelCase = 1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
network, TRT_LOGGER
) as parser:
with open(args.onnx_model_path, '''rb''') as model:
if not parser.parse(model.read()):
for error in range(parser.num_errors):
print(parser.get_error(error))
# Query input names and shapes from parsed TensorRT network
_lowerCamelCase = [network.get_input(i) for i in range(network.num_inputs)]
_lowerCamelCase = [_input.name for _input in network_inputs] # ex: ["actual_input1"]
with builder.create_builder_config() as config:
_lowerCamelCase = 1 << 50
if STRICT_TYPES:
config.set_flag(trt.BuilderFlag.STRICT_TYPES)
if args.fpaa:
config.set_flag(trt.BuilderFlag.FPaa)
if args.inta:
config.set_flag(trt.BuilderFlag.INTa)
_lowerCamelCase = builder.create_optimization_profile()
config.add_optimization_profile(profile)
for i in range(len(input_names)):
profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
_lowerCamelCase = builder.build_engine(network, config)
# serialize_engine and store in file (can be directly loaded and deserialized):
with open(engine_name, '''wb''') as f:
f.write(engine.serialize())
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Any , lowercase_ : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = np.asarray(inputs['''input_ids'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''attention_mask'''] , dtype=np.intaa )
__SCREAMING_SNAKE_CASE : Optional[Any] = np.asarray(inputs['''token_type_ids'''] , dtype=np.intaa )
# Copy inputs
cuda.memcpy_htod_async(d_inputs[0] , input_ids.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[1] , attention_mask.ravel() , lowercase_ )
cuda.memcpy_htod_async(d_inputs[2] , token_type_ids.ravel() , lowercase_ )
# start time
__SCREAMING_SNAKE_CASE : Tuple = time.time()
# Run inference
context.execute_async(
bindings=[int(lowercase_ ) for d_inp in d_inputs] + [int(lowercase_ ), int(lowercase_ )] , stream_handle=stream.handle )
# Transfer predictions back from GPU
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
cuda.memcpy_dtoh_async(lowercase_ , lowercase_ , lowercase_ )
# Synchronize the stream and take time
stream.synchronize()
# end time
__SCREAMING_SNAKE_CASE : List[str] = time.time()
__SCREAMING_SNAKE_CASE : int = end_time - start_time
__SCREAMING_SNAKE_CASE : int = (h_outputa, h_outputa)
# print(outputs)
return outputs, infer_time
# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCamelCase = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_lowerCamelCase = load_dataset(args.dataset_name, args.dataset_config_name)
else:
raise ValueError('''Evaluation requires a dataset name''')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
_lowerCamelCase = raw_datasets['''validation'''].column_names
_lowerCamelCase = '''question''' if '''question''' in column_names else column_names[0]
_lowerCamelCase = '''context''' if '''context''' in column_names else column_names[1]
_lowerCamelCase = '''answers''' if '''answers''' in column_names else column_names[2]
# Padding side determines if we do (question|context) or (context|question).
_lowerCamelCase = tokenizer.padding_side == '''right'''
if args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'
)
_lowerCamelCase = min(args.max_seq_length, tokenizer.model_max_length)
def lowerCAmelCase_ ( lowercase_ : Tuple ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = [q.lstrip() for q in examples[question_column_name]]
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
__SCREAMING_SNAKE_CASE : Optional[Any] = tokenizer(
examples[question_column_name if pad_on_right else context_column_name] , examples[context_column_name if pad_on_right else question_column_name] , truncation='''only_second''' if pad_on_right else '''only_first''' , max_length=lowercase_ , stride=args.doc_stride , return_overflowing_tokens=lowercase_ , return_offsets_mapping=lowercase_ , padding='''max_length''' , )
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
__SCREAMING_SNAKE_CASE : Optional[int] = tokenized_examples.pop('''overflow_to_sample_mapping''' )
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
__SCREAMING_SNAKE_CASE : Any = []
for i in range(len(tokenized_examples['''input_ids'''] ) ):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
__SCREAMING_SNAKE_CASE : int = tokenized_examples.sequence_ids(lowercase_ )
__SCREAMING_SNAKE_CASE : str = 1 if pad_on_right else 0
# One example can give several spans, this is the index of the example containing this span of text.
__SCREAMING_SNAKE_CASE : str = sample_mapping[i]
tokenized_examples["example_id"].append(examples['''id'''][sample_index] )
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
__SCREAMING_SNAKE_CASE : List[str] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples['''offset_mapping'''][i] )
]
return tokenized_examples
_lowerCamelCase = raw_datasets['''validation''']
# Validation Feature Creation
_lowerCamelCase = eval_examples.map(
prepare_validation_features,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc='''Running tokenizer on validation dataset''',
)
_lowerCamelCase = default_data_collator
_lowerCamelCase = eval_dataset.remove_columns(['''example_id''', '''offset_mapping'''])
_lowerCamelCase = DataLoader(
eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : List[Any]="eval" ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = postprocess_qa_predictions(
examples=lowercase_ , features=lowercase_ , predictions=lowercase_ , version_2_with_negative=args.version_2_with_negative , n_best_size=args.n_best_size , max_answer_length=args.max_answer_length , null_score_diff_threshold=args.null_score_diff_threshold , output_dir=args.output_dir , prefix=lowercase_ , )
# Format the result to the format the metric expects.
if args.version_2_with_negative:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
]
else:
__SCREAMING_SNAKE_CASE : int = [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]
__SCREAMING_SNAKE_CASE : Any = [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=lowercase_ , label_ids=lowercase_ )
_lowerCamelCase = load_metric('''squad_v2''' if args.version_2_with_negative else '''squad''')
# Evaluation!
logger.info('''Loading ONNX model %s for evaluation''', args.onnx_model_path)
with open(engine_name, '''rb''') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
f.read()
) as engine, engine.create_execution_context() as context:
# setup for TRT inferrence
for i in range(len(input_names)):
context.set_binding_shape(i, INPUT_SHAPE)
assert context.all_binding_shapes_specified
def lowerCAmelCase_ ( lowercase_ : Any ):
'''simple docstring'''
return trt.volume(engine.get_binding_shape(lowercase_ ) ) * engine.get_binding_dtype(lowercase_ ).itemsize
# Allocate device memory for inputs and outputs.
_lowerCamelCase = [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]
# Allocate output buffer
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
_lowerCamelCase = cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
_lowerCamelCase = cuda.mem_alloc(h_outputa.nbytes)
# Create a stream in which to copy inputs/outputs and run inference.
_lowerCamelCase = cuda.Stream()
# Evaluation
logger.info('''***** Running Evaluation *****''')
logger.info(f' Num examples = {len(eval_dataset)}')
logger.info(f' Batch size = {args.per_device_eval_batch_size}')
_lowerCamelCase = 0.0
_lowerCamelCase = 0
_lowerCamelCase = timeit.default_timer()
_lowerCamelCase = None
for step, batch in enumerate(eval_dataloader):
_lowerCamelCase , _lowerCamelCase = model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
total_time += infer_time
niter += 1
_lowerCamelCase , _lowerCamelCase = outputs
_lowerCamelCase = torch.tensor(start_logits)
_lowerCamelCase = torch.tensor(end_logits)
# necessary to pad predictions and labels for being gathered
_lowerCamelCase = accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)
_lowerCamelCase = (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
_lowerCamelCase = logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)
if all_preds is not None:
_lowerCamelCase = nested_truncate(all_preds, len(eval_dataset))
_lowerCamelCase = timeit.default_timer() - start_time
logger.info(''' Evaluation done in total %f secs (%f sec per example)''', evalTime, evalTime / len(eval_dataset))
# Inference time from TRT
logger.info('''Average Inference Time = {:.3f} ms'''.format(total_time * 10_00 / niter))
logger.info('''Total Inference Time = {:.3f} ms'''.format(total_time * 10_00))
logger.info('''Total Number of Inference = %d''', niter)
_lowerCamelCase = post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCamelCase = metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'Evaluation metrics: {eval_metric}')
| 674 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a = logging.get_logger(__name__)
def _snake_case ( _snake_case : List[Any] , _snake_case : Dict=False , _snake_case : Optional[int]=False , _snake_case : str=False ) -> Optional[Any]:
'''simple docstring'''
_A = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''transformer.blocks.{i}.norm1.weight''', F'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm1.bias''', F'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.weight''', F'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(F'''transformer.blocks.{i}.attn.proj.bias''', F'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.weight''', F'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.norm2.bias''', F'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(F'''transformer.blocks.{i}.mlp.fc1.weight''', F'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc1.bias''', F'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.weight''', F'''vilt.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.bias''', F'''vilt.encoder.layer.{i}.output.dense.bias''') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def _snake_case ( _snake_case : int , _snake_case : int ) -> Any:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
_A = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_A = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight''' )
_A = state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
_A = in_proj_weight[
: config.hidden_size, :
]
_A = in_proj_bias[: config.hidden_size]
_A = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_A = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_A = in_proj_weight[
-config.hidden_size :, :
]
_A = in_proj_bias[-config.hidden_size :]
def _snake_case ( _snake_case : Tuple ) -> str:
'''simple docstring'''
_A = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
def _snake_case ( _snake_case : Dict , _snake_case : str , _snake_case : Tuple ) -> str:
'''simple docstring'''
_A = dct.pop(_snake_case )
_A = val
@torch.no_grad()
def _snake_case ( _snake_case : List[str] , _snake_case : Dict ) -> List[str]:
'''simple docstring'''
_A = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=_snake_case )
_A = False
_A = False
_A = False
_A = False
if "vqa" in checkpoint_url:
_A = True
_A = 31_29
_A = 'huggingface/label-files'
_A = 'vqa2-id2label.json'
_A = json.load(open(hf_hub_download(_snake_case , _snake_case , repo_type='dataset' ) , 'r' ) )
_A = {int(_snake_case ): v for k, v in idalabel.items()}
_A = idalabel
_A = {v: k for k, v in idalabel.items()}
_A = ViltForQuestionAnswering(_snake_case )
elif "nlvr" in checkpoint_url:
_A = True
_A = 2
_A = {0: 'False', 1: 'True'}
_A = {v: k for k, v in config.idalabel.items()}
_A = 3
_A = ViltForImagesAndTextClassification(_snake_case )
elif "irtr" in checkpoint_url:
_A = True
_A = ViltForImageAndTextRetrieval(_snake_case )
elif "mlm_itm" in checkpoint_url:
_A = True
_A = ViltForMaskedLM(_snake_case )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
_A = torch.hub.load_state_dict_from_url(_snake_case , map_location='cpu' )['state_dict']
_A = create_rename_keys(_snake_case , _snake_case , _snake_case , _snake_case )
for src, dest in rename_keys:
rename_key(_snake_case , _snake_case , _snake_case )
read_in_q_k_v(_snake_case , _snake_case )
if mlm_model or irtr_model:
_A = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(_snake_case , _snake_case )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
_A , _A = model.load_state_dict(_snake_case , strict=_snake_case )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(_snake_case )
# Define processor
_A = ViltImageProcessor(size=3_84 )
_A = BertTokenizer.from_pretrained('bert-base-uncased' )
_A = ViltProcessor(_snake_case , _snake_case )
# Forward pass on example inputs (image + text)
if nlvr_model:
_A = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=_snake_case ).raw )
_A = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=_snake_case ).raw )
_A = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
_A = processor(_snake_case , _snake_case , return_tensors='pt' )
_A = processor(_snake_case , _snake_case , return_tensors='pt' )
_A = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
_A = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=_snake_case ).raw )
if mlm_model:
_A = 'a bunch of [MASK] laying on a [MASK].'
else:
_A = 'How many cats are there?'
_A = processor(_snake_case , _snake_case , return_tensors='pt' )
_A = model(**_snake_case )
# Verify outputs
if mlm_model:
_A = torch.Size([1, 11, 3_05_22] )
_A = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , _snake_case , atol=1E-4 )
# verify masked token prediction equals "cats"
_A = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
_A = torch.Size([1, 31_29] )
_A = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] , _snake_case , atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , _snake_case , atol=1E-4 )
# verify vqa prediction equals "2"
_A = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
_A = torch.Size([1, 2] )
_A = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] , _snake_case , atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(_snake_case ).mkdir(exist_ok=_snake_case )
print(F'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(_snake_case )
processor.save_pretrained(_snake_case )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
a = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 7 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class snake_case ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = TFAutoModel.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModel.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForPreTraining.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Any ):
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Optional[int] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = TFAutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForCausalLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Any = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = TFAutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = TFAutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelForMaskedLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = AutoModelForMaskedLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : str = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : str = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = AutoModelForSeqaSeqLM.from_pretrained(
_lowerCamelCase , output_loading_info=_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = TFAutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["bert-base-uncased"]:
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = TFAutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = AutoModelForQuestionAnswering.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Tuple = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Tuple = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_pt=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoModelWithLMHead.from_pretrained(_lowerCamelCase , from_tf=_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
self.assertEqual(model.num_parameters() , 1_4_4_1_0 )
self.assertEqual(model.num_parameters(only_trainable=_lowerCamelCase ) , 1_4_4_1_0 )
| 674 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( __snake_case : float , __snake_case : int ) -> float:
__A : int = u
for i in range(1 , __snake_case ):
__A : Optional[int] = temp * (u - i)
return temp
def _lowerCAmelCase ( ) -> None:
__A : Dict = int(input('enter the numbers of values: ' ) )
__A : list[list[float]] = []
for _ in range(__snake_case ):
y.append([] )
for i in range(__snake_case ):
for j in range(__snake_case ):
y[i].append(__snake_case )
__A : int = 0
print('enter the values of parameters in a list: ' )
__A : List[str] = list(map(__snake_case , input().split() ) )
print('enter the values of corresponding parameters: ' )
for i in range(__snake_case ):
__A : Tuple = float(input() )
__A : Tuple = int(input('enter the value to interpolate: ' ) )
__A : Dict = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , __snake_case ):
for j in range(n - i ):
__A : Dict = y[j + 1][i - 1] - y[j][i - 1]
__A : List[Any] = y[0][0]
for i in range(1 , __snake_case ):
summ += (ucal(__snake_case , __snake_case ) * y[0][i]) / math.factorial(__snake_case )
print(f'the value at {value} is {summ}' )
if __name__ == "__main__":
main() | 8 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
_lowerCamelCase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
_lowerCamelCase = {
'''t5-small''': 5_12,
'''t5-base''': 5_12,
'''t5-large''': 5_12,
'''t5-3b''': 5_12,
'''t5-11b''': 5_12,
}
_lowerCamelCase = '''▁'''
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['''input_ids''', '''attention_mask''']
def __init__( self :int , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Union[str, Any]="</s>" , _lowerCamelCase :List[Any]="<unk>" , _lowerCamelCase :Union[str, Any]="<pad>" , _lowerCamelCase :int=1_0_0 , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :Optional[Dict[str, Any]] = None , _lowerCamelCase :int=True , **_lowerCamelCase :List[Any] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [f'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Optional[int] = len(set(filter(lambda _lowerCamelCase : bool('''extra_id''' in str(_lowerCamelCase ) ) , _lowerCamelCase ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
''' tokens''' )
if legacy:
logger.warning_once(
f'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = legacy
__SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , extra_ids=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , legacy=_lowerCamelCase , **_lowerCamelCase , )
__SCREAMING_SNAKE_CASE : Tuple = vocab_file
__SCREAMING_SNAKE_CASE : List[str] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _lowerCamelCase :str , _lowerCamelCase :Union[str, Any] , _lowerCamelCase :int ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Any = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
'''This tokenizer was incorrectly instantiated with a model max length of'''
f''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
f''' {pretrained_model_name_or_path} automatically truncating your input to'''
f''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
f''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' , _lowerCamelCase , )
return max_model_length
@property
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
return self.sp_model.get_piece_size() + self._extra_ids
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : str = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(_lowerCamelCase )) + [1]
return ([0] * len(_lowerCamelCase )) + [1] + ([0] * len(_lowerCamelCase )) + [1]
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
return list(
set(filter(lambda _lowerCamelCase : bool(re.search(r'''<extra_id_\d+>''' , _lowerCamelCase ) ) is not None , self.additional_special_tokens ) ) )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return [self._convert_token_to_id(_lowerCamelCase ) for token in self.get_sentinel_tokens()]
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :List[int] ):
if len(_lowerCamelCase ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
''' eos tokens being added.''' )
return token_ids
else:
return token_ids + [self.eos_token_id]
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def SCREAMING_SNAKE_CASE_ ( self :List[Any] , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ):
__SCREAMING_SNAKE_CASE : Optional[Any] = self._add_eos_if_not_present(_lowerCamelCase )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = self._add_eos_if_not_present(_lowerCamelCase )
return token_ids_a + token_ids_a
def __getstate__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self :Optional[Any] , _lowerCamelCase :List[str] ):
__SCREAMING_SNAKE_CASE : Tuple = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = {}
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :"TextInput" , **_lowerCamelCase :str ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : Dict = SPIECE_UNDERLINE + text.replace(_lowerCamelCase , ''' ''' )
return super().tokenize(_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :List[Any] , **_lowerCamelCase :Dict ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = text.startswith(_lowerCamelCase )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : Tuple = self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
if not self.legacy and not is_first and not text.startswith(''' ''' ) and tokens[0].startswith(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[Any] ):
if token.startswith('''<extra_id_''' ):
__SCREAMING_SNAKE_CASE : Tuple = re.match(r'''<extra_id_(\d+)>''' , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : List[Any] = self.sp_model.IdToPiece(_lowerCamelCase )
else:
__SCREAMING_SNAKE_CASE : Dict = f'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : str = []
__SCREAMING_SNAKE_CASE : Dict = ''''''
__SCREAMING_SNAKE_CASE : Dict = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_lowerCamelCase ) + token
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : str = []
else:
current_sub_tokens.append(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = False
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ):
if not os.path.isdir(_lowerCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
__SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 674 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _a ( _snake_case : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
def _a ( self : Union[str, Any] ):
"""simple docstring"""
raise NotImplementedError()
| 9 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' , [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_infos.json'''],
['''dataset_infos.json'''],
['''full:README.md'''],
] , )
def lowerCAmelCase_ ( lowercase_ : List[str] , lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = tmp_path_factory.mktemp('''dset_infos_dir''' )
if "full:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''---\ndataset_info:\n dataset_size: 42\n---''' )
if "empty:README.md" in files:
with open(dataset_infos_dir / '''README.md''' , '''w''' ) as f:
f.write('''''' )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / '''dataset_infos.json''' , '''w''' ) as f:
f.write('''{"default": {"dataset_size": 42}}''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
'''dataset_info''' , [
DatasetInfo(),
DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , ),
] , )
def lowerCAmelCase_ ( lowercase_ : Any , lowercase_ : DatasetInfo ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[str] = str(lowercase_ )
dataset_info.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Dict = DatasetInfo.from_directory(lowercase_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowercase_ , '''dataset_info.json''' ) )
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo(
description='''foo''' , citation='''bar''' , homepage='''https://foo.bar''' , license='''CC0''' , features=Features({'''a''': Value('''int32''' )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train''', '''num_examples''': 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
__SCREAMING_SNAKE_CASE : Optional[int] = dataset_info._to_yaml_dict()
assert sorted(lowercase_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
__SCREAMING_SNAKE_CASE : int = yaml.safe_dump(lowercase_ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.safe_load(lowercase_ )
assert dataset_info_yaml_dict == reloaded
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = DatasetInfo()
__SCREAMING_SNAKE_CASE : Union[str, Any] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
'''dataset_infos_dict''' , [
DatasetInfosDict(),
DatasetInfosDict({'''default''': DatasetInfo()} ),
DatasetInfosDict({'''my_config_name''': DatasetInfo()} ),
DatasetInfosDict(
{
'''default''': DatasetInfo(
description='''foo''' , features=Features({'''a''': Value('''int32''' )} ) , builder_name='''builder''' , config_name='''config''' , version='''1.0.0''' , splits=[{'''name''': '''train'''}] , download_size=42 , )
} ),
DatasetInfosDict(
{
'''v1''': DatasetInfo(dataset_size=42 ),
'''v2''': DatasetInfo(dataset_size=1337 ),
} ),
] , )
def lowerCAmelCase_ ( lowercase_ : List[Any] , lowercase_ : DatasetInfosDict ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
dataset_infos_dict.write_to_directory(lowercase_ )
__SCREAMING_SNAKE_CASE : Optional[Any] = DatasetInfosDict.from_directory(lowercase_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
__SCREAMING_SNAKE_CASE : Optional[int] = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
__SCREAMING_SNAKE_CASE : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowercase_ , '''README.md''' ) )
| 674 | 0 |
from __future__ import annotations
_lowerCAmelCase = 8.988E9 # units = N * m^s * C^-2
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if distance < 0:
raise ValueError('''Distance cannot be negative''' )
if force == 0:
_UpperCamelCase = COULOMBS_CONSTANT * charge_product / (distance**2)
return {"force": force}
elif chargea == 0:
_UpperCamelCase = abs(__snake_case ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge1": chargea}
elif chargea == 0:
_UpperCamelCase = abs(__snake_case ) * (distance**2) / (COULOMBS_CONSTANT * chargea)
return {"charge2": chargea}
elif distance == 0:
_UpperCamelCase = (COULOMBS_CONSTANT * charge_product / abs(__snake_case )) ** 0.5
return {"distance": distance}
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 10 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
@register_to_config
def __init__( self :List[str] , _lowerCamelCase :int = 7_6_8 , ):
super().__init__()
__SCREAMING_SNAKE_CASE : Union[str, Any] = nn.Parameter(torch.zeros(1 , _lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Parameter(torch.ones(1 , _lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any , _lowerCamelCase :Optional[Union[str, torch.device]] = None , _lowerCamelCase :Optional[torch.dtype] = None , ):
__SCREAMING_SNAKE_CASE : Optional[int] = nn.Parameter(self.mean.to(_lowerCamelCase ).to(_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Tuple = nn.Parameter(self.std.to(_lowerCamelCase ).to(_lowerCamelCase ) )
return self
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Tuple = (embeds - self.mean) * 1.0 / self.std
return embeds
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = (embeds * self.std) + self.mean
return embeds
| 674 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A , __A = None , __A = None):
"""simple docstring"""
if start is None:
_a = 0
if end is None:
_a = len(__A) - 1
if start >= end:
return
_a = (start + end) // 2
slowsort(__A , __A , __A)
slowsort(__A , mid + 1 , __A)
if sequence[end] < sequence[mid]:
_a , _a = sequence[mid], sequence[end]
slowsort(__A , __A , end - 1)
if __name__ == "__main__":
from doctest import testmod
testmod()
| 11 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( lowercase_ : Dict , lowercase_ : int , lowercase_ : Any ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_json_file(lowercase_ )
print(F'''Building PyTorch model from configuration: {config}''' )
__SCREAMING_SNAKE_CASE : Optional[int] = BertForPreTraining(lowercase_ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(lowercase_ , lowercase_ , lowercase_ )
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , lowercase_ )
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
_lowerCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 674 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class _snake_case ( UpperCAmelCase_ , unittest.TestCase ):
__lowerCAmelCase : Dict = FlaxAutoencoderKL
@property
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : str = 4
lowercase__ : str = 3
lowercase__ : Union[str, Any] = (32, 32)
lowercase__ : Optional[Any] = jax.random.PRNGKey(0)
lowercase__ : int = jax.random.uniform(SCREAMING_SNAKE_CASE_ , ((batch_size, num_channels) + sizes))
return {"sample": image, "prng_key": prng_key}
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
lowercase__ : List[str] = self.dummy_input
return init_dict, inputs_dict
| 12 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
lowerCamelCase__ = '''CIDAS/clipseg-rd64-refined'''
lowerCamelCase__ = '''image_segmenter'''
lowerCamelCase__ = CLIPSegForImageSegmentation
lowerCamelCase__ = ['''image''', '''text''']
lowerCamelCase__ = ['''image''']
def __init__( self :Dict , *_lowerCamelCase :Union[str, Any] , **_lowerCamelCase :Tuple ):
requires_backends(self , ['''vision'''] )
super().__init__(*_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :"Image" , _lowerCamelCase :str ):
return self.pre_processor(text=[label] , images=[image] , padding=_lowerCamelCase , return_tensors='''pt''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] , _lowerCamelCase :Optional[int] ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = self.model(**_lowerCamelCase ).logits
return logits
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Tuple ):
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.cpu().detach().numpy()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : str = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 674 | 0 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCAmelCase__ ( UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : int ) -> List[Any]:
# ===== initialization =====
__lowerCamelCase : Dict = Mock()
__lowerCamelCase : Union[str, Any] = conn, Mock()
__lowerCamelCase : Dict = iter([1, None] )
__lowerCamelCase : Tuple = lambda UpperCAmelCase_ : next(UpperCAmelCase_ )
# ===== invoke =====
send_file(filename='mytext.txt' , testing=UpperCAmelCase_ )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 13 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
# fmt: on
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
__SCREAMING_SNAKE_CASE : Optional[int] = {
'''do_resize''': True,
'''size''': {'''height''': 1_8, '''width''': 1_8},
'''do_normalize''': True,
'''image_mean''': [0.5, 0.5, 0.5],
'''image_std''': [0.5, 0.5, 0.5],
}
__SCREAMING_SNAKE_CASE : List[Any] = os.path.join(self.tmpdirname , _lowerCamelCase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] , **_lowerCamelCase :List[str] ):
return BertTokenizer.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] , **_lowerCamelCase :Optional[int] ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )]
__SCREAMING_SNAKE_CASE : Tuple = [Image.fromarray(np.moveaxis(_lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Optional[Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : str = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=_lowerCamelCase , padding_value=1.0 )
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=_lowerCamelCase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , (BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : Optional[int] = image_processor(_lowerCamelCase , return_tensors='''np''' )
__SCREAMING_SNAKE_CASE : Tuple = processor(images=_lowerCamelCase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : Any = self.get_image_processor()
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Tuple = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Optional[int] = processor(text=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = tokenizer(_lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : int = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : int = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = '''lower newer'''
__SCREAMING_SNAKE_CASE : int = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : int = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with self.assertRaises(_lowerCamelCase ):
processor()
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Union[str, Any] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE : Tuple = processor.batch_decode(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.batch_decode(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : str = self.get_image_processor()
__SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
__SCREAMING_SNAKE_CASE : Optional[int] = VisionTextDualEncoderProcessor(tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = '''lower newer'''
__SCREAMING_SNAKE_CASE : Dict = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE : str = processor(text=_lowerCamelCase , images=_lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 674 | 0 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
a__ = [
'''cross_validation.py''',
'''gradient_accumulation.py''',
'''local_sgd.py''',
'''multi_process_metrics.py''',
'''memory.py''',
'''automatic_gradient_accumulation.py''',
'''fsdp_with_peak_mem_tracking.py''',
'''deepspeed_with_config_support.py''',
'''megatron_lm_gpt_pretraining.py''',
]
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self , _a , _a , _a = None , _a = None ) -> List[Any]:
_a : Union[str, Any] = None
_a : Optional[Any] = os.path.abspath(os.path.join('''examples''' , '''by_feature''' ) )
_a : Optional[int] = os.path.abspath('''examples''' )
for item in os.listdir(_a ):
if item not in EXCLUDE_EXAMPLES:
_a : Any = os.path.join(_a , _a )
if os.path.isfile(_a ) and ".py" in item_path:
with self.subTest(
tested_script=_a , feature_script=_a , tested_section='''main()''' if parser_only else '''training_function()''' , ):
_a : Optional[int] = compare_against_test(
os.path.join(_a , _a ) , _a , _a , _a )
_a : Union[str, Any] = '''\n'''.join(_a )
if special_strings is not None:
for string in special_strings:
_a : Union[str, Any] = diff.replace(_a , '''''' )
self.assertEqual(_a , '''''' )
def __lowercase ( self ) -> Optional[Any]:
self.one_complete_example('''complete_nlp_example.py''' , _a )
self.one_complete_example('''complete_nlp_example.py''' , _a )
def __lowercase ( self ) -> Union[str, Any]:
_a : Optional[int] = os.path.abspath(os.path.join('''examples''' , '''cv_example.py''' ) )
_a : int = [
''' ''' * 1_6 + '''{\n\n''',
''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
''' ''' * 1_6 + '''},\n\n''',
''' ''' * 1_6 + '''step=epoch,\n''',
''' ''' * 1_2,
''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
]
self.one_complete_example('''complete_cv_example.py''' , _a , _a , _a )
self.one_complete_example('''complete_cv_example.py''' , _a , _a , _a )
@mock.patch.dict(os.environ , {"TESTING_MOCKED_DATALOADERS": "1"} )
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Any = False
@classmethod
def __lowercase ( cls ) -> List[Any]:
super().setUpClass()
_a : str = tempfile.mkdtemp()
_a : str = os.path.join(cls._tmpdir , '''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
_a : int = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def __lowercase ( cls ) -> Optional[int]:
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def __lowercase ( self ) -> Dict:
_a : Union[str, Any] = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps epoch
--output_dir {self.tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''epoch_0''' ) ) )
def __lowercase ( self ) -> List[str]:
_a : Union[str, Any] = F"""
examples/by_feature/checkpointing.py
--checkpointing_steps 1
--output_dir {self.tmpdir}
""".split()
_a : List[str] = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , '''step_2''' ) ) )
def __lowercase ( self ) -> Any:
_a : Dict = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}
""".split()
_a : str = run_command(self._launch_args + testargs , return_stdout=_a )
self.assertNotIn('''epoch 0:''' , _a )
self.assertIn('''epoch 1:''' , _a )
def __lowercase ( self ) -> Dict:
_a : Optional[Any] = F"""
examples/by_feature/checkpointing.py
--resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}
""".split()
_a : Optional[int] = run_command(self._launch_args + testargs , return_stdout=_a )
if torch.cuda.is_available():
_a : List[Any] = torch.cuda.device_count()
else:
_a : Tuple = 1
if num_processes > 1:
self.assertNotIn('''epoch 0:''' , _a )
self.assertIn('''epoch 1:''' , _a )
else:
self.assertIn('''epoch 0:''' , _a )
self.assertIn('''epoch 1:''' , _a )
@slow
def __lowercase ( self ) -> Union[str, Any]:
_a : List[str] = '''
examples/by_feature/cross_validation.py
--num_folds 2
'''.split()
with mock.patch.dict(os.environ , {'''TESTING_MOCKED_DATALOADERS''': '''0'''} ):
_a : Tuple = run_command(self._launch_args + testargs , return_stdout=_a )
_a : int = re.findall('''({.+})''' , _a )
_a : int = [r for r in results if '''accuracy''' in r][-1]
_a : Optional[Any] = ast.literal_eval(_a )
self.assertGreaterEqual(results['''accuracy'''] , 0.75 )
def __lowercase ( self ) -> str:
_a : Optional[int] = ['''examples/by_feature/multi_process_metrics.py''']
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {'''WANDB_MODE''': '''offline'''} )
def __lowercase ( self ) -> Union[str, Any]:
with tempfile.TemporaryDirectory() as tmpdir:
_a : str = F"""
examples/by_feature/tracking.py
--with_tracking
--project_dir {tmpdir}
""".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_a , '''tracking''' ) ) )
def __lowercase ( self ) -> Optional[int]:
_a : List[str] = ['''examples/by_feature/gradient_accumulation.py''']
run_command(self._launch_args + testargs )
def __lowercase ( self ) -> List[Any]:
_a : Union[str, Any] = ['''examples/by_feature/local_sgd.py''']
run_command(self._launch_args + testargs )
| 14 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class snake_case :
def __init__( self :Optional[int] , _lowerCamelCase :int , _lowerCamelCase :Any=2 , _lowerCamelCase :List[str]=True , _lowerCamelCase :str=False , _lowerCamelCase :Tuple=1_0 , _lowerCamelCase :str=3 , _lowerCamelCase :str=3_2 * 4 , _lowerCamelCase :Dict=3_2 * 6 , _lowerCamelCase :str=4 , _lowerCamelCase :Any=3_2 , ):
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Tuple = batch_size
__SCREAMING_SNAKE_CASE : Optional[Any] = is_training
__SCREAMING_SNAKE_CASE : Dict = use_auxiliary_loss
__SCREAMING_SNAKE_CASE : List[str] = num_queries
__SCREAMING_SNAKE_CASE : Optional[int] = num_channels
__SCREAMING_SNAKE_CASE : List[Any] = min_size
__SCREAMING_SNAKE_CASE : int = max_size
__SCREAMING_SNAKE_CASE : Any = num_labels
__SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_size
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : str = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCamelCase ) > 0.5
).float()
__SCREAMING_SNAKE_CASE : Dict = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCamelCase ) > 0.5).long()
__SCREAMING_SNAKE_CASE : str = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=1_2_8 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_ ( self :str ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Any = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self :int , _lowerCamelCase :int , _lowerCamelCase :Optional[int] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = output.encoder_hidden_states
__SCREAMING_SNAKE_CASE : int = output.pixel_decoder_hidden_states
__SCREAMING_SNAKE_CASE : Optional[int] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCamelCase ) , config.decoder_config.decoder_layers )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] , _lowerCamelCase :Dict , _lowerCamelCase :str , _lowerCamelCase :Any , _lowerCamelCase :Optional[Any]=False ):
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = MaskFormerModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
__SCREAMING_SNAKE_CASE : str = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[Any] , _lowerCamelCase :Optional[int] , _lowerCamelCase :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerForInstanceSegmentation(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
def comm_check_on_output(_lowerCamelCase :Optional[Any] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[str] = model(pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = model(_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = model(
pixel_values=_lowerCamelCase , pixel_mask=_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
comm_check_on_output(_lowerCamelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class snake_case ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
lowerCamelCase__ = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
__SCREAMING_SNAKE_CASE : Optional[Any] = MaskFormerModelTester(self )
__SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
__SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCamelCase )
@unittest.skip(reason='''MaskFormer does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
pass
@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method''' )
def SCREAMING_SNAKE_CASE_ ( self :int ):
pass
@unittest.skip(reason='''MaskFormer is not a generative model''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
pass
@unittest.skip(reason='''MaskFormer does not use token embeddings''' )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
pass
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Tuple = model_class(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__SCREAMING_SNAKE_CASE : List[str] = [*signature.parameters.keys()]
__SCREAMING_SNAKE_CASE : Optional[Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
for model_name in ["facebook/maskformer-swin-small-coco"]:
__SCREAMING_SNAKE_CASE : Tuple = MaskFormerModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : Dict = (self.model_tester.min_size,) * 2
__SCREAMING_SNAKE_CASE : Dict = {
'''pixel_values''': torch.randn((2, 3, *size) , device=_lowerCamelCase ),
'''mask_labels''': torch.randn((2, 1_0, *size) , device=_lowerCamelCase ),
'''class_labels''': torch.zeros(2 , 1_0 , device=_lowerCamelCase ).long(),
}
__SCREAMING_SNAKE_CASE : Dict = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCamelCase , **_lowerCamelCase , output_hidden_states=_lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : Dict = model_class(_lowerCamelCase ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Optional[int] = model(**_lowerCamelCase , output_attentions=_lowerCamelCase )
self.assertTrue(outputs.attentions is not None )
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : List[Any] = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : int = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Any = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# only MaskFormerForInstanceSegmentation has the loss
__SCREAMING_SNAKE_CASE : Tuple = self.all_model_classes[1]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
__SCREAMING_SNAKE_CASE : Tuple = True
__SCREAMING_SNAKE_CASE : List[str] = True
__SCREAMING_SNAKE_CASE : Any = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
__SCREAMING_SNAKE_CASE : Optional[int] = model(_lowerCamelCase , mask_labels=_lowerCamelCase , class_labels=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : str = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__SCREAMING_SNAKE_CASE : Optional[int] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__SCREAMING_SNAKE_CASE : int = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCamelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowerCamelCase = 1e-4
def lowerCAmelCase_ ( ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : List[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self :str ):
return (
MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco''' )
if is_vision_available()
else None
)
def SCREAMING_SNAKE_CASE_ ( self :List[Any] ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : Tuple = model(**_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor(
[[-0.0_4_8_2, 0.9_2_2_8, 0.4_9_5_1], [-0.2_5_4_7, 0.8_0_1_7, 0.8_5_2_7], [-0.0_0_6_9, 0.3_3_8_5, -0.0_0_8_9]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Any = torch.tensor(
[[-0.8_4_2_2, -0.8_4_3_4, -0.9_7_1_8], [-1.0_1_4_4, -0.5_5_6_5, -0.4_1_9_5], [-1.0_0_3_8, -0.4_4_8_4, -0.1_9_6_1]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor(
[[0.2_8_5_2, -0.0_1_5_9, 0.9_7_3_5], [0.6_2_5_4, 0.1_8_5_8, 0.8_5_2_9], [-0.0_6_8_0, -0.4_1_1_6, 1.8_4_1_3]] ).to(_lowerCamelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.default_image_processor
__SCREAMING_SNAKE_CASE : Tuple = prepare_img()
__SCREAMING_SNAKE_CASE : str = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : str = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Any = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[Any] = [
[-1.3_7_3_7_1_2_4, -1.7_7_2_4_9_3_7, -1.9_3_6_4_2_3_3],
[-1.5_9_7_7_2_8_1, -1.9_8_6_7_9_3_9, -2.1_5_2_3_6_9_5],
[-1.5_7_9_5_3_9_8, -1.9_2_6_9_8_3_2, -2.0_9_3_9_4_2],
]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : List[Any] = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[
[1.65_12e00, -5.25_72e00, -3.35_19e00],
[3.61_69e-02, -5.90_25e00, -2.93_13e00],
[1.07_66e-04, -7.76_30e00, -5.12_63e00],
] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Tuple = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Tuple = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : List[Any] = image_processor(_lowerCamelCase , return_tensors='''pt''' ).to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[Any] = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0 )
# check size
self.assertEqual(_lowerCamelCase , (1, 3, 8_0_0, 1_0_8_8) )
with torch.no_grad():
__SCREAMING_SNAKE_CASE : List[Any] = model(**_lowerCamelCase )
# masks_queries_logits
__SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__SCREAMING_SNAKE_CASE : List[str] = [[-0.9_0_4_6, -2.6_3_6_6, -4.6_0_6_2], [-3.4_1_7_9, -5.7_8_9_0, -8.8_0_5_7], [-4.9_1_7_9, -7.6_5_6_0, -1_0.7_7_1_1]]
__SCREAMING_SNAKE_CASE : Any = torch.tensor(_lowerCamelCase ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
# class_queries_logits
__SCREAMING_SNAKE_CASE : int = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[4.7_1_8_8, -3.2_5_8_5, -2.8_8_5_7], [6.6_8_7_1, -2.9_1_8_1, -1.2_4_8_7], [7.2_4_4_9, -2.2_7_6_4, -2.1_8_7_4]] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCamelCase , atol=_lowerCamelCase ) )
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : int = (
MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco''' )
.to(_lowerCamelCase )
.eval()
)
__SCREAMING_SNAKE_CASE : Any = self.default_image_processor
__SCREAMING_SNAKE_CASE : int = image_processor(
[np.zeros((3, 8_0_0, 1_3_3_3) ), np.zeros((3, 8_0_0, 1_3_3_3) )] , segmentation_maps=[np.zeros((3_8_4, 3_8_4) ).astype(np.floataa ), np.zeros((3_8_4, 3_8_4) ).astype(np.floataa )] , return_tensors='''pt''' , )
__SCREAMING_SNAKE_CASE : Dict = inputs['''pixel_values'''].to(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Dict = [el.to(_lowerCamelCase ) for el in inputs['''mask_labels''']]
__SCREAMING_SNAKE_CASE : str = [el.to(_lowerCamelCase ) for el in inputs['''class_labels''']]
with torch.no_grad():
__SCREAMING_SNAKE_CASE : int = model(**_lowerCamelCase )
self.assertTrue(outputs.loss is not None )
| 674 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
A : Optional[int] = logging.get_logger(__name__)
def UpperCamelCase ( __magic_name__ : np.ndarray , __magic_name__ : Union[int, Iterable[int]] , __magic_name__ : bool , __magic_name__ : int ) -> Tuple[int, int]:
"""simple docstring"""
def constraint_to_multiple_of(__magic_name__ : Any , __magic_name__ : Any , __magic_name__ : Dict=0 , __magic_name__ : Dict=None ):
lowercase__ = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
lowercase__ = math.floor(val / multiple ) * multiple
if x < min_val:
lowercase__ = math.ceil(val / multiple ) * multiple
return x
lowercase__ = (output_size, output_size) if isinstance(__magic_name__ , __magic_name__ ) else output_size
lowercase__ , lowercase__ = get_image_size(__magic_name__ )
lowercase__ , lowercase__ = output_size
# determine new height and width
lowercase__ = output_height / input_height
lowercase__ = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
lowercase__ = scale_width
else:
# fit height
lowercase__ = scale_height
lowercase__ = constraint_to_multiple_of(scale_height * input_height , multiple=__magic_name__ )
lowercase__ = constraint_to_multiple_of(scale_width * input_width , multiple=__magic_name__ )
return (new_height, new_width)
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = ['''pixel_values''']
def __init__(self : Any , _UpperCAmelCase : bool = True , _UpperCAmelCase : Dict[str, int] = None , _UpperCAmelCase : PILImageResampling = PILImageResampling.BILINEAR , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 1 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Union[int, float] = 1 / 255 , _UpperCAmelCase : bool = True , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , **_UpperCAmelCase : Tuple , ) -> None:
"""simple docstring"""
super().__init__(**_UpperCAmelCase )
lowercase__ = size if size is not None else {"""height""": 384, """width""": 384}
lowercase__ = get_size_dict(_UpperCAmelCase )
lowercase__ = do_resize
lowercase__ = size
lowercase__ = keep_aspect_ratio
lowercase__ = ensure_multiple_of
lowercase__ = resample
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_normalize
lowercase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCamelCase__ (self : Optional[Any] , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Dict[str, int] , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 1 , _UpperCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : Any , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = get_size_dict(_UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}''' )
lowercase__ = get_resize_output_image_size(
_UpperCAmelCase , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=_UpperCAmelCase , multiple=_UpperCAmelCase , )
return resize(_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : str , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, float] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : str , ) -> int:
"""simple docstring"""
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : str , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Union[float, List[float]] , _UpperCAmelCase : Optional[Union[str, ChannelDimension]] = None , **_UpperCAmelCase : int , ) -> np.ndarray:
"""simple docstring"""
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def lowerCamelCase__ (self : Any , _UpperCAmelCase : ImageInput , _UpperCAmelCase : bool = None , _UpperCAmelCase : int = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : int = None , _UpperCAmelCase : PILImageResampling = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : float = None , _UpperCAmelCase : bool = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[float, List[float]]] = None , _UpperCAmelCase : Optional[Union[str, TensorType]] = None , _UpperCAmelCase : ChannelDimension = ChannelDimension.FIRST , **_UpperCAmelCase : Optional[Any] , ) -> PIL.Image.Image:
"""simple docstring"""
lowercase__ = do_resize if do_resize is not None else self.do_resize
lowercase__ = size if size is not None else self.size
lowercase__ = get_size_dict(_UpperCAmelCase )
lowercase__ = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
lowercase__ = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
lowercase__ = resample if resample is not None else self.resample
lowercase__ = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ = image_mean if image_mean is not None else self.image_mean
lowercase__ = image_std if image_std is not None else self.image_std
lowercase__ = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
lowercase__ = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
lowercase__ = [self.resize(image=_UpperCAmelCase , size=_UpperCAmelCase , resample=_UpperCAmelCase ) for image in images]
if do_rescale:
lowercase__ = [self.rescale(image=_UpperCAmelCase , scale=_UpperCAmelCase ) for image in images]
if do_normalize:
lowercase__ = [self.normalize(image=_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase ) for image in images]
lowercase__ = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
lowercase__ = {"""pixel_values""": images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Tuple] = None ) -> Dict:
"""simple docstring"""
lowercase__ = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(_UpperCAmelCase ):
lowercase__ = target_sizes.numpy()
lowercase__ = []
for idx in range(len(_UpperCAmelCase ) ):
lowercase__ = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=_UpperCAmelCase )
lowercase__ = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_UpperCAmelCase )
else:
lowercase__ = logits.argmax(dim=1 )
lowercase__ = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 15 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available
if is_torch_available():
from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification
if is_tf_available():
from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification
if is_flax_available():
from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification
_lowerCamelCase = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co
_lowerCamelCase = '''main'''
# Default branch name
_lowerCamelCase = '''f2c752cfc5c0ab6f4bdec59acea69eefbee381c2'''
# One particular commit (not the top of `main`)
_lowerCamelCase = '''aaaaaaa'''
# This commit does not exist, so we should 404.
_lowerCamelCase = '''d9e9f15bc825e4b2c9249e9578f884bbcb5e3684'''
# Sha-1 of config.json on the top of `main`, for checking purposes
_lowerCamelCase = '''4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3'''
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Welcome!''' )
yield
print('''Bye!''' )
@contextlib.contextmanager
def lowerCAmelCase_ ( ):
'''simple docstring'''
print('''Bonjour!''' )
yield
print('''Au revoir!''' )
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
# If the spec is missing, importlib would not be able to import the module dynamically.
assert transformers.__spec__ is not None
assert importlib.util.find_spec('''transformers''' ) is not None
class snake_case ( unittest.TestCase ):
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :Optional[int] ):
with ContextManagers([] ):
print('''Transformers are awesome!''' )
# The print statement adds a new line at the end of the output
self.assertEqual(mock_stdout.getvalue() , '''Transformers are awesome!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :str , _lowerCamelCase :Optional[int] ):
with ContextManagers([context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Welcome!\nTransformers are awesome!\nBye!\n''' )
@unittest.mock.patch('''sys.stdout''' , new_callable=io.StringIO )
def SCREAMING_SNAKE_CASE_ ( self :Dict , _lowerCamelCase :List[str] ):
with ContextManagers([context_fr(), context_en()] ):
print('''Transformers are awesome!''' )
# The output should be wrapped with an English and French welcome and goodbye
self.assertEqual(mock_stdout.getvalue() , '''Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n''' )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_tf
def SCREAMING_SNAKE_CASE_ ( self :int ):
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels''', '''next_sentence_label'''] )
self.assertEqual(find_labels(_lowerCamelCase ) , ['''start_positions''', '''end_positions'''] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , ['''labels'''] )
@require_flax
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
# Flax models don't have labels
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
class snake_case ( __UpperCAmelCase ):
pass
self.assertEqual(find_labels(_lowerCamelCase ) , [] )
| 674 | 0 |
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__A : Tuple = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6S 9D TH AD',
'KS 8D 4D 9S 4S', # pair
'8C 4S KH JS 4D', # pair
'QH 8H KD JH 8S', # pair
'KC 4H KS 2H 8D', # pair
'KD 4S KC 3H 8S', # pair
'AH 8S AS KC JH', # pair
'3H 4C 4H 3S 2H', # 2 pairs
'5S 5D 2C KH KH', # 2 pairs
'3C KH 5D 5S KH', # 2 pairs
'AS 3C KH AD KH', # 2 pairs
'7C 7S 3S 7H 5S', # 3 of a kind
'7C 7S KH 2H 7H', # 3 of a kind
'AC KH QH AH AS', # 3 of a kind
'2H 4D 3C AS 5S', # straight (low ace)
'3C 5C 4C 2C 6H', # straight
'6S 8S 7S 5H 9H', # straight
'JS QS 9H TS KH', # straight
'QC KH TS JS AH', # straight (high ace)
'8C 9C 5C 3C TC', # flush
'3S 8S 9S 5S KS', # flush
'4C 5C 9C 8C KC', # flush
'JH 8H AH KH QH', # flush
'3D 2H 3H 2C 2D', # full house
'2H 2C 3S 3H 3D', # full house
'KH KC 3S 3H 3D', # full house
'JC 6H JS JD JH', # 4 of a kind
'JC 7H JS JD JH', # 4 of a kind
'JC KH JS JD JH', # 4 of a kind
'2S AS 4S 5S 3S', # straight flush (low ace)
'2D 6D 3D 4D 5D', # straight flush
'5C 6C 3C 7C 4C', # straight flush
'JH 9H TH KH QH', # straight flush
'JH AH TH KH QH', # royal flush (high ace straight flush)
)
__A : Union[str, Any] = (
('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)
__A : List[Any] = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', True),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', False),
('AS 3S 4S 8S 2S', True),
)
__A : int = (
('2H 3H 4H 5H 6H', True),
('AS AH 2H AD AC', False),
('2H 3H 5H 6H 7H', False),
('KS AS TS QS JS', True),
('8H 9H QS JS TH', True),
)
__A : Any = (
('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 1_4]),
('2H 5D 3C AS 5S', False, [1_4, 5, 5, 3, 2]),
('JH QD KC AS TS', False, [1_4, 1_3, 1_2, 1_1, 1_0]),
('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)
__A : List[Any] = (
('JH AH TH KH QH', 0),
('JH 9H TH KH QH', 0),
('JC KH JS JD JH', 7),
('KH KC 3S 3H 3D', 6),
('8C 9C 5C 3C TC', 0),
('JS QS 9H TS KH', 0),
('7C 7S KH 2H 7H', 3),
('3C KH 5D 5S KH', 2),
('QH 8H KD JH 8S', 1),
('2D 6D 9D TH 7D', 0),
)
__A : str = (
('JH AH TH KH QH', 2_3),
('JH 9H TH KH QH', 2_2),
('JC KH JS JD JH', 2_1),
('KH KC 3S 3H 3D', 2_0),
('8C 9C 5C 3C TC', 1_9),
('JS QS 9H TS KH', 1_8),
('7C 7S KH 2H 7H', 1_7),
('3C KH 5D 5S KH', 1_6),
('QH 8H KD JH 8S', 1_5),
('2D 6D 9D TH 7D', 1_4),
)
def __a ( ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = randrange(len(A__ ) ), randrange(len(A__ ) )
SCREAMING_SNAKE_CASE = ["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def __a ( A__ : int = 100 ):
return (generate_random_hand() for _ in range(A__ ))
@pytest.mark.parametrize("hand, expected" , A__ )
def __a ( A__ : str , A__ : List[Any] ):
assert PokerHand(A__ )._is_flush() == expected
@pytest.mark.parametrize("hand, expected" , A__ )
def __a ( A__ : Optional[Any] , A__ : int ):
assert PokerHand(A__ )._is_straight() == expected
@pytest.mark.parametrize("hand, expected, card_values" , A__ )
def __a ( A__ : Any , A__ : List[str] , A__ : Any ):
SCREAMING_SNAKE_CASE = PokerHand(A__ )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("hand, expected" , A__ )
def __a ( A__ : Optional[int] , A__ : str ):
assert PokerHand(A__ )._is_same_kind() == expected
@pytest.mark.parametrize("hand, expected" , A__ )
def __a ( A__ : str , A__ : Optional[int] ):
assert PokerHand(A__ )._hand_type == expected
@pytest.mark.parametrize("hand, other, expected" , A__ )
def __a ( A__ : Union[str, Any] , A__ : str , A__ : Optional[Any] ):
assert PokerHand(A__ ).compare_with(PokerHand(A__ ) ) == expected
@pytest.mark.parametrize("hand, other, expected" , generate_random_hands() )
def __a ( A__ : List[Any] , A__ : Optional[Any] , A__ : List[Any] ):
assert PokerHand(A__ ).compare_with(PokerHand(A__ ) ) == expected
def __a ( ):
SCREAMING_SNAKE_CASE = [PokerHand(A__ ) for hand in SORTED_HANDS]
SCREAMING_SNAKE_CASE = poker_hands.copy()
shuffle(A__ )
SCREAMING_SNAKE_CASE = chain(sorted(A__ ) )
for index, hand in enumerate(A__ ):
assert hand == poker_hands[index]
def __a ( ):
# Test that five high straights are compared correctly.
SCREAMING_SNAKE_CASE = [PokerHand("2D AC 3H 4H 5S" ), PokerHand("2S 3H 4H 5S 6C" )]
pokerhands.sort(reverse=A__ )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def __a ( ):
# Multiple calls to five_high_straight function should still return True
# and shouldn't mutate the list in every call other than the first.
SCREAMING_SNAKE_CASE = PokerHand("2C 4S AS 3D 5C" )
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def __a ( ):
# Problem number 54 from Project Euler
# Testing from poker_hands.txt file
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = os.path.abspath(os.path.dirname(A__ ) )
SCREAMING_SNAKE_CASE = os.path.join(A__ , "poker_hands.txt" )
with open(A__ ) as file_hand:
for line in file_hand:
SCREAMING_SNAKE_CASE = line[:14].strip()
SCREAMING_SNAKE_CASE = line[15:].strip()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = PokerHand(A__ ), PokerHand(A__ )
SCREAMING_SNAKE_CASE = player.compare_with(A__ )
if output == "Win":
answer += 1
assert answer == 376 | 16 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class snake_case ( unittest.TestCase ):
def __init__( self :List[Any] , _lowerCamelCase :Dict , _lowerCamelCase :Tuple=7 , _lowerCamelCase :Dict=3 , _lowerCamelCase :Optional[Any]=3_0 , _lowerCamelCase :List[str]=4_0_0 , _lowerCamelCase :Union[str, Any]=True , _lowerCamelCase :Union[str, Any]=None , _lowerCamelCase :List[Any]=True , _lowerCamelCase :Any=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=[0.5, 0.5, 0.5] , _lowerCamelCase :Dict=True , _lowerCamelCase :str=1 / 2_5_5 , _lowerCamelCase :Union[str, Any]=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
__SCREAMING_SNAKE_CASE : Optional[Any] = size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
__SCREAMING_SNAKE_CASE : List[str] = parent
__SCREAMING_SNAKE_CASE : Dict = batch_size
__SCREAMING_SNAKE_CASE : str = num_channels
__SCREAMING_SNAKE_CASE : Union[str, Any] = min_resolution
__SCREAMING_SNAKE_CASE : Union[str, Any] = max_resolution
__SCREAMING_SNAKE_CASE : Tuple = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : int = do_normalize
__SCREAMING_SNAKE_CASE : List[Any] = image_mean
__SCREAMING_SNAKE_CASE : Tuple = image_std
__SCREAMING_SNAKE_CASE : Dict = do_rescale
__SCREAMING_SNAKE_CASE : Optional[int] = rescale_factor
__SCREAMING_SNAKE_CASE : List[Any] = do_pad
def SCREAMING_SNAKE_CASE_ ( self :Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE_ ( self :Tuple , _lowerCamelCase :Dict , _lowerCamelCase :List[Any]=False ):
if not batched:
__SCREAMING_SNAKE_CASE : str = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = image.size
else:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = image.shape[1], image.shape[2]
if w < h:
__SCREAMING_SNAKE_CASE : str = int(self.size['''shortest_edge'''] * h / w )
__SCREAMING_SNAKE_CASE : int = self.size['''shortest_edge''']
elif w > h:
__SCREAMING_SNAKE_CASE : Optional[Any] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : int = int(self.size['''shortest_edge'''] * w / h )
else:
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
__SCREAMING_SNAKE_CASE : List[str] = self.size['''shortest_edge''']
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = []
for image in image_inputs:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
__SCREAMING_SNAKE_CASE : Optional[int] = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[0] )[0]
__SCREAMING_SNAKE_CASE : int = max(_lowerCamelCase , key=lambda _lowerCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class snake_case ( __UpperCAmelCase , unittest.TestCase ):
lowerCamelCase__ = YolosImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Any = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , '''image_mean''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''image_std''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_normalize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''do_resize''' ) )
self.assertTrue(hasattr(_lowerCamelCase , '''size''' ) )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class.from_dict(
self.image_processor_dict , size=4_2 , max_size=8_4 , pad_and_return_pixel_mask=_lowerCamelCase )
self.assertEqual(image_processor.size , {'''shortest_edge''': 4_2, '''longest_edge''': 8_4} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
pass
def SCREAMING_SNAKE_CASE_ ( self :int ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__SCREAMING_SNAKE_CASE : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
__SCREAMING_SNAKE_CASE : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Dict = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Tuple = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__SCREAMING_SNAKE_CASE : Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
__SCREAMING_SNAKE_CASE : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : List[Any] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :Any ):
# Initialize image_processing
__SCREAMING_SNAKE_CASE : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
__SCREAMING_SNAKE_CASE : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
__SCREAMING_SNAKE_CASE : Optional[int] = image_processing(_lowerCamelCase , return_tensors='''pt''' ).pixel_values
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
# Initialize image_processings
__SCREAMING_SNAKE_CASE : Dict = self.image_processing_class(**self.image_processor_dict )
__SCREAMING_SNAKE_CASE : Tuple = self.image_processing_class(do_resize=_lowerCamelCase , do_normalize=_lowerCamelCase , do_rescale=_lowerCamelCase )
# create random PyTorch tensors
__SCREAMING_SNAKE_CASE : int = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a.pad(_lowerCamelCase , return_tensors='''pt''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_processing_a(_lowerCamelCase , return_tensors='''pt''' )
self.assertTrue(
torch.allclose(encoded_images_with_method['''pixel_values'''] , encoded_images['''pixel_values'''] , atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :int ):
# prepare image and target
__SCREAMING_SNAKE_CASE : Tuple = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Tuple = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Union[str, Any] = {'''image_id''': 3_9_7_6_9, '''annotations''': target}
# encode them
__SCREAMING_SNAKE_CASE : List[Any] = YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
__SCREAMING_SNAKE_CASE : Optional[Any] = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : Optional[int] = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : str = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify orig_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# prepare image, target and masks_path
__SCREAMING_SNAKE_CASE : List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' , '''r''' ) as f:
__SCREAMING_SNAKE_CASE : Union[str, Any] = json.loads(f.read() )
__SCREAMING_SNAKE_CASE : Dict = {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}
__SCREAMING_SNAKE_CASE : Optional[int] = pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )
# encode them
__SCREAMING_SNAKE_CASE : Any = YolosImageProcessor(format='''coco_panoptic''' )
__SCREAMING_SNAKE_CASE : Dict = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors='''pt''' )
# verify pixel values
__SCREAMING_SNAKE_CASE : str = torch.Size([1, 3, 8_0_0, 1_0_6_6] )
self.assertEqual(encoding['''pixel_values'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : str = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] , _lowerCamelCase , atol=1e-4 ) )
# verify area
__SCREAMING_SNAKE_CASE : Any = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] , _lowerCamelCase ) )
# verify boxes
__SCREAMING_SNAKE_CASE : List[str] = torch.Size([6, 4] )
self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape , _lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] , _lowerCamelCase , atol=1e-3 ) )
# verify image_id
__SCREAMING_SNAKE_CASE : Dict = torch.tensor([3_9_7_6_9] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] , _lowerCamelCase ) )
# verify is_crowd
__SCREAMING_SNAKE_CASE : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] , _lowerCamelCase ) )
# verify class_labels
__SCREAMING_SNAKE_CASE : Optional[Any] = torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] , _lowerCamelCase ) )
# verify masks
__SCREAMING_SNAKE_CASE : Optional[Any] = 8_2_2_8_7_3
self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() , _lowerCamelCase )
# verify orig_size
__SCREAMING_SNAKE_CASE : List[str] = torch.tensor([4_8_0, 6_4_0] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] , _lowerCamelCase ) )
# verify size
__SCREAMING_SNAKE_CASE : Any = torch.tensor([8_0_0, 1_0_6_6] )
self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] , _lowerCamelCase ) )
| 674 | 0 |
def __SCREAMING_SNAKE_CASE ( a__ : int ,a__ : int ,a__ : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
__A : int = _modexpt(a__ ,exponent // 2 ,a__ ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(a__ ,exponent - 1 ,a__ )) % modulo_value
def __SCREAMING_SNAKE_CASE ( a__ : int = 1777 ,a__ : int = 1855 ,a__ : int = 8 ) -> int:
__A : Dict = base
for _ in range(1 ,a__ ):
__A : Optional[Any] = _modexpt(a__ ,a__ ,10**digits )
return result
if __name__ == "__main__":
print(f"""{solution() = }""")
| 17 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = str(lowercase_ )
return len(lowercase_ ) == 9 and set(lowercase_ ) == set('''123456789''' )
def lowerCAmelCase_ ( ):
'''simple docstring'''
for base_num in range(9999 , 4999 , -1 ):
__SCREAMING_SNAKE_CASE : List[str] = 10_0002 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__SCREAMING_SNAKE_CASE : List[Any] = 100_2003 * base_num
if is_9_pandigital(lowercase_ ):
return candidate
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 674 | 0 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_SCREAMING_SNAKE_CASE = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __a(SCREAMING_SNAKE_CASE_ : dict[int, list[int]] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[bool] ):
'''simple docstring'''
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
order.append(SCREAMING_SNAKE_CASE_ )
return order
def __a(SCREAMING_SNAKE_CASE_ : dict[int, list[int]] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[bool] ):
'''simple docstring'''
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return component
def __a(SCREAMING_SNAKE_CASE_ : dict[int, list[int]] ):
'''simple docstring'''
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(SCREAMING_SNAKE_CASE_ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = []
for i, was_visited in enumerate(SCREAMING_SNAKE_CASE_ ):
if not was_visited:
order += topology_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = []
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ ) * [False]
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
_lowerCAmelCase = order[len(SCREAMING_SNAKE_CASE_ ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
components_list.append(SCREAMING_SNAKE_CASE_ )
return components_list
| 18 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self :int ):
__SCREAMING_SNAKE_CASE : Tuple = 0
def SCREAMING_SNAKE_CASE_ ( self :Any ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Optional[Any] = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :int ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : str = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__SCREAMING_SNAKE_CASE : Tuple = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Any = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(_lowerCamelCase ).to_dict()
config_dict.pop('''image_processor_type''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPImageProcessor(**_lowerCamelCase )
# save in new folder
model_config.save_pretrained(_lowerCamelCase )
config.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
# make sure private variable is not incorrectly saved
__SCREAMING_SNAKE_CASE : Tuple = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
def SCREAMING_SNAKE_CASE_ ( self :Optional[int] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''clip-base is not a local folder and is not a valid model identifier''' ):
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained('''clip-base''' )
def SCREAMING_SNAKE_CASE_ ( self :Union[str, Any] ):
with self.assertRaisesRegex(
_lowerCamelCase , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(_lowerCamelCase , revision='''aaaaaa''' )
def SCREAMING_SNAKE_CASE_ ( self :List[str] ):
with self.assertRaisesRegex(
_lowerCamelCase , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
__SCREAMING_SNAKE_CASE : int = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase , trust_remote_code=_lowerCamelCase )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def SCREAMING_SNAKE_CASE_ ( self :Tuple ):
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(_lowerCamelCase ):
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''preprocessor_config.json'''
__SCREAMING_SNAKE_CASE : Dict = Path(_lowerCamelCase ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(_lowerCamelCase , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(_lowerCamelCase , '''w''' ) )
__SCREAMING_SNAKE_CASE : Optional[int] = CustomImageProcessor.from_pretrained(_lowerCamelCase )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(_lowerCamelCase )
__SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(_lowerCamelCase )
self.assertIsInstance(_lowerCamelCase , _lowerCamelCase )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def SCREAMING_SNAKE_CASE_ ( self :Dict ):
class snake_case ( __UpperCAmelCase ):
lowerCamelCase__ = True
try:
AutoConfig.register('''custom''' , _lowerCamelCase )
AutoImageProcessor.register(_lowerCamelCase , _lowerCamelCase )
# If remote code is not set, the default is to use local
__SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=_lowerCamelCase )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(_lowerCamelCase , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 674 | 0 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
_a = """examples/"""
_a = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""": (re.compile(R"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
"""setup""": (re.compile(R"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R"""\1version=\"VERSION\","""),
"""doc""": (re.compile(R"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
_a = {
"""init""": """src/diffusers/__init__.py""",
"""setup""": """setup.py""",
}
_a = """README.md"""
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case ) -> Union[str, Any]:
"""simple docstring"""
with open(__snake_case, '''r''', encoding='''utf-8''', newline='''\n''' ) as f:
_UpperCamelCase = f.read()
_UpperCamelCase , _UpperCamelCase = REPLACE_PATTERNS[pattern]
_UpperCamelCase = replace.replace('''VERSION''', __snake_case )
_UpperCamelCase = re_pattern.sub(__snake_case, __snake_case )
with open(__snake_case, '''w''', encoding='''utf-8''', newline='''\n''' ) as f:
f.write(__snake_case )
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
for folder, directories, fnames in os.walk(__snake_case ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(__snake_case, __snake_case ), __snake_case, pattern='''examples''' )
def lowerCamelCase__ ( __snake_case, __snake_case=False ) -> Dict:
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case, __snake_case, __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowerCamelCase__ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = '''🤗 Transformers currently provides the following architectures'''
_UpperCamelCase = '''1. Want to contribute a new model?'''
with open(__snake_case, '''r''', encoding='''utf-8''', newline='''\n''' ) as f:
_UpperCamelCase = f.readlines()
# Find the start of the list.
_UpperCamelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_UpperCamelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
_UpperCamelCase = lines[index].replace(
'''https://huggingface.co/docs/diffusers/main/model_doc''', '''https://huggingface.co/docs/diffusers/model_doc''', )
index += 1
with open(__snake_case, '''w''', encoding='''utf-8''', newline='''\n''' ) as f:
f.writelines(__snake_case )
def lowerCamelCase__ ( ) -> str:
"""simple docstring"""
with open(REPLACE_FILES['''init'''], '''r''' ) as f:
_UpperCamelCase = f.read()
_UpperCamelCase = REPLACE_PATTERNS['''init'''][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowerCamelCase__ ( __snake_case=False ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
_UpperCamelCase = default_version.base_version
elif patch:
_UpperCamelCase = F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
else:
_UpperCamelCase = F'''{default_version.major}.{default_version.minor + 1}.0'''
# Now let's ask nicely if that's the right one.
_UpperCamelCase = input(F'''Which version are you releasing? [{default_version}]''' )
if len(__snake_case ) == 0:
_UpperCamelCase = default_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case, patch=__snake_case )
def lowerCamelCase__ ( ) -> Any:
"""simple docstring"""
_UpperCamelCase = get_version()
_UpperCamelCase = F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
_UpperCamelCase = current_version.base_version
# Check with the user we got that right.
_UpperCamelCase = input(F'''Which version are we developing now? [{dev_version}]''' )
if len(__snake_case ) == 0:
_UpperCamelCase = dev_version
print(F'''Updating version to {version}.''' )
global_version_update(__snake_case )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
_a = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("""Nothing to do after a patch :-)""")
else:
post_release_work()
| 19 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case ( __UpperCAmelCase ):
pass
class snake_case :
def __init__( self :List[Any] , _lowerCamelCase :Any ):
__SCREAMING_SNAKE_CASE : Any = data
__SCREAMING_SNAKE_CASE : Node | None = None
def __iter__( self :Union[str, Any] ):
__SCREAMING_SNAKE_CASE : List[str] = self
__SCREAMING_SNAKE_CASE : List[str] = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(_lowerCamelCase )
yield node.data
__SCREAMING_SNAKE_CASE : List[str] = node.next_node
@property
def SCREAMING_SNAKE_CASE_ ( self :Any ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
_lowerCamelCase = Node(1)
_lowerCamelCase = Node(2)
_lowerCamelCase = Node(3)
_lowerCamelCase = Node(4)
print(root_node.has_loop) # False
_lowerCamelCase = root_node.next_node
print(root_node.has_loop) # True
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
_lowerCamelCase = Node(5)
_lowerCamelCase = Node(6)
print(root_node.has_loop) # False
_lowerCamelCase = Node(1)
print(root_node.has_loop) # False
| 674 | 0 |
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