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 |
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'''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
_a : Union[str, Any] = logging.get_logger(__name__)
_a : Tuple = {'''vocab_file''': '''spiece.model'''}
_a : Dict = {
'''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
_a : List[str] = {
'''t5-small''': 512,
'''t5-base''': 512,
'''t5-large''': 512,
'''t5-3b''': 512,
'''t5-11b''': 512,
}
_a : Tuple = '''▁'''
class lowercase_ ( a_ ):
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
__lowerCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : int = ["input_ids", "attention_mask"]
def __init__( self , a_ , a_="</s>" , a_="<unk>" , a_="<pad>" , a_=1_0_0 , a_=None , a_ = None , a_=True , **a_ , ) -> None:
"""simple docstring"""
if extra_ids > 0 and additional_special_tokens is None:
UpperCAmelCase = [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
UpperCAmelCase = len(set(filter(lambda a_ : 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' )
UpperCAmelCase = legacy
UpperCAmelCase = {} 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__ , )
UpperCAmelCase = vocab_file
UpperCAmelCase = extra_ids
UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase__ )
@staticmethod
def snake_case_ ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
UpperCAmelCase = 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 snake_case_ ( self ) -> Optional[int]:
"""simple docstring"""
return self.sp_model.get_piece_size() + self._extra_ids
def snake_case_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase = {self.convert_ids_to_tokens(lowerCAmelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self , a_ , a_ = None , a_ = False ) -> List[int]:
"""simple docstring"""
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 snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
return list(
set(filter(lambda a_ : bool(re.search(r'<extra_id_\d+>' , lowerCAmelCase__ ) ) is not None , self.additional_special_tokens ) ) )
def snake_case_ ( self ) -> Optional[Any]:
"""simple docstring"""
return [self._convert_token_to_id(lowerCAmelCase__ ) for token in self.get_sentinel_tokens()]
def snake_case_ ( self , a_ ) -> List[int]:
"""simple docstring"""
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 snake_case_ ( self , a_ , a_ = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase = [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 snake_case_ ( self , a_ , a_ = None ) -> List[int]:
"""simple docstring"""
UpperCAmelCase = self._add_eos_if_not_present(lowerCAmelCase__ )
if token_ids_a is None:
return token_ids_a
else:
UpperCAmelCase = self._add_eos_if_not_present(lowerCAmelCase__ )
return token_ids_a + token_ids_a
def __getstate__( self ) -> int:
"""simple docstring"""
UpperCAmelCase = self.__dict__.copy()
UpperCAmelCase = None
return state
def __setstate__( self , a_ ) -> int:
"""simple docstring"""
UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
UpperCAmelCase = {}
UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def snake_case_ ( self , a_ , **a_ ) -> List[str]:
"""simple docstring"""
if not self.legacy:
UpperCAmelCase = SPIECE_UNDERLINE + text.replace(lowerCAmelCase__ , ' ' )
return super().tokenize(lowerCAmelCase__ , **lowerCAmelCase__ )
def snake_case_ ( self , a_ , **a_ ) -> Any:
"""simple docstring"""
if not self.legacy:
UpperCAmelCase = text.startswith(lowerCAmelCase__ )
if is_first:
UpperCAmelCase = text[1:]
UpperCAmelCase = 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__ ):
UpperCAmelCase = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def snake_case_ ( self , a_ ) -> List[str]:
"""simple docstring"""
if token.startswith('<extra_id_' ):
UpperCAmelCase = re.match(r'<extra_id_(\d+)>' , lowerCAmelCase__ )
UpperCAmelCase = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(lowerCAmelCase__ )
def snake_case_ ( self , a_ ) -> Optional[int]:
"""simple docstring"""
if index < self.sp_model.get_piece_size():
UpperCAmelCase = self.sp_model.IdToPiece(lowerCAmelCase__ )
else:
UpperCAmelCase = F'''<extra_id_{self.vocab_size - 1 - index}>'''
return token
def snake_case_ ( self , a_ ) -> int:
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = ""
UpperCAmelCase = 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
UpperCAmelCase = True
UpperCAmelCase = []
else:
current_sub_tokens.append(lowerCAmelCase__ )
UpperCAmelCase = False
out_string += self.sp_model.decode(lowerCAmelCase__ )
return out_string.strip()
def snake_case_ ( self , a_ , a_ = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
UpperCAmelCase = 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:
UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__ )
return (out_vocab_file,)
| 447 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__lowerCamelCase : Optional[int] = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''' , [None, '''v2'''] )
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = hf_hub_url(repo_id=_lowercase , path=_lowercase , revision=_lowercase )
assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_lowercase )}"""
| 248 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
'''simple docstring'''
import math
lowerCamelCase = 10
lowerCamelCase = 7
lowerCamelCase = BALLS_PER_COLOUR * NUM_COLOURS
def _A ( _lowerCAmelCase = 20 ):
"""simple docstring"""
__lowercase =math.comb(_lowerCAmelCase , _lowerCAmelCase )
__lowercase =math.comb(NUM_BALLS - BALLS_PER_COLOUR , _lowerCAmelCase )
__lowercase =NUM_COLOURS * (1 - missing_colour / total)
return f"""{result:.9f}"""
if __name__ == "__main__":
print(solution(20))
| 474 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def lowerCAmelCase (__A , __A="shi-labs/oneformer_demo"):
"""simple docstring"""
with open(hf_hub_download(__A , __A , repo_type='''dataset''') , '''r''') as f:
_a = json.load(__A)
_a = {}
_a = []
_a = []
for key, info in class_info.items():
_a = info["name"]
class_names.append(info['''name'''])
if info["isthing"]:
thing_ids.append(int(__A))
_a = thing_ids
_a = class_names
return metadata
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=7 , A=3 , A=30 , A=400 , A=None , A=True , A=True , A=[0.5, 0.5, 0.5] , A=[0.5, 0.5, 0.5] , A=10 , A=False , A=255 , A="shi-labs/oneformer_demo" , A="ade20k_panoptic.json" , A=10 , ) -> int:
"""simple docstring"""
_a = parent
_a = batch_size
_a = num_channels
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
_a = do_normalize
_a = image_mean
_a = image_std
_a = class_info_file
_a = prepare_metadata(lowerCAmelCase__ , lowerCAmelCase__ )
_a = num_text
_a = repo_path
# for the post_process_functions
_a = 2
_a = 10
_a = 10
_a = 3
_a = 4
_a = num_labels
_a = do_reduce_labels
_a = ignore_index
def a__ (self ) -> str:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def a__ (self , A , A=False ) -> Optional[int]:
"""simple docstring"""
if not batched:
_a = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
_a = image.size
else:
_a = image.shape[1], image.shape[2]
if w < h:
_a = int(self.size['''shortest_edge'''] * h / w )
_a = self.size["shortest_edge"]
elif w > h:
_a = self.size["shortest_edge"]
_a = int(self.size['''shortest_edge'''] * w / h )
else:
_a = self.size["shortest_edge"]
_a = self.size["shortest_edge"]
else:
_a = []
for image in image_inputs:
_a = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_a = max(lowerCAmelCase__ , key=lambda A : item[0] )[0]
_a = max(lowerCAmelCase__ , key=lambda A : item[1] )[1]
return expected_height, expected_width
def a__ (self ) -> Tuple:
"""simple docstring"""
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __A ( a_ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[str] = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
__lowerCamelCase : int = image_processing_class
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = OneFormerImageProcessorTester(self )
@property
def a__ (self ) -> int:
"""simple docstring"""
return self.image_processing_tester.prepare_image_processor_dict()
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = 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''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''ignore_index''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''class_info_file''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''num_text''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''repo_path''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''metadata''' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , '''do_reduce_labels''' ) )
def a__ (self ) -> List[Any]:
"""simple docstring"""
pass
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_a = image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
_a = self.image_processing_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = self.image_processing_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
_a = image_processor(
lowerCAmelCase__ , ['''semantic'''] * len(lowerCAmelCase__ ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
_a = image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
_a = self.image_processing_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = self.image_processing_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
_a = image_processor(
lowerCAmelCase__ , ['''semantic'''] * len(lowerCAmelCase__ ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_a = image_processor(image_inputs[0] , ['''semantic'''] , return_tensors='''pt''' ).pixel_values
_a = self.image_processing_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = self.image_processing_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
_a = image_processor(
lowerCAmelCase__ , ['''semantic'''] * len(lowerCAmelCase__ ) , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def a__ (self , A=False , A=False , A="np" ) -> str:
"""simple docstring"""
_a = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
_a = self.image_processing_tester.num_labels
_a = None
_a = None
_a = prepare_image_inputs(self.image_processing_tester , equal_resolution=lowerCAmelCase__ )
if with_segmentation_maps:
_a = num_labels
if is_instance_map:
_a = list(range(lowerCAmelCase__ ) ) * 2
_a = dict(enumerate(lowerCAmelCase__ ) )
_a = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
_a = [Image.fromarray(lowerCAmelCase__ ) for annotation in annotations]
_a = image_processor(
lowerCAmelCase__ , ['''semantic'''] * len(lowerCAmelCase__ ) , lowerCAmelCase__ , return_tensors='''pt''' , instance_id_to_semantic_id=lowerCAmelCase__ , pad_and_return_pixel_mask=lowerCAmelCase__ , )
return inputs
def a__ (self ) -> List[Any]:
"""simple docstring"""
pass
def a__ (self ) -> Dict:
"""simple docstring"""
def common(A=False , A=None ):
_a = self.comm_get_image_processor_inputs(
with_segmentation_maps=lowerCAmelCase__ , is_instance_map=lowerCAmelCase__ , segmentation_type=lowerCAmelCase__ )
_a = inputs["mask_labels"]
_a = inputs["class_labels"]
_a = inputs["pixel_values"]
_a = inputs["text_inputs"]
# check the batch_size
for mask_label, class_label, text_input in zip(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(lowerCAmelCase__ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=lowerCAmelCase__ )
common(is_instance_map=lowerCAmelCase__ , segmentation_type='''pil''' )
common(is_instance_map=lowerCAmelCase__ , segmentation_type='''pil''' )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = np.zeros((20, 50) )
_a = 1
_a = 1
_a = 1
_a = binary_mask_to_rle(lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
_a = self.image_processing_tester.get_fake_oneformer_outputs()
_a = fature_extractor.post_process_semantic_segmentation(lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
_a = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
_a = fature_extractor.post_process_semantic_segmentation(lowerCAmelCase__ , target_sizes=lowerCAmelCase__ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
_a = self.image_processing_tester.get_fake_oneformer_outputs()
_a = image_processor.post_process_instance_segmentation(lowerCAmelCase__ , threshold=0 )
self.assertTrue(len(lowerCAmelCase__ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('''segmentation''' in el )
self.assertTrue('''segments_info''' in el )
self.assertEqual(type(el['''segments_info'''] ) , lowerCAmelCase__ )
self.assertEqual(
el['''segmentation'''].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file='''ade20k_panoptic.json''' , num_text=self.image_processing_tester.num_text , repo_path='''shi-labs/oneformer_demo''' , )
_a = self.image_processing_tester.get_fake_oneformer_outputs()
_a = image_processor.post_process_panoptic_segmentation(lowerCAmelCase__ , threshold=0 )
self.assertTrue(len(lowerCAmelCase__ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue('''segmentation''' in el )
self.assertTrue('''segments_info''' in el )
self.assertEqual(type(el['''segments_info'''] ) , lowerCAmelCase__ )
self.assertEqual(
el['''segmentation'''].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 11 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
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
__A : Dict = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
__A : Optional[Any] = importlib.util.spec_from_file_location(
'transformers',
os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
__A : List[Any] = spec.loader.load_module()
__A : Union[str, Any] = 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)`
__A : Union[str, Any] = re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')
__A : Any = {
'''CLIPConfigMixin''',
'''DecisionTransformerConfigMixin''',
'''EncoderDecoderConfigMixin''',
'''RagConfigMixin''',
'''SpeechEncoderDecoderConfigMixin''',
'''VisionEncoderDecoderConfigMixin''',
'''VisionTextDualEncoderConfigMixin''',
}
def __a ( ):
SCREAMING_SNAKE_CASE = []
for config_class in list(CONFIG_MAPPING.values() ):
SCREAMING_SNAKE_CASE = False
# source code of `config_class`
SCREAMING_SNAKE_CASE = inspect.getsource(A__ )
SCREAMING_SNAKE_CASE = _re_checkpoint.findall(A__ )
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 = checkpoint
# verify the checkpoint name corresponds to the checkpoint link
SCREAMING_SNAKE_CASE = F"https://huggingface.co/{ckpt_name}"
if ckpt_link == ckpt_link_from_name:
SCREAMING_SNAKE_CASE = True
break
SCREAMING_SNAKE_CASE = config_class.__name__
if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(A__ )
if len(A__ ) > 0:
SCREAMING_SNAKE_CASE = "\n".join(sorted(A__ ) )
raise ValueError(F"The following configurations don\'t contain any valid checkpoint:\n{message}" )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints() | 16 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
AlbertConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
DPRConfig,
ElectraConfig,
FlaubertConfig,
GPTaConfig,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
TaConfig,
TFAlbertForPreTraining,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFCamembertForMaskedLM,
TFCTRLLMHeadModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPTaLMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForSequenceClassification,
TFTaForConditionalGeneration,
TFTransfoXLLMHeadModel,
TFWavaVecaModel,
TFXLMRobertaForMaskedLM,
TFXLMWithLMHeadModel,
TFXLNetLMHeadModel,
TransfoXLConfig,
WavaVecaConfig,
WavaVecaModel,
XLMConfig,
XLMRobertaConfig,
XLNetConfig,
is_torch_available,
load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging
if is_torch_available():
import numpy as np
import torch
from . import (
AlbertForPreTraining,
BartForConditionalGeneration,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
CamembertForMaskedLM,
CTRLLMHeadModel,
DistilBertForMaskedLM,
DistilBertForQuestionAnswering,
DPRContextEncoder,
DPRQuestionEncoder,
DPRReader,
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPTaLMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
RobertaForMaskedLM,
RobertaForSequenceClassification,
TaForConditionalGeneration,
TransfoXLLMHeadModel,
XLMRobertaForMaskedLM,
XLMWithLMHeadModel,
XLNetLMHeadModel,
)
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE : Optional[int] = {
'''bart''': (
BartConfig,
TFBartForConditionalGeneration,
TFBartForSequenceClassification,
BartForConditionalGeneration,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''bert''': (
BertConfig,
TFBertForPreTraining,
BertForPreTraining,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
BertConfig,
TFBertForQuestionAnswering,
BertForQuestionAnswering,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''bert-base-cased-finetuned-mrpc''': (
BertConfig,
TFBertForSequenceClassification,
BertForSequenceClassification,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''dpr''': (
DPRConfig,
TFDPRQuestionEncoder,
TFDPRContextEncoder,
TFDPRReader,
DPRQuestionEncoder,
DPRContextEncoder,
DPRReader,
DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''gpt2''': (
GPTaConfig,
TFGPTaLMHeadModel,
GPTaLMHeadModel,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlnet''': (
XLNetConfig,
TFXLNetLMHeadModel,
XLNetLMHeadModel,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm''': (
XLMConfig,
TFXLMWithLMHeadModel,
XLMWithLMHeadModel,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''xlm-roberta''': (
XLMRobertaConfig,
TFXLMRobertaForMaskedLM,
XLMRobertaForMaskedLM,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''transfo-xl''': (
TransfoXLConfig,
TFTransfoXLLMHeadModel,
TransfoXLLMHeadModel,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''openai-gpt''': (
OpenAIGPTConfig,
TFOpenAIGPTLMHeadModel,
OpenAIGPTLMHeadModel,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''roberta''': (
RobertaConfig,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''layoutlm''': (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
'''roberta-large-mnli''': (
RobertaConfig,
TFRobertaForSequenceClassification,
RobertaForSequenceClassification,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''camembert''': (
CamembertConfig,
TFCamembertForMaskedLM,
CamembertForMaskedLM,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''flaubert''': (
FlaubertConfig,
TFFlaubertWithLMHeadModel,
FlaubertWithLMHeadModel,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert''': (
DistilBertConfig,
TFDistilBertForMaskedLM,
DistilBertForMaskedLM,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''distilbert-base-distilled-squad''': (
DistilBertConfig,
TFDistilBertForQuestionAnswering,
DistilBertForQuestionAnswering,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert''': (
LxmertConfig,
TFLxmertForPreTraining,
LxmertForPreTraining,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''lxmert-visual-feature-encoder''': (
LxmertConfig,
TFLxmertVisualFeatureEncoder,
LxmertVisualFeatureEncoder,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''ctrl''': (
CTRLConfig,
TFCTRLLMHeadModel,
CTRLLMHeadModel,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''albert''': (
AlbertConfig,
TFAlbertForPreTraining,
AlbertForPreTraining,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''t5''': (
TaConfig,
TFTaForConditionalGeneration,
TaForConditionalGeneration,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''electra''': (
ElectraConfig,
TFElectraForPreTraining,
ElectraForPreTraining,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
'''wav2vec2''': (
WavaVecaConfig,
TFWavaVecaModel,
WavaVecaModel,
WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
}
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=False , UpperCamelCase__=True ):
"""simple docstring"""
if model_type not in MODEL_CLASSES:
raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.""" )
__magic_name__ : str = MODEL_CLASSES[model_type]
# Initialise TF model
if config_file in aws_config_map:
__magic_name__ : int = cached_file(UpperCamelCase__ , UpperCamelCase__ , force_download=not use_cached_models )
__magic_name__ : Any = config_class.from_json_file(UpperCamelCase__ )
__magic_name__ : Union[str, Any] = True
__magic_name__ : Tuple = True
print(F"""Building TensorFlow model from configuration: {config}""" )
__magic_name__ : Tuple = model_class(UpperCamelCase__ )
# Load weights from tf checkpoint
if pytorch_checkpoint_path in aws_config_map.keys():
__magic_name__ : str = cached_file(
UpperCamelCase__ , UpperCamelCase__ , force_download=not use_cached_models )
# Load PyTorch checkpoint in tf2 model:
__magic_name__ : Dict = load_pytorch_checkpoint_in_tfa_model(UpperCamelCase__ , UpperCamelCase__ )
if compare_with_pt_model:
__magic_name__ : Tuple = tf_model(tf_model.dummy_inputs , training=UpperCamelCase__ ) # build the network
__magic_name__ : Union[str, Any] = torch.load(UpperCamelCase__ , map_location="cpu" )
__magic_name__ : int = pt_model_class.from_pretrained(
pretrained_model_name_or_path=UpperCamelCase__ , config=UpperCamelCase__ , state_dict=UpperCamelCase__ )
with torch.no_grad():
__magic_name__ : List[str] = pt_model(**pt_model.dummy_inputs )
__magic_name__ : List[Any] = pto[0].numpy()
__magic_name__ : int = tfo[0].numpy()
__magic_name__ : str = np.amax(np.abs(np_pt - np_tf ) )
print(F"""Max absolute difference between models outputs {diff}""" )
assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}"""
# Save pytorch-model
print(F"""Save TensorFlow model to {tf_dump_path}""" )
tf_model.save_weights(UpperCamelCase__ , save_format="h5" )
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , ):
"""simple docstring"""
if args_model_type is None:
__magic_name__ : List[str] = list(MODEL_CLASSES.keys() )
else:
__magic_name__ : Tuple = [args_model_type]
for j, model_type in enumerate(UpperCamelCase__ , start=1 ):
print("=" * 100 )
print(F""" Converting model type {j}/{len(UpperCamelCase__ )}: {model_type}""" )
print("=" * 100 )
if model_type not in MODEL_CLASSES:
raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.""" )
__magic_name__ : Tuple = MODEL_CLASSES[model_type]
if model_shortcut_names_or_path is None:
__magic_name__ : Tuple = list(aws_model_maps.keys() )
if config_shortcut_names_or_path is None:
__magic_name__ : int = model_shortcut_names_or_path
for i, (model_shortcut_name, config_shortcut_name) in enumerate(
zip(UpperCamelCase__ , UpperCamelCase__ ) , start=1 ):
print("-" * 100 )
if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
if not only_convert_finetuned_models:
print(F""" Skipping finetuned checkpoint {model_shortcut_name}""" )
continue
__magic_name__ : Any = model_shortcut_name
elif only_convert_finetuned_models:
print(F""" Skipping not finetuned checkpoint {model_shortcut_name}""" )
continue
print(
F""" Converting checkpoint {i}/{len(UpperCamelCase__ )}: {model_shortcut_name} - model_type {model_type}""" )
print("-" * 100 )
if config_shortcut_name in aws_config_map:
__magic_name__ : Optional[Any] = cached_file(UpperCamelCase__ , UpperCamelCase__ , force_download=not use_cached_models )
else:
__magic_name__ : List[str] = config_shortcut_name
if model_shortcut_name in aws_model_maps:
__magic_name__ : Optional[int] = cached_file(UpperCamelCase__ , UpperCamelCase__ , force_download=not use_cached_models )
else:
__magic_name__ : Dict = model_shortcut_name
if os.path.isfile(UpperCamelCase__ ):
__magic_name__ : Tuple = "converted_model"
convert_pt_checkpoint_to_tf(
model_type=UpperCamelCase__ , pytorch_checkpoint_path=UpperCamelCase__ , config_file=UpperCamelCase__ , tf_dump_path=os.path.join(UpperCamelCase__ , model_shortcut_name + "-tf_model.h5" ) , compare_with_pt_model=UpperCamelCase__ , )
if remove_cached_files:
os.remove(UpperCamelCase__ )
os.remove(UpperCamelCase__ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
)
parser.add_argument(
"--model_type",
default=None,
type=str,
help=(
f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
"convert all the models from AWS."
),
)
parser.add_argument(
"--pytorch_checkpoint_path",
default=None,
type=str,
help=(
"Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
"If not given, will download and convert all the checkpoints from AWS."
),
)
parser.add_argument(
"--config_file",
default=None,
type=str,
help=(
"The config json file corresponding to the pre-trained model. \n"
"This specifies the model architecture. If not given and "
"--pytorch_checkpoint_path is not given or is a shortcut name "
"use the configuration associated to the shortcut name on the AWS"
),
)
parser.add_argument(
"--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
)
parser.add_argument(
"--use_cached_models",
action="store_true",
help="Use cached models if possible instead of updating to latest checkpoint versions.",
)
parser.add_argument(
"--remove_cached_files",
action="store_true",
help="Remove pytorch models after conversion (save memory when converting in batches).",
)
parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
_SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
# if args.pytorch_checkpoint_path is not None:
# convert_pt_checkpoint_to_tf(args.model_type.lower(),
# args.pytorch_checkpoint_path,
# args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
# args.tf_dump_path,
# compare_with_pt_model=args.compare_with_pt_model,
# use_cached_models=args.use_cached_models)
# else:
convert_all_pt_checkpoints_to_tf(
args.model_type.lower() if args.model_type is not None else None,
args.tf_dump_path,
model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
if args.pytorch_checkpoint_path is not None
else None,
config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
compare_with_pt_model=args.compare_with_pt_model,
use_cached_models=args.use_cached_models,
remove_cached_files=args.remove_cached_files,
only_convert_finetuned_models=args.only_convert_finetuned_models,
) | 436 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
def _lowerCamelCase( lowercase__ , lowercase__ ) -> tuple[float, float]:
'''simple docstring'''
if not len(lowercase__ ) == len(lowercase__ ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError('Both a & b of two equations can\'t be zero.' )
# Extract the coefficients
__lowercase= equationa
__lowercase= equationa
# Calculate the determinants of the matrices
__lowercase= aa * ba - aa * ba
__lowercase= ca * ba - ca * ba
__lowercase= aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError('Infinite solutions. (Consistent system)' )
else:
raise ValueError('No solution. (Inconsistent system)' )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__lowercase= determinant_x / determinant
__lowercase= determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 230 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
'''simple docstring'''
import re
import subprocess
import sys
lowerCAmelCase : int = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
lowerCAmelCase : Optional[Any] = (
subprocess.check_output(f"""git diff --diff-filter=d --name-only {fork_point_sha}""".split()).decode('utf-8').split()
)
lowerCAmelCase : int = '''|'''.join(sys.argv[1:])
lowerCAmelCase : Tuple = re.compile(rf"""^({joined_dirs}).*?\.py$""")
lowerCAmelCase : List[Any] = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 3 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
'''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
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> Optional[Any]:
def run_func(UpperCamelCase ):
@wraps(UpperCamelCase )
def run_in_eager_mode(*UpperCamelCase ,**UpperCamelCase ):
return func(*UpperCamelCase ,**UpperCamelCase )
@wraps(UpperCamelCase )
@tf.function(experimental_compile=UpperCamelCase )
def run_in_graph_mode(*UpperCamelCase ,**UpperCamelCase ):
return func(*UpperCamelCase ,**UpperCamelCase )
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 snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> ["tf.Tensor"]:
_UpperCamelCase : List[Any] = random.Random()
_UpperCamelCase : Union[str, Any] = [rng.randint(0 ,vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(UpperCamelCase ,shape=(batch_size, sequence_length) ,dtype=tf.intaa )
class UpperCAmelCase ( a_ ):
"""simple docstring"""
A__ : List[str] = 42
A__ : List[Any] = 42
A__ : Optional[Any] = 'TensorFlow'
@property
def _lowercase ( self ) -> str:
return tf.__version__
def _lowercase ( self , _snake_case , _snake_case , _snake_case ) -> float:
_UpperCamelCase : Tuple = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_UpperCamelCase : int = self._prepare_inference_func(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return self._measure_speed(_inference )
def _lowercase ( self , _snake_case , _snake_case , _snake_case ) -> float:
_UpperCamelCase : int = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_UpperCamelCase : Optional[Any] = self._prepare_train_func(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return self._measure_speed(_train )
def _lowercase ( self , _snake_case , _snake_case , _snake_case ) -> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCAmelCase__ )
_UpperCamelCase : Tuple = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_UpperCamelCase : List[str] = self._prepare_inference_func(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return self._measure_memory(_inference )
def _lowercase ( self , _snake_case , _snake_case , _snake_case ) -> [Memory, Optional[MemorySummary]]:
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCAmelCase__ )
_UpperCamelCase : Tuple = self.args.strategy
if strategy is None:
raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
_UpperCamelCase : Dict = self._prepare_train_func(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
return self._measure_memory(_train )
def _lowercase ( self , _snake_case , _snake_case , _snake_case ) -> Callable[[], None]:
_UpperCamelCase : List[str] = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError('''Mixed precision is currently not supported.''' )
_UpperCamelCase : List[Any] = (
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:
_UpperCamelCase : Dict = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
_UpperCamelCase : Dict = __import__('''transformers''' , fromlist=[model_class] )
_UpperCamelCase : Any = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase : Union[str, 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:
_UpperCamelCase : Dict = TF_MODEL_MAPPING[config.__class__](lowerCAmelCase__ )
# encoder-decoder has vocab size saved differently
_UpperCamelCase : List[str] = config.vocab_size if hasattr(lowerCAmelCase__ , '''vocab_size''' ) else config.encoder.vocab_size
_UpperCamelCase : List[str] = 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__ )
_UpperCamelCase : int = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _lowercase ( self , _snake_case , _snake_case , _snake_case ) -> Callable[[], None]:
_UpperCamelCase : 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.''' )
_UpperCamelCase : Optional[Any] = (
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:
_UpperCamelCase : int = "TF" + config.architectures[0] # prepend 'TF' for tensorflow model
_UpperCamelCase : Union[str, Any] = __import__('''transformers''' , fromlist=[model_class] )
_UpperCamelCase : Optional[Any] = getattr(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase : Optional[int] = 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:
_UpperCamelCase : int = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase__ )
# encoder-decoder has vocab size saved differently
_UpperCamelCase : Union[str, Any] = config.vocab_size if hasattr(lowerCAmelCase__ , '''vocab_size''' ) else config.encoder.vocab_size
_UpperCamelCase : Dict = random_input_ids(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_UpperCamelCase : List[Any] = model(lowerCAmelCase__ , decoder_input_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , training=lowerCAmelCase__ )[0]
_UpperCamelCase : Optional[Any] = tf.gradients(lowerCAmelCase__ , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_UpperCamelCase : str = model(lowerCAmelCase__ , labels=lowerCAmelCase__ , training=lowerCAmelCase__ )[0]
_UpperCamelCase : int = tf.gradients(lowerCAmelCase__ , model.trainable_variables )
return gradients
_UpperCamelCase : Dict = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _lowercase ( self , _snake_case ) -> float:
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
_UpperCamelCase : List[str] = timeit.repeat(
lowerCAmelCase__ , repeat=self.args.repeat , number=10 , )
return min(lowerCAmelCase__ ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(F'''Doesn\'t fit on GPU. {e}''' )
def _lowercase ( self , _snake_case ) -> [Memory, MemorySummary]:
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.''' )
_UpperCamelCase : Union[str, Any] = 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.''' )
_UpperCamelCase : Dict = "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()
_UpperCamelCase : str = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_UpperCamelCase : int = nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase__ )
_UpperCamelCase : int = meminfo.used
_UpperCamelCase : str = 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.''' )
_UpperCamelCase : Dict = None
else:
_UpperCamelCase : int = measure_peak_memory_cpu(lowerCAmelCase__ )
_UpperCamelCase : int = Memory(lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else memory_bytes
if self.args.trace_memory_line_by_line:
_UpperCamelCase : Union[str, Any] = stop_memory_tracing(lowerCAmelCase__ )
if memory is None:
_UpperCamelCase : int = summary.total
else:
_UpperCamelCase : str = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F'''Doesn\'t fit on GPU. {e}''' )
return "N/A", None
| 683 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A (a_ ):
'''simple docstring'''
__lowerCamelCase : Tuple = ['''image_processor''', '''tokenizer''']
__lowerCamelCase : Optional[int] = '''ChineseCLIPImageProcessor'''
__lowerCamelCase : str = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self : Optional[Any] , __lowerCAmelCase : Optional[int]=None , __lowerCAmelCase : Optional[Any]=None , **__lowerCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
A__ = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowerCAmelCase__ , )
A__ = kwargs.pop("""feature_extractor""" )
A__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
A__ = self.image_processor
def __call__( self : List[Any] , __lowerCAmelCase : int=None , __lowerCAmelCase : Union[str, Any]=None , __lowerCAmelCase : List[str]=None , **__lowerCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
A__ = self.tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if images is not None:
A__ = self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if text is not None and images is not None:
A__ = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase__ ) , tensor_type=lowerCAmelCase__ )
def a_ ( self : Optional[Any] , *__lowerCAmelCase : Union[str, Any] , **__lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def a_ ( self : Tuple , *__lowerCAmelCase : List[Any] , **__lowerCAmelCase : Tuple ) -> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def a_ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
A__ = self.tokenizer.model_input_names
A__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def a_ ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowerCAmelCase__ , )
return self.image_processor_class
| 176 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
__A = logging.get_logger(__name__)
@dataclass
class _lowerCAmelCase ( a_ ):
"""simple docstring"""
__magic_name__ :str = [
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self , **__UpperCAmelCase ):
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowerCAmelCase__ :Union[str, Any] = deprecated_arg[3:]
setattr(self , lowerCAmelCase__ , not kwargs.pop(lowerCAmelCase__ ) )
logger.warning(
F"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
F" {positive_arg}={kwargs[positive_arg]}" )
lowerCAmelCase__ :Dict = kwargs.pop('torchscript' , self.torchscript )
lowerCAmelCase__ :Optional[int] = kwargs.pop('torch_xla_tpu_print_metrics' , self.torch_xla_tpu_print_metrics )
lowerCAmelCase__ :Any = kwargs.pop('fp16_opt_level' , self.fpaa_opt_level )
super().__init__(**lowerCAmelCase__ )
__magic_name__ :Tuple = field(default=a_ , metadata={"""help""": """Trace the models using torchscript"""} )
__magic_name__ :str = field(default=a_ , metadata={"""help""": """Print Xla/PyTorch tpu metrics"""} )
__magic_name__ :int = field(
default="""O1""" , metadata={
"""help""": (
"""For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. """
"""See details at https://nvidia.github.io/apex/amp.html"""
)
} , )
@cached_property
def snake_case ( self ):
'''simple docstring'''
requires_backends(self , ['torch'] )
logger.info('PyTorch: setting up devices' )
if not self.cuda:
lowerCAmelCase__ :str = torch.device('cpu' )
lowerCAmelCase__ :Any = 0
elif is_torch_tpu_available():
lowerCAmelCase__ :str = xm.xla_device()
lowerCAmelCase__ :Tuple = 0
else:
lowerCAmelCase__ :Any = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
lowerCAmelCase__ :Any = torch.cuda.device_count()
return device, n_gpu
@property
def snake_case ( self ):
'''simple docstring'''
return is_torch_tpu_available() and self.tpu
@property
def snake_case ( self ):
'''simple docstring'''
requires_backends(self , ['torch'] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def snake_case ( self ):
'''simple docstring'''
requires_backends(self , ['torch'] )
return self._setup_devices[0]
@property
def snake_case ( self ):
'''simple docstring'''
requires_backends(self , ['torch'] )
return self._setup_devices[1]
@property
def snake_case ( self ):
'''simple docstring'''
return self.n_gpu > 0
| 93 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCamelCase__ ( ):
UpperCAmelCase = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
UpperCAmelCase = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw ).convert('RGB' )
return image
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Optional[Any] ):
UpperCAmelCase = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias') )
# fmt: on
return rename_keys
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Dict ):
UpperCAmelCase = dct.pop(SCREAMING_SNAKE_CASE )
UpperCAmelCase = val
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Tuple ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
UpperCAmelCase = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
UpperCAmelCase = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
UpperCAmelCase = torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE , requires_grad=SCREAMING_SNAKE_CASE ), v_bias) )
UpperCAmelCase = qkv_bias
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Union[str, Any] ):
UpperCAmelCase = 364 if "coco" in model_name else 224
UpperCAmelCase = BlipaVisionConfig(image_size=SCREAMING_SNAKE_CASE ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
UpperCAmelCase = OPTConfig.from_pretrained('facebook/opt-2.7b' , eos_token_id=SCREAMING_SNAKE_CASE ).to_dict()
elif "opt-6.7b" in model_name:
UpperCAmelCase = OPTConfig.from_pretrained('facebook/opt-6.7b' , eos_token_id=SCREAMING_SNAKE_CASE ).to_dict()
elif "t5-xl" in model_name:
UpperCAmelCase = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
UpperCAmelCase = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
UpperCAmelCase = BlipaConfig(vision_config=SCREAMING_SNAKE_CASE , text_config=SCREAMING_SNAKE_CASE )
return config, image_size
@torch.no_grad()
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str=None , SCREAMING_SNAKE_CASE : List[str]=False ):
UpperCAmelCase = (
AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
if "opt" in model_name
else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
)
UpperCAmelCase = tokenizer('\n' , add_special_tokens=SCREAMING_SNAKE_CASE ).input_ids[0]
UpperCAmelCase = get_blipa_config(SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE )
UpperCAmelCase = BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE ).eval()
UpperCAmelCase = {
"blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
"blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
"blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
"blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
"blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
"blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
"blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
}
UpperCAmelCase = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
UpperCAmelCase = "cuda" if torch.cuda.is_available() else "cpu"
UpperCAmelCase = load_model_and_preprocess(
name=SCREAMING_SNAKE_CASE , model_type=SCREAMING_SNAKE_CASE , is_eval=SCREAMING_SNAKE_CASE , device=SCREAMING_SNAKE_CASE )
original_model.eval()
print('Done!' )
# update state dict keys
UpperCAmelCase = original_model.state_dict()
UpperCAmelCase = create_rename_keys(SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
UpperCAmelCase = state_dict.pop(SCREAMING_SNAKE_CASE )
if key.startswith('Qformer.bert' ):
UpperCAmelCase = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
UpperCAmelCase = key.replace('self' , 'attention' )
if "opt_proj" in key:
UpperCAmelCase = key.replace('opt_proj' , 'language_projection' )
if "t5_proj" in key:
UpperCAmelCase = key.replace('t5_proj' , 'language_projection' )
if key.startswith('opt' ):
UpperCAmelCase = key.replace('opt' , 'language' )
if key.startswith('t5' ):
UpperCAmelCase = key.replace('t5' , 'language' )
UpperCAmelCase = val
# read in qv biases
read_in_q_v_bias(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCAmelCase = hf_model.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )
assert len(SCREAMING_SNAKE_CASE ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
UpperCAmelCase = load_demo_image()
UpperCAmelCase = vis_processors["eval"](SCREAMING_SNAKE_CASE ).unsqueeze(0 ).to(SCREAMING_SNAKE_CASE )
UpperCAmelCase = tokenizer(['\n'] , return_tensors='pt' ).input_ids.to(SCREAMING_SNAKE_CASE )
# create processor
UpperCAmelCase = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=SCREAMING_SNAKE_CASE , image_std=SCREAMING_SNAKE_CASE )
UpperCAmelCase = BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE )
UpperCAmelCase = processor(images=SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values.to(SCREAMING_SNAKE_CASE )
# make sure processor creates exact same pixel values
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
original_model.to(SCREAMING_SNAKE_CASE )
hf_model.to(SCREAMING_SNAKE_CASE )
with torch.no_grad():
if "opt" in model_name:
UpperCAmelCase = original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
UpperCAmelCase = hf_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).logits
else:
UpperCAmelCase = original_model(
{'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
UpperCAmelCase = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
UpperCAmelCase = hf_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE ).logits
assert original_logits.shape == logits.shape
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
UpperCAmelCase = torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] , device=SCREAMING_SNAKE_CASE )
assert torch.allclose(logits[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
UpperCAmelCase = torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] , device=SCREAMING_SNAKE_CASE )
else:
# cast to same type
UpperCAmelCase = logits.dtype
assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE , atol=1E-2 )
print('Looks ok!' )
print('Generating a caption...' )
UpperCAmelCase = ""
UpperCAmelCase = tokenizer(SCREAMING_SNAKE_CASE , return_tensors='pt' ).input_ids.to(SCREAMING_SNAKE_CASE )
UpperCAmelCase = original_model.generate({'image': original_pixel_values} )
UpperCAmelCase = hf_model.generate(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , do_sample=SCREAMING_SNAKE_CASE , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print('Original generation:' , SCREAMING_SNAKE_CASE )
UpperCAmelCase = input_ids.shape[1]
UpperCAmelCase = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=SCREAMING_SNAKE_CASE )
UpperCAmelCase = [text.strip() for text in output_text]
print('HF generation:' , SCREAMING_SNAKE_CASE )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(SCREAMING_SNAKE_CASE )
hf_model.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
processor.push_to_hub(f'''nielsr/{model_name}''' )
hf_model.push_to_hub(f'''nielsr/{model_name}''' )
if __name__ == "__main__":
_a : Union[str, Any] = argparse.ArgumentParser()
_a : List[str] = [
'''blip2-opt-2.7b''',
'''blip2-opt-6.7b''',
'''blip2-opt-2.7b-coco''',
'''blip2-opt-6.7b-coco''',
'''blip2-flan-t5-xl''',
'''blip2-flan-t5-xl-coco''',
'''blip2-flan-t5-xxl''',
]
parser.add_argument(
'--model_name',
default='blip2-opt-2.7b',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
_a : str = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 248 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ={}
__lowercase =job["started_at"]
__lowercase =job["completed_at"]
__lowercase =date_parser.parse(_lowerCAmelCase )
__lowercase =date_parser.parse(_lowerCAmelCase )
__lowercase =round((end_datetime - start_datetime).total_seconds() / 60.0 )
__lowercase =start
__lowercase =end
__lowercase =duration_in_min
return job_info
def _A ( _lowerCAmelCase , _lowerCAmelCase=None ):
"""simple docstring"""
__lowercase =None
if token is not None:
__lowercase ={"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}
__lowercase =f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
__lowercase =requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json()
__lowercase ={}
try:
job_time.update({job['name']: extract_time_from_single_job(_lowerCAmelCase ) for job in result['jobs']} )
__lowercase =math.ceil((result['total_count'] - 100) / 100 )
for i in range(_lowerCAmelCase ):
__lowercase =requests.get(url + f"""&page={i + 2}""" , headers=_lowerCAmelCase ).json()
job_time.update({job['name']: extract_time_from_single_job(_lowerCAmelCase ) for job in result['jobs']} )
return job_time
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
lowerCamelCase = parser.parse_args()
lowerCamelCase = get_job_time(args.workflow_run_id)
lowerCamelCase = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f"{k}: {v['duration']}")
| 474 |
'''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 A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''simple docstring'''
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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = 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 _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = 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
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : 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 _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = 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
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, 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
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, 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,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = 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
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, 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
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, 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 _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = 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
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
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 _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
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
snake_case_ : Dict = 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
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
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
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
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
snake_case_ : int = 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
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = 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
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __A ( a_ ):
'''simple docstring'''
__lowerCamelCase : Union[str, Any] = (DPMSolverSinglestepScheduler,)
__lowerCamelCase : Optional[int] = (('num_inference_steps', 25),)
def a__ (self , **A ) -> int:
"""simple docstring"""
_a = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"prediction_type": "epsilon",
"thresholding": False,
"sample_max_value": 1.0,
"algorithm_type": "dpmsolver++",
"solver_type": "midpoint",
"lambda_min_clipped": -float('''inf''' ),
"variance_type": None,
}
config.update(**lowerCAmelCase__ )
return config
def a__ (self , A=0 , **A ) -> Dict:
"""simple docstring"""
_a = dict(self.forward_default_kwargs )
_a = kwargs.pop('''num_inference_steps''' , lowerCAmelCase__ )
_a = self.dummy_sample
_a = 0.1 * sample
_a = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_a = self.get_scheduler_config(**lowerCAmelCase__ )
_a = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
_a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
_a = scheduler_class.from_pretrained(lowerCAmelCase__ )
new_scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals
_a = dummy_past_residuals[: new_scheduler.config.solver_order]
_a = sample, sample
for t in range(lowerCAmelCase__ , time_step + scheduler.config.solver_order + 1 ):
_a = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
_a = 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 a__ (self ) -> Dict:
"""simple docstring"""
pass
def a__ (self , A=0 , **A ) -> List[Any]:
"""simple docstring"""
_a = dict(self.forward_default_kwargs )
_a = kwargs.pop('''num_inference_steps''' , lowerCAmelCase__ )
_a = self.dummy_sample
_a = 0.1 * sample
_a = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_a = self.get_scheduler_config()
_a = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(lowerCAmelCase__ )
# copy over dummy past residuals (must be after setting timesteps)
_a = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowerCAmelCase__ )
_a = 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)
_a = dummy_past_residuals[: new_scheduler.config.solver_order]
_a = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample
_a = 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 a__ (self , A=None , **A ) -> str:
"""simple docstring"""
if scheduler is None:
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(**lowerCAmelCase__ )
_a = scheduler_class(**lowerCAmelCase__ )
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(**lowerCAmelCase__ )
_a = scheduler_class(**lowerCAmelCase__ )
_a = 10
_a = self.dummy_model()
_a = self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_a = model(lowerCAmelCase__ , lowerCAmelCase__ )
_a = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
return sample
def a__ (self ) -> Any:
"""simple docstring"""
_a = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_a = 50
_a = self.dummy_model()
_a = self.dummy_sample_deter
scheduler.set_timesteps(lowerCAmelCase__ )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
_a = model(lowerCAmelCase__ , lowerCAmelCase__ )
_a = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.2574 ) < 1E-3
def a__ (self ) -> int:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
_a = self.full_loop(scheduler=lowerCAmelCase__ )
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
_a = DEISMultistepScheduler.from_config(scheduler.config )
_a = DPMSolverMultistepScheduler.from_config(scheduler.config )
_a = UniPCMultistepScheduler.from_config(scheduler.config )
_a = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_a = self.full_loop(scheduler=lowerCAmelCase__ )
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def a__ (self ) -> List[Any]:
"""simple docstring"""
self.check_over_configs(thresholding=lowerCAmelCase__ )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=lowerCAmelCase__ , prediction_type=lowerCAmelCase__ , sample_max_value=lowerCAmelCase__ , algorithm_type='''dpmsolver++''' , solver_order=lowerCAmelCase__ , solver_type=lowerCAmelCase__ , )
def a__ (self ) -> Dict:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def a__ (self ) -> Tuple:
"""simple docstring"""
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=lowerCAmelCase__ , solver_type=lowerCAmelCase__ , prediction_type=lowerCAmelCase__ , algorithm_type=lowerCAmelCase__ , )
_a = self.full_loop(
solver_order=lowerCAmelCase__ , solver_type=lowerCAmelCase__ , prediction_type=lowerCAmelCase__ , algorithm_type=lowerCAmelCase__ , )
assert not torch.isnan(lowerCAmelCase__ ).any(), "Samples have nan numbers"
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.check_over_configs(lower_order_final=lowerCAmelCase__ )
self.check_over_configs(lower_order_final=lowerCAmelCase__ )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def a__ (self ) -> Dict:
"""simple docstring"""
self.check_over_configs(variance_type=lowerCAmelCase__ )
self.check_over_configs(variance_type='''learned_range''' )
def a__ (self ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=lowerCAmelCase__ , time_step=0 )
def a__ (self ) -> str:
"""simple docstring"""
_a = self.full_loop()
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.2791 ) < 1E-3
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.full_loop(use_karras_sigmas=lowerCAmelCase__ )
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.2248 ) < 1E-3
def a__ (self ) -> List[str]:
"""simple docstring"""
_a = self.full_loop(prediction_type='''v_prediction''' )
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.1453 ) < 1E-3
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = self.full_loop(prediction_type='''v_prediction''' , use_karras_sigmas=lowerCAmelCase__ )
_a = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_mean.item() - 0.0649 ) < 1E-3
def a__ (self ) -> int:
"""simple docstring"""
_a = self.scheduler_classes[0]
_a = self.get_scheduler_config(thresholding=lowerCAmelCase__ , dynamic_thresholding_ratio=0 )
_a = scheduler_class(**lowerCAmelCase__ )
_a = 10
_a = self.dummy_model()
_a = self.dummy_sample_deter.half()
scheduler.set_timesteps(lowerCAmelCase__ )
for i, t in enumerate(scheduler.timesteps ):
_a = model(lowerCAmelCase__ , lowerCAmelCase__ )
_a = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ).prev_sample
assert sample.dtype == torch.floataa
| 11 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
from manim import *
class _SCREAMING_SNAKE_CASE ( a_ ):
'''simple docstring'''
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.25 , width=0.25 )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = Text("CPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(4 )]
SCREAMING_SNAKE_CASE = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = Text("GPU" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
gpu.move_to([-1, -1, 0] )
self.add(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = [mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = Text("Model" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
model.move_to([3, -1.0, 0] )
self.add(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i, rect in enumerate(lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE = fill.copy().set_fill(lowerCAmelCase__ , opacity=0.8 )
target.move_to(lowerCAmelCase__ )
model_arr.append(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(lowerCAmelCase__ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(lowerCAmelCase__ )
self.add(*lowerCAmelCase__ , *lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = [meta_mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = [meta_mem.copy() for i in range(6 )]
SCREAMING_SNAKE_CASE = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(*lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = VGroup(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0 )
SCREAMING_SNAKE_CASE = Text("Disk" , font_size=24 )
SCREAMING_SNAKE_CASE = Group(lowerCAmelCase__ , lowerCAmelCase__ ).arrange(lowerCAmelCase__ , buff=0.5 , aligned_edge=lowerCAmelCase__ )
disk.move_to([-4, -1.25, 0] )
self.add(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCAmelCase__ , lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = MarkupText(
f"<span fgcolor=\'{BLUE}\'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(lowerCAmelCase__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = MarkupText(
f"Now watch as an input is passed through the model\nand how the memory is utilized and handled." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE = Square(0.3 )
input.set_fill(lowerCAmelCase__ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , lowerCAmelCase__ , buff=0.5 )
self.play(Write(lowerCAmelCase__ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=lowerCAmelCase__ , buff=0.02 )
self.play(MoveToTarget(lowerCAmelCase__ ) )
self.play(FadeOut(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE = Arrow(start=lowerCAmelCase__ , end=lowerCAmelCase__ , color=lowerCAmelCase__ , buff=0.5 )
a.next_to(model_arr[0].get_left() , lowerCAmelCase__ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
SCREAMING_SNAKE_CASE = MarkupText(
f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ , run_time=3 ) )
SCREAMING_SNAKE_CASE = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}
self.play(
Write(lowerCAmelCase__ ) , Circumscribe(model_arr[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(model_cpu_arr[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(gpu_rect[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
SCREAMING_SNAKE_CASE = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , lowerCAmelCase__ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
SCREAMING_SNAKE_CASE = AnimationGroup(
FadeOut(lowerCAmelCase__ , run_time=0.5 ) , MoveToTarget(lowerCAmelCase__ , run_time=0.5 ) , FadeIn(lowerCAmelCase__ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(lowerCAmelCase__ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
SCREAMING_SNAKE_CASE = 0.7
self.play(
Circumscribe(model_arr[i] , **lowerCAmelCase__ ) , Circumscribe(cpu_left_col_base[i] , **lowerCAmelCase__ ) , Circumscribe(cpu_left_col_base[i + 1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(gpu_rect[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(model_arr[i + 1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(cpu_left_col_base[-1] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , Circumscribe(gpu_rect[0] , color=lowerCAmelCase__ , **lowerCAmelCase__ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
SCREAMING_SNAKE_CASE = a_c
SCREAMING_SNAKE_CASE = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(lowerCAmelCase__ ) , FadeOut(lowerCAmelCase__ , run_time=0.5 ) , )
SCREAMING_SNAKE_CASE = MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed." , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase__ , run_time=3 ) , MoveToTarget(lowerCAmelCase__ ) )
self.wait() | 16 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Any = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}
class _snake_case ( a_ ):
'''simple docstring'''
__snake_case = "nllb-moe"
__snake_case = ["past_key_values"]
__snake_case = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self: int , __UpperCamelCase: Optional[Any]=12_8112 , __UpperCamelCase: Union[str, Any]=1024 , __UpperCamelCase: Dict=12 , __UpperCamelCase: Union[str, Any]=4096 , __UpperCamelCase: str=16 , __UpperCamelCase: int=12 , __UpperCamelCase: Optional[Any]=4096 , __UpperCamelCase: int=16 , __UpperCamelCase: Optional[int]=0.0_5 , __UpperCamelCase: str=0.0_5 , __UpperCamelCase: Dict=True , __UpperCamelCase: Tuple=True , __UpperCamelCase: str="relu" , __UpperCamelCase: List[Any]=1024 , __UpperCamelCase: Any=0.1 , __UpperCamelCase: List[str]=0.1 , __UpperCamelCase: str=0.0 , __UpperCamelCase: List[str]=0.0_2 , __UpperCamelCase: List[Any]=2 , __UpperCamelCase: Optional[Any]=True , __UpperCamelCase: Any=False , __UpperCamelCase: str="float32" , __UpperCamelCase: Union[str, Any]=False , __UpperCamelCase: List[str]=128 , __UpperCamelCase: int=64 , __UpperCamelCase: Union[str, Any]=4 , __UpperCamelCase: List[str]=4 , __UpperCamelCase: str=0.0_0_1 , __UpperCamelCase: List[Any]=0.0_0_1 , __UpperCamelCase: Any="all" , __UpperCamelCase: Dict=False , __UpperCamelCase: Dict=False , __UpperCamelCase: Optional[Any]=1.0 , __UpperCamelCase: Any=0.2 , __UpperCamelCase: Optional[Any]=1 , __UpperCamelCase: Optional[Any]=0 , __UpperCamelCase: Any=2 , __UpperCamelCase: Optional[int]=False , **__UpperCamelCase: Union[str, Any] , ) -> Any:
__magic_name__ : str = vocab_size
__magic_name__ : Dict = max_position_embeddings
__magic_name__ : Optional[int] = d_model
__magic_name__ : Optional[Any] = encoder_ffn_dim
__magic_name__ : Optional[int] = encoder_layers
__magic_name__ : Union[str, Any] = encoder_attention_heads
__magic_name__ : Tuple = decoder_ffn_dim
__magic_name__ : List[str] = decoder_layers
__magic_name__ : List[str] = decoder_attention_heads
__magic_name__ : str = dropout
__magic_name__ : str = attention_dropout
__magic_name__ : Union[str, Any] = activation_dropout
__magic_name__ : Tuple = activation_function
__magic_name__ : Any = init_std
__magic_name__ : List[str] = encoder_layerdrop
__magic_name__ : Optional[int] = decoder_layerdrop
__magic_name__ : Tuple = use_cache
__magic_name__ : Optional[Any] = encoder_layers
__magic_name__ : Any = scale_embedding # scale factor will be sqrt(d_model) if True
__magic_name__ : Optional[Any] = router_z_loss_coef
__magic_name__ : List[Any] = router_aux_loss_coef
__magic_name__ : Any = decoder_sparse_step
__magic_name__ : Any = encoder_sparse_step
__magic_name__ : List[str] = num_experts
__magic_name__ : Dict = expert_capacity
__magic_name__ : Any = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(f"""`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}""" )
__magic_name__ : int = router_dtype
__magic_name__ : Any = router_ignore_padding_tokens
__magic_name__ : List[Any] = batch_prioritized_routing
__magic_name__ : Tuple = second_expert_policy
__magic_name__ : Tuple = normalize_router_prob_before_dropping
__magic_name__ : Any = moe_eval_capacity_token_fraction
__magic_name__ : Optional[Any] = moe_token_dropout
__magic_name__ : List[Any] = output_router_logits
super().__init__(
pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , is_encoder_decoder=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , ) | 436 |
'''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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
'''allenai/longformer-large-4096-finetuned-triviaqa''': (
'''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
),
'''allenai/longformer-base-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
),
'''allenai/longformer-large-4096-extra.pos.embd.only''': (
'''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
),
}
class A ( a_ ):
UpperCamelCase_ : int ='''longformer'''
def __init__(self , lowerCAmelCase = 5_1_2 , lowerCAmelCase = 2 , lowerCAmelCase = 1 , lowerCAmelCase = 0 , lowerCAmelCase = 2 , lowerCAmelCase = 3_0_5_2_2 , lowerCAmelCase = 7_6_8 , lowerCAmelCase = 1_2 , lowerCAmelCase = 1_2 , lowerCAmelCase = 3_0_7_2 , lowerCAmelCase = "gelu" , lowerCAmelCase = 0.1 , lowerCAmelCase = 0.1 , lowerCAmelCase = 5_1_2 , lowerCAmelCase = 2 , lowerCAmelCase = 0.02 , lowerCAmelCase = 1E-12 , lowerCAmelCase = False , **lowerCAmelCase , ):
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
__lowercase= attention_window
__lowercase= sep_token_id
__lowercase= bos_token_id
__lowercase= eos_token_id
__lowercase= vocab_size
__lowercase= hidden_size
__lowercase= num_hidden_layers
__lowercase= num_attention_heads
__lowercase= hidden_act
__lowercase= intermediate_size
__lowercase= hidden_dropout_prob
__lowercase= attention_probs_dropout_prob
__lowercase= max_position_embeddings
__lowercase= type_vocab_size
__lowercase= initializer_range
__lowercase= layer_norm_eps
__lowercase= onnx_export
class A ( a_ ):
def __init__(self , lowerCAmelCase , lowerCAmelCase = "default" , lowerCAmelCase = None ):
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
__lowercase= True
@property
def _A (self ):
if self.task == "multiple-choice":
__lowercase= {0: "batch", 1: "choice", 2: "sequence"}
else:
__lowercase= {0: "batch", 1: "sequence"}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('global_attention_mask', dynamic_axis),
] )
@property
def _A (self ):
__lowercase= super().outputs
if self.task == "default":
__lowercase= {0: "batch"}
return outputs
@property
def _A (self ):
return 1E-4
@property
def _A (self ):
return max(super().default_onnx_opset , 1_4 )
def _A (self , lowerCAmelCase , lowerCAmelCase = -1 , lowerCAmelCase = -1 , lowerCAmelCase = False , lowerCAmelCase = None , ):
__lowercase= super().generate_dummy_inputs(
preprocessor=lowerCAmelCase__ , batch_size=lowerCAmelCase__ , seq_length=lowerCAmelCase__ , is_pair=lowerCAmelCase__ , framework=lowerCAmelCase__ )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
__lowercase= torch.zeros_like(inputs['input_ids'] )
# make every second token global
__lowercase= 1
return inputs
| 230 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A_( A : str , A : Any , A : Union[str, Any]):
UpperCamelCase = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
UpperCamelCase = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(A):
os.makedirs(A)
UpperCamelCase = model.state_dict()
def to_tf_var_name(A : Union[str, Any]):
for patt, repl in iter(A):
UpperCamelCase = name.replace(A , A)
return f'''bert/{name}'''
def create_tf_var(A : Optional[Any] , A : List[str] , A : List[Any]):
UpperCamelCase = tf.dtypes.as_dtype(tensor.dtype)
UpperCamelCase = tf.get_variable(dtype=A , shape=tensor.shape , name=A , initializer=tf.zeros_initializer())
session.run(tf.variables_initializer([tf_var]))
session.run(A)
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
UpperCamelCase = to_tf_var_name(A)
UpperCamelCase = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose):
UpperCamelCase = torch_tensor.T
UpperCamelCase = create_tf_var(tensor=A , name=A , session=A)
tf.keras.backend.set_value(A , A)
UpperCamelCase = session.run(A)
print(f'''Successfully created {tf_name}: {np.allclose(A , A)}''')
UpperCamelCase = tf.train.Saver(tf.trainable_variables())
saver.save(A , os.path.join(A , model_name.replace('-' , '_') + '.ckpt'))
def A_( A : str=None):
UpperCamelCase = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=A , required=A , help='model name e.g. bert-base-uncased')
parser.add_argument(
'--cache_dir' , type=A , default=A , required=A , help='Directory containing pytorch model')
parser.add_argument('--pytorch_model_path' , type=A , required=A , help='/path/to/<pytorch-model-name>.bin')
parser.add_argument('--tf_cache_dir' , type=A , required=A , help='Directory in which to save tensorflow model')
UpperCamelCase = parser.parse_args(A)
UpperCamelCase = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path) , cache_dir=args.cache_dir , )
convert_pytorch_checkpoint_to_tf(model=A , ckpt_dir=args.tf_cache_dir , model_name=args.model_name)
if __name__ == "__main__":
main()
| 3 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=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 _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
'''simple docstring'''
from string import ascii_uppercase
_UpperCAmelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase : List[str] = dict(enumerate(ascii_uppercase))
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> str:
_UpperCamelCase : Tuple = len(UpperCamelCase )
_UpperCamelCase : str = 0
while True:
if x == i:
_UpperCamelCase : List[str] = 0
if len(UpperCamelCase ) == len(UpperCamelCase ):
break
key += key[i]
i += 1
return key
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> str:
_UpperCamelCase : str = ""
_UpperCamelCase : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
_UpperCamelCase : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> str:
_UpperCamelCase : Dict = ""
_UpperCamelCase : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
_UpperCamelCase : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def snake_case__ ( ) -> None:
_UpperCamelCase : List[str] = "THE GERMAN ATTACK"
_UpperCamelCase : List[str] = "SECRET"
_UpperCamelCase : Optional[int] = generate_key(UpperCamelCase ,UpperCamelCase )
_UpperCamelCase : Any = cipher_text(UpperCamelCase ,UpperCamelCase )
print(f'''Encrypted Text = {s}''' )
print(f'''Original Text = {original_text(UpperCamelCase ,UpperCamelCase )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
A : List[Any] = logging.get_logger(__name__)
A : Dict = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class A (a_ ):
'''simple docstring'''
__lowerCamelCase : List[Any] = '''dpt'''
def __init__( self : Dict , __lowerCAmelCase : Tuple=7_68 , __lowerCAmelCase : Tuple=12 , __lowerCAmelCase : List[Any]=12 , __lowerCAmelCase : Optional[Any]=30_72 , __lowerCAmelCase : Optional[Any]="gelu" , __lowerCAmelCase : str=0.0 , __lowerCAmelCase : List[Any]=0.0 , __lowerCAmelCase : Dict=0.0_2 , __lowerCAmelCase : Dict=1e-12 , __lowerCAmelCase : Optional[Any]=3_84 , __lowerCAmelCase : Dict=16 , __lowerCAmelCase : Optional[Any]=3 , __lowerCAmelCase : List[Any]=False , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Optional[int]=[2, 5, 8, 11] , __lowerCAmelCase : Optional[Any]="project" , __lowerCAmelCase : Dict=[4, 2, 1, 0.5] , __lowerCAmelCase : Dict=[96, 1_92, 3_84, 7_68] , __lowerCAmelCase : int=2_56 , __lowerCAmelCase : int=-1 , __lowerCAmelCase : Union[str, Any]=False , __lowerCAmelCase : str=True , __lowerCAmelCase : str=0.4 , __lowerCAmelCase : Any=2_55 , __lowerCAmelCase : Dict=0.1 , __lowerCAmelCase : str=[1, 10_24, 24, 24] , __lowerCAmelCase : str=[0, 1] , __lowerCAmelCase : Union[str, Any]=None , **__lowerCAmelCase : List[Any] , ) -> Any:
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
A__ = hidden_size
A__ = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("""Initializing the config with a `BiT` backbone.""" )
A__ = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
}
A__ = BitConfig(**lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
logger.info("""Initializing the config with a `BiT` backbone.""" )
A__ = BitConfig(**lowerCAmelCase__ )
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
A__ = backbone_config
else:
raise ValueError(
f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.' )
A__ = backbone_featmap_shape
A__ = neck_ignore_stages
if readout_type != "project":
raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""" )
else:
A__ = None
A__ = None
A__ = []
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = initializer_range
A__ = layer_norm_eps
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = qkv_bias
A__ = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""" )
A__ = readout_type
A__ = reassemble_factors
A__ = neck_hidden_sizes
A__ = fusion_hidden_size
A__ = head_in_index
A__ = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
A__ = use_auxiliary_head
A__ = auxiliary_loss_weight
A__ = semantic_loss_ignore_index
A__ = semantic_classifier_dropout
def a_ ( self : int ) -> Any:
"""simple docstring"""
A__ = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
A__ = self.backbone_config.to_dict()
A__ = self.__class__.model_type
return output
| 176 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Dict:
"""simple docstring"""
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_disk(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
"""simple docstring"""
print('moving disk from' , _SCREAMING_SNAKE_CASE , 'to' , _SCREAMING_SNAKE_CASE )
def __A () ->Tuple:
"""simple docstring"""
lowerCAmelCase__ :Optional[Any] = int(input('Height of hanoi: ' ).strip() )
move_tower(_SCREAMING_SNAKE_CASE , 'A' , 'B' , 'C' )
if __name__ == "__main__":
main()
| 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Any ):
UpperCAmelCase = model.config
UpperCAmelCase = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
UpperCAmelCase = MBartConfig(
is_decoder=SCREAMING_SNAKE_CASE , is_encoder_decoder=SCREAMING_SNAKE_CASE , add_cross_attention=SCREAMING_SNAKE_CASE , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=SCREAMING_SNAKE_CASE , add_final_layer_norm=SCREAMING_SNAKE_CASE , )
return encoder_config, decoder_config
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : int ):
if "encoder.model" in name:
UpperCAmelCase = name.replace('encoder.model' , 'encoder' )
if "decoder.model" in name:
UpperCAmelCase = name.replace('decoder.model' , 'decoder' )
if "patch_embed.proj" in name:
UpperCAmelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
UpperCAmelCase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if name.startswith('encoder' ):
if "layers" in name:
UpperCAmelCase = "encoder." + name
if "attn.proj" in name:
UpperCAmelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name and "mask" not in name:
UpperCAmelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
UpperCAmelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
UpperCAmelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
UpperCAmelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
UpperCAmelCase = name.replace('mlp.fc2' , 'output.dense' )
if name == "encoder.norm.weight":
UpperCAmelCase = "encoder.layernorm.weight"
if name == "encoder.norm.bias":
UpperCAmelCase = "encoder.layernorm.bias"
return name
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Any ):
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(SCREAMING_SNAKE_CASE )
if "qkv" in key:
UpperCAmelCase = key.split('.' )
UpperCAmelCase = int(key_split[3] )
UpperCAmelCase = int(key_split[5] )
UpperCAmelCase = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
UpperCAmelCase = val[:dim, :]
UpperCAmelCase = val[dim : dim * 2, :]
UpperCAmelCase = val[-dim:, :]
else:
UpperCAmelCase = val[:dim]
UpperCAmelCase = val[dim : dim * 2]
UpperCAmelCase = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
UpperCAmelCase = val
return orig_state_dict
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Tuple=None , SCREAMING_SNAKE_CASE : int=False ):
UpperCAmelCase = DonutModel.from_pretrained(SCREAMING_SNAKE_CASE ).eval()
# load HuggingFace model
UpperCAmelCase = get_configs(SCREAMING_SNAKE_CASE )
UpperCAmelCase = DonutSwinModel(SCREAMING_SNAKE_CASE )
UpperCAmelCase = MBartForCausalLM(SCREAMING_SNAKE_CASE )
UpperCAmelCase = VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE , decoder=SCREAMING_SNAKE_CASE )
model.eval()
UpperCAmelCase = original_model.state_dict()
UpperCAmelCase = convert_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
model.load_state_dict(SCREAMING_SNAKE_CASE )
# verify results on scanned document
UpperCAmelCase = load_dataset('hf-internal-testing/example-documents' )
UpperCAmelCase = dataset["test"][0]["image"].convert('RGB' )
UpperCAmelCase = XLMRobertaTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE , from_slow=SCREAMING_SNAKE_CASE )
UpperCAmelCase = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
UpperCAmelCase = DonutProcessor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCAmelCase = processor(SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
UpperCAmelCase = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
UpperCAmelCase = "When is the coffee break?"
UpperCAmelCase = task_prompt.replace('{user_input}' , SCREAMING_SNAKE_CASE )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
UpperCAmelCase = "<s_rvlcdip>"
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
UpperCAmelCase = "<s_cord>"
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
UpperCAmelCase = "s_cord-v2>"
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
UpperCAmelCase = "<s_zhtrainticket>"
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
UpperCAmelCase = "hello world"
else:
raise ValueError('Model name not supported' )
UpperCAmelCase = original_model.decoder.tokenizer(SCREAMING_SNAKE_CASE , add_special_tokens=SCREAMING_SNAKE_CASE , return_tensors='pt' )[
"input_ids"
]
UpperCAmelCase = original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE )
UpperCAmelCase = model.encoder.embeddings(SCREAMING_SNAKE_CASE )
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 )
# verify encoder hidden states
UpperCAmelCase = original_model.encoder(SCREAMING_SNAKE_CASE )
UpperCAmelCase = model.encoder(SCREAMING_SNAKE_CASE ).last_hidden_state
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-2 )
# verify decoder hidden states
UpperCAmelCase = original_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).logits
UpperCAmelCase = model(SCREAMING_SNAKE_CASE , decoder_input_ids=SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1E-3 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE )
processor.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
model.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
processor.push_to_hub('nielsr/' + model_name.split('/' )[-1] , commit_message='Update model' )
if __name__ == "__main__":
_a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='naver-clova-ix/donut-base-finetuned-docvqa',
required=False,
type=str,
help='Name of the original model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
required=False,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub.',
)
_a : Any = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 447 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__lowerCamelCase : Optional[int] = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
'''configuration_xlm_roberta''': [
'''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaConfig''',
'''XLMRobertaOnnxConfig''',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = ['''XLMRobertaTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : str = ['''XLMRobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
'''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMRobertaForCausalLM''',
'''XLMRobertaForMaskedLM''',
'''XLMRobertaForMultipleChoice''',
'''XLMRobertaForQuestionAnswering''',
'''XLMRobertaForSequenceClassification''',
'''XLMRobertaForTokenClassification''',
'''XLMRobertaModel''',
'''XLMRobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = [
'''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMRobertaForCausalLM''',
'''TFXLMRobertaForMaskedLM''',
'''TFXLMRobertaForMultipleChoice''',
'''TFXLMRobertaForQuestionAnswering''',
'''TFXLMRobertaForSequenceClassification''',
'''TFXLMRobertaForTokenClassification''',
'''TFXLMRobertaModel''',
'''TFXLMRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
'''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FlaxXLMRobertaForMaskedLM''',
'''FlaxXLMRobertaForCausalLM''',
'''FlaxXLMRobertaForMultipleChoice''',
'''FlaxXLMRobertaForQuestionAnswering''',
'''FlaxXLMRobertaForSequenceClassification''',
'''FlaxXLMRobertaForTokenClassification''',
'''FlaxXLMRobertaModel''',
'''FlaxXLMRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 248 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
'''simple docstring'''
from __future__ import annotations
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[]
__lowercase =[]
__lowercase =0
__lowercase =sum(_lowerCAmelCase )
create_state_space_tree(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return result
def _A ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
"""simple docstring"""
if sum(_lowerCAmelCase ) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase )) < max_sum:
return
if sum(_lowerCAmelCase ) == max_sum:
result.append(_lowerCAmelCase )
return
for index in range(_lowerCAmelCase , len(_lowerCAmelCase ) ):
create_state_space_tree(
_lowerCAmelCase , _lowerCAmelCase , index + 1 , [*path, nums[index]] , _lowerCAmelCase , remaining_nums_sum - nums[index] , )
lowerCamelCase = [3, 34, 4, 12, 5, 2]
lowerCamelCase = 9
lowerCamelCase = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 474 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowerCAmelCase (__A , __A=7):
"""simple docstring"""
_a = None
if token is not None:
_a = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
_a = "636036"
_a = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
_a = requests.get(__A , headers=__A).json()
return result["workflow_runs"]
def lowerCAmelCase (__A):
"""simple docstring"""
_a = get_daily_ci_runs(__A)
_a = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
_a = workflow_run["id"]
break
return workflow_run_id
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
_a = get_last_daily_ci_runs(__A)
if workflow_run_id is not None:
_a = get_artifacts_links(worflow_run_id=__A , token=__A)
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
_a = artifacts_links[artifact_name]
download_artifact(
artifact_name=__A , artifact_url=__A , output_dir=__A , token=__A)
def lowerCAmelCase (__A , __A , __A):
"""simple docstring"""
get_last_daily_ci_artifacts(__A , __A , __A)
_a = {}
for artifact_name in artifact_names:
_a = os.path.join(__A , F'''{artifact_name}.zip''')
if os.path.isfile(__A):
_a = {}
with zipfile.ZipFile(__A) as z:
for filename in z.namelist():
if not os.path.isdir(__A):
# read the file
with z.open(__A) as f:
_a = f.read().decode('''UTF-8''')
return results
| 11 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : List[str]=13 , __lowerCamelCase : int=7 , __lowerCamelCase : Any=True , __lowerCamelCase : int=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : int=True , __lowerCamelCase : Tuple=99 , __lowerCamelCase : Any=32 , __lowerCamelCase : Optional[Any]=5 , __lowerCamelCase : str=4 , __lowerCamelCase : Tuple=37 , __lowerCamelCase : int="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : int=0.1 , __lowerCamelCase : Optional[Any]=512 , __lowerCamelCase : Optional[int]=16 , __lowerCamelCase : List[str]=2 , __lowerCamelCase : str=0.02 , __lowerCamelCase : Tuple=4 , ):
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_attention_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 = 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 = type_vocab_size
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_choices
def _snake_case ( self : Optional[int] ):
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE = RobertaConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def _snake_case ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
def _snake_case ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class _SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = True
lowerCamelCase__ = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _snake_case ( self : int ):
SCREAMING_SNAKE_CASE = FlaxRobertaModelTester(self )
@slow
def _snake_case ( self : Optional[Any] ):
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("roberta-base" , from_pt=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCAmelCase__ ) | 16 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
def get_matched_characters(UpperCamelCase__ , UpperCamelCase__ ) -> str:
__magic_name__ : int = []
__magic_name__ : Any = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__magic_name__ : Union[str, Any] = int(max(0 , i - limit ) )
__magic_name__ : Optional[Any] = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(UpperCamelCase__ )
__magic_name__ : Tuple = F"""{_stra[0:_stra.index(UpperCamelCase__ )]} {_stra[_stra.index(UpperCamelCase__ ) + 1:]}"""
return "".join(UpperCamelCase__ )
# matching characters
__magic_name__ : List[Any] = get_matched_characters(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ : Tuple = get_matched_characters(UpperCamelCase__ , UpperCamelCase__ )
__magic_name__ : Union[str, Any] = len(UpperCamelCase__ )
# transposition
__magic_name__ : Any = (
len([(ca, ca) for ca, ca in zip(UpperCamelCase__ , UpperCamelCase__ ) if ca != ca] ) // 2
)
if not match_count:
__magic_name__ : Dict = 0.0
else:
__magic_name__ : List[Any] = (
1
/ 3
* (
match_count / len(UpperCamelCase__ )
+ match_count / len(UpperCamelCase__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__magic_name__ : Optional[Any] = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("hello", "world")) | 436 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _lowerCamelCase( lowercase__ ) -> int: # picklable for multiprocessing
'''simple docstring'''
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def _lowerCamelCase( ) -> List[str]:
'''simple docstring'''
with parallel_backend('spark' ):
assert ParallelBackendConfig.backend_name == "spark"
__lowercase= [1, 2, 3]
with pytest.raises(lowercase__ ):
with parallel_backend('unsupported backend' ):
map_nested(lowercase__ , lowercase__ , num_proc=2 )
with pytest.raises(lowercase__ ):
with parallel_backend('unsupported backend' ):
map_nested(lowercase__ , lowercase__ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc' , [2, -1] )
def _lowerCamelCase( lowercase__ ) -> List[Any]:
'''simple docstring'''
__lowercase= [1, 2]
__lowercase= {"a": 1, "b": 2}
__lowercase= {"a": [1, 2], "b": [3, 4]}
__lowercase= {"a": {"1": 1}, "b": 2}
__lowercase= {"a": 1, "b": 2, "c": 3, "d": 4}
__lowercase= [2, 3]
__lowercase= {"a": 2, "b": 3}
__lowercase= {"a": [2, 3], "b": [4, 5]}
__lowercase= {"a": {"1": 2}, "b": 3}
__lowercase= {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend('spark' ):
assert map_nested(lowercase__ , lowercase__ , num_proc=lowercase__ ) == expected_map_nested_sa
assert map_nested(lowercase__ , lowercase__ , num_proc=lowercase__ ) == expected_map_nested_sa
assert map_nested(lowercase__ , lowercase__ , num_proc=lowercase__ ) == expected_map_nested_sa
assert map_nested(lowercase__ , lowercase__ , num_proc=lowercase__ ) == expected_map_nested_sa
assert map_nested(lowercase__ , lowercase__ , num_proc=lowercase__ ) == expected_map_nested_sa
| 230 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
'''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class SCREAMING_SNAKE_CASE__ ( a_):
lowerCAmelCase_ = """vit_msn"""
def __init__( self , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.0 , A_=0.0 , A_=0.02 , A_=1e-06 , A_=224 , A_=16 , A_=3 , A_=True , **A_ , )-> int:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = initializer_range
UpperCamelCase = layer_norm_eps
UpperCamelCase = image_size
UpperCamelCase = patch_size
UpperCamelCase = num_channels
UpperCamelCase = qkv_bias
| 3 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( a_ , a_ , a_ , unittest.TestCase ):
"""simple docstring"""
A__ : List[str] = AltDiffusionPipeline
A__ : List[str] = TEXT_TO_IMAGE_PARAMS
A__ : Dict = TEXT_TO_IMAGE_BATCH_PARAMS
A__ : str = TEXT_TO_IMAGE_IMAGE_PARAMS
A__ : int = TEXT_TO_IMAGE_IMAGE_PARAMS
def _lowercase ( self ) -> List[Any]:
torch.manual_seed(0 )
_UpperCamelCase : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
_UpperCamelCase : str = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=lowerCAmelCase__ , set_alpha_to_one=lowerCAmelCase__ , )
torch.manual_seed(0 )
_UpperCamelCase : str = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
_UpperCamelCase : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
_UpperCamelCase : int = CLIPTextModel(lowerCAmelCase__ )
_UpperCamelCase : Tuple = XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''' )
_UpperCamelCase : str = 77
_UpperCamelCase : Optional[int] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _lowercase ( self , _snake_case , _snake_case=0 ) -> Optional[int]:
if str(lowerCAmelCase__ ).startswith('''mps''' ):
_UpperCamelCase : Tuple = torch.manual_seed(lowerCAmelCase__ )
else:
_UpperCamelCase : Optional[int] = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
_UpperCamelCase : int = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _lowercase ( self ) -> Dict:
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _lowercase ( self ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _lowercase ( self ) -> Optional[Any]:
_UpperCamelCase : List[str] = "cpu" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase : Union[str, Any] = self.get_dummy_components()
torch.manual_seed(0 )
_UpperCamelCase : Union[str, Any] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
_UpperCamelCase : int = RobertaSeriesModelWithTransformation(lowerCAmelCase__ )
_UpperCamelCase : List[Any] = text_encoder
_UpperCamelCase : int = AltDiffusionPipeline(**lowerCAmelCase__ )
_UpperCamelCase : Optional[Any] = alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase : List[Any] = self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCamelCase : int = "A photo of an astronaut"
_UpperCamelCase : List[Any] = alt_pipe(**lowerCAmelCase__ )
_UpperCamelCase : Dict = output.images
_UpperCamelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCamelCase : Optional[int] = np.array(
[0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self ) -> Any:
_UpperCamelCase : List[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
_UpperCamelCase : str = self.get_dummy_components()
_UpperCamelCase : Any = PNDMScheduler(skip_prk_steps=lowerCAmelCase__ )
torch.manual_seed(0 )
_UpperCamelCase : Optional[Any] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
_UpperCamelCase : Dict = RobertaSeriesModelWithTransformation(lowerCAmelCase__ )
_UpperCamelCase : str = text_encoder
_UpperCamelCase : Any = AltDiffusionPipeline(**lowerCAmelCase__ )
_UpperCamelCase : str = alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase : Optional[Any] = self.get_dummy_inputs(lowerCAmelCase__ )
_UpperCamelCase : Tuple = alt_pipe(**lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = output.images
_UpperCamelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCamelCase : List[Any] = np.array(
[0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self ) -> List[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self ) -> Dict:
_UpperCamelCase : Optional[Any] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , safety_checker=lowerCAmelCase__ )
_UpperCamelCase : str = alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase : Tuple = "A painting of a squirrel eating a burger"
_UpperCamelCase : str = torch.manual_seed(0 )
_UpperCamelCase : Optional[Any] = alt_pipe([prompt] , generator=lowerCAmelCase__ , guidance_scale=6.0 , num_inference_steps=20 , output_type='''np''' )
_UpperCamelCase : Union[str, Any] = output.images
_UpperCamelCase : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : Dict = np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowercase ( self ) -> Union[str, Any]:
_UpperCamelCase : Tuple = DDIMScheduler.from_pretrained('''BAAI/AltDiffusion''' , subfolder='''scheduler''' )
_UpperCamelCase : List[str] = AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion''' , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ )
_UpperCamelCase : Optional[Any] = alt_pipe.to(lowerCAmelCase__ )
alt_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
_UpperCamelCase : Union[str, Any] = "A painting of a squirrel eating a burger"
_UpperCamelCase : Optional[int] = torch.manual_seed(0 )
_UpperCamelCase : Tuple = alt_pipe([prompt] , generator=lowerCAmelCase__ , num_inference_steps=2 , output_type='''numpy''' )
_UpperCamelCase : Optional[int] = output.images
_UpperCamelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCamelCase : Optional[Any] = np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 683 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
A : Optional[int] = logging.getLogger(__name__)
A : int = tf.data.AUTOTUNE
def __lowerCamelCase ( ) -> Any:
"""simple docstring"""
A__ = argparse.ArgumentParser(description="""Train a masked language model on TPU.""" )
parser.add_argument(
"""--pretrained_model_config""" , type=__a , default="""roberta-base""" , help="""The model config to use. Note that we don't copy the model's weights, only the config!""" , )
parser.add_argument(
"""--tokenizer""" , type=__a , default="""unigram-tokenizer-wikitext""" , help="""The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.""" , )
parser.add_argument(
"""--per_replica_batch_size""" , type=__a , default=8 , help="""Batch size per TPU core.""" , )
parser.add_argument(
"""--no_tpu""" , action="""store_true""" , help="""If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.""" , )
parser.add_argument(
"""--tpu_name""" , type=__a , help="""Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.""" , default="""local""" , )
parser.add_argument(
"""--tpu_zone""" , type=__a , help="""Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.""" , )
parser.add_argument(
"""--gcp_project""" , type=__a , help="""Google cloud project name. Only used for non-Colab TPU nodes.""" )
parser.add_argument(
"""--bfloat16""" , action="""store_true""" , help="""Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.""" , )
parser.add_argument(
"""--train_dataset""" , type=__a , help="""Path to training dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--shuffle_buffer_size""" , type=__a , default=2**1_8 , help="""Size of the shuffle buffer (in samples)""" , )
parser.add_argument(
"""--eval_dataset""" , type=__a , help="""Path to evaluation dataset to load. If the path begins with `gs://`"""
""" then the dataset will be loaded from a Google Cloud Storage bucket.""" , )
parser.add_argument(
"""--num_epochs""" , type=__a , default=1 , help="""Number of epochs to train for.""" , )
parser.add_argument(
"""--learning_rate""" , type=__a , default=1E-4 , help="""Learning rate to use for training.""" , )
parser.add_argument(
"""--weight_decay_rate""" , type=__a , default=1E-3 , help="""Weight decay rate to use for training.""" , )
parser.add_argument(
"""--max_length""" , type=__a , default=5_1_2 , help="""Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py""" , )
parser.add_argument(
"""--mlm_probability""" , type=__a , default=0.15 , help="""Fraction of tokens to mask during training.""" , )
parser.add_argument("""--output_dir""" , type=__a , required=__a , help="""Path to save model checkpoints to.""" )
parser.add_argument("""--hub_model_id""" , type=__a , help="""Model ID to upload to on the Hugging Face Hub.""" )
A__ = parser.parse_args()
return args
def __lowerCamelCase ( __a :Union[str, Any] ) -> Tuple:
"""simple docstring"""
try:
if args.tpu_name:
A__ = tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
A__ = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
"""Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or """
"""--gcp_project. When running on a TPU VM, use --tpu_name local.""" )
tf.config.experimental_connect_to_cluster(__a )
tf.tpu.experimental.initialize_tpu_system(__a )
return tpu
def __lowerCamelCase ( __a :Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
A__ = 0
for file in file_list:
A__ = file.split("""/""" )[-1]
A__ = re.search(R"""-\d+-(\d+)\.tfrecord""" , __a ).group(1 )
A__ = int(__a )
num_samples += sample_count
return num_samples
def __lowerCamelCase ( __a :List[Any] , __a :Optional[Any] , __a :Dict , __a :Union[str, Any] , __a :List[str] , __a :Optional[Any]=None ) -> Tuple:
"""simple docstring"""
A__ = count_samples(__a )
A__ = tf.data.Dataset.from_tensor_slices(__a )
if shuffle:
A__ = dataset.shuffle(len(__a ) )
A__ = tf.data.TFRecordDataset(__a , num_parallel_reads=__a )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
A__ = dataset.apply(tf.data.experimental.assert_cardinality(__a ) )
A__ = dataset.map(__a , num_parallel_calls=__a )
if shuffle:
assert shuffle_buffer_size is not None
A__ = dataset.shuffle(args.shuffle_buffer_size )
A__ = dataset.batch(__a , drop_remainder=__a )
A__ = dataset.map(__a , num_parallel_calls=__a )
A__ = dataset.prefetch(__a )
return dataset
def __lowerCamelCase ( __a :List[Any] ) -> Tuple:
"""simple docstring"""
if not args.no_tpu:
A__ = initialize_tpu(__a )
A__ = tf.distribute.TPUStrategy(__a )
else:
A__ = tf.distribute.OneDeviceStrategy(device="""/gpu:0""" )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16""" )
A__ = AutoTokenizer.from_pretrained(args.tokenizer )
A__ = AutoConfig.from_pretrained(args.pretrained_model_config )
A__ = tokenizer.vocab_size
A__ = tf.io.gfile.glob(os.path.join(args.train_dataset , """*.tfrecord""" ) )
if not training_records:
raise ValueError(F'No .tfrecord files found in {args.train_dataset}.' )
A__ = tf.io.gfile.glob(os.path.join(args.eval_dataset , """*.tfrecord""" ) )
if not eval_records:
raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.' )
A__ = count_samples(__a )
A__ = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
A__ = steps_per_epoch * args.num_epochs
with strategy.scope():
A__ = TFAutoModelForMaskedLM.from_config(__a )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
A__ = create_optimizer(
num_train_steps=__a , num_warmup_steps=total_train_steps // 2_0 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=__a , metrics=["""accuracy"""] )
def decode_fn(__a :Optional[int] ):
A__ = {
"input_ids": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
"attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(__a , __a )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
A__ = DataCollatorForLanguageModeling(
tokenizer=__a , mlm_probability=args.mlm_probability , mlm=__a , return_tensors="""tf""" )
def mask_with_collator(__a :int ):
# TF really needs an isin() function
A__ = (
~tf.cast(batch["""attention_mask"""] , tf.bool )
| (batch["input_ids"] == tokenizer.cls_token_id)
| (batch["input_ids"] == tokenizer.sep_token_id)
)
A__ = data_collator.tf_mask_tokens(
batch["""input_ids"""] , vocab_size=len(__a ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=__a , )
return batch
A__ = args.per_replica_batch_size * strategy.num_replicas_in_sync
A__ = prepare_dataset(
__a , decode_fn=__a , mask_fn=__a , batch_size=__a , shuffle=__a , shuffle_buffer_size=args.shuffle_buffer_size , )
A__ = prepare_dataset(
__a , decode_fn=__a , mask_fn=__a , batch_size=__a , shuffle=__a , )
A__ = []
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=__a ) )
model.fit(
__a , validation_data=__a , epochs=args.num_epochs , callbacks=__a , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
A : Tuple = parse_args()
main(args)
| 176 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance < 0:
raise ValueError('Resistance cannot be negative' )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 93 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
_a : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
class lowercase_ ( a_ ):
'''simple docstring'''
def __init__( self , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
if hasattr(scheduler.config , 'steps_offset' ) and scheduler.config.steps_offset != 1:
UpperCAmelCase = (
F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
"to update the config accordingly as leaving `steps_offset` might led to incorrect results"
" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
" file"
)
deprecate('steps_offset!=1' , '1.0.0' , lowerCAmelCase__ , standard_warn=lowerCAmelCase__ )
UpperCAmelCase = dict(scheduler.config )
UpperCAmelCase = 1
UpperCAmelCase = FrozenDict(lowerCAmelCase__ )
if hasattr(scheduler.config , 'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
UpperCAmelCase = (
F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"
" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"
" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"
" Hub, it would be very nice if you could open a Pull request for the"
" `scheduler/scheduler_config.json` file"
)
deprecate('skip_prk_steps not set' , '1.0.0' , lowerCAmelCase__ , standard_warn=lowerCAmelCase__ )
UpperCAmelCase = dict(scheduler.config )
UpperCAmelCase = True
UpperCAmelCase = FrozenDict(lowerCAmelCase__ )
if safety_checker is None:
logger.warning(
F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
' results in services or applications open to the public. Both the diffusers team and Hugging Face'
' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )
self.register_modules(
segmentation_model=lowerCAmelCase__ , segmentation_processor=lowerCAmelCase__ , vae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , safety_checker=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , )
def snake_case_ ( self , a_ = "auto" ) -> List[Any]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
UpperCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCAmelCase__ )
def snake_case_ ( self ) -> Tuple:
"""simple docstring"""
self.enable_attention_slicing(lowerCAmelCase__ )
def snake_case_ ( self ) -> Union[str, Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
UpperCAmelCase = torch.device('cuda' )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase__ , lowerCAmelCase__ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def snake_case_ ( self ) -> List[str]:
"""simple docstring"""
if self.device != torch.device('meta' ) or 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()
def __call__( self , a_ , a_ , a_ , a_ = 5_1_2 , a_ = 5_1_2 , a_ = 5_0 , a_ = 7.5 , a_ = None , a_ = 1 , a_ = 0.0 , a_ = None , a_ = None , a_ = "pil" , a_ = True , a_ = None , a_ = 1 , **a_ , ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = self.segmentation_processor(
text=[text] , images=[image] , padding='max_length' , return_tensors='pt' ).to(self.device )
UpperCAmelCase = self.segmentation_model(**lowerCAmelCase__ )
UpperCAmelCase = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
UpperCAmelCase = self.numpy_to_pil(lowerCAmelCase__ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
UpperCAmelCase = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=lowerCAmelCase__ , image=lowerCAmelCase__ , mask_image=lowerCAmelCase__ , height=lowerCAmelCase__ , width=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , negative_prompt=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , latents=lowerCAmelCase__ , output_type=lowerCAmelCase__ , return_dict=lowerCAmelCase__ , callback=lowerCAmelCase__ , callback_steps=lowerCAmelCase__ , )
| 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'
)
__UpperCamelCase : Any = None
__UpperCamelCase : int = {
'''7B''': 1_1008,
'''13B''': 1_3824,
'''30B''': 1_7920,
'''65B''': 2_2016,
'''70B''': 2_8672,
}
__UpperCamelCase : List[str] = {
'''7B''': 1,
'''7Bf''': 1,
'''13B''': 2,
'''13Bf''': 2,
'''30B''': 4,
'''65B''': 8,
'''70B''': 8,
'''70Bf''': 8,
}
def A ( _lowercase , _lowercase=1 , _lowercase=256 ):
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A ( _lowercase ):
with open(_lowercase , '''r''' ) as f:
return json.load(_lowercase )
def A ( _lowercase , _lowercase ):
with open(_lowercase , '''w''' ) as f:
json.dump(_lowercase , _lowercase )
def A ( _lowercase , _lowercase , _lowercase , _lowercase=True ):
os.makedirs(_lowercase , exist_ok=_lowercase )
SCREAMING_SNAKE_CASE : List[Any] = os.path.join(_lowercase , '''tmp''' )
os.makedirs(_lowercase , exist_ok=_lowercase )
SCREAMING_SNAKE_CASE : List[str] = read_json(os.path.join(_lowercase , '''params.json''' ) )
SCREAMING_SNAKE_CASE : Optional[Any] = NUM_SHARDS[model_size]
SCREAMING_SNAKE_CASE : Optional[Any] = params["n_layers"]
SCREAMING_SNAKE_CASE : Tuple = params["n_heads"]
SCREAMING_SNAKE_CASE : int = n_heads // num_shards
SCREAMING_SNAKE_CASE : Any = params["dim"]
SCREAMING_SNAKE_CASE : List[Any] = dim // n_heads
SCREAMING_SNAKE_CASE : List[Any] = 10_000.0
SCREAMING_SNAKE_CASE : int = 1.0 / (base ** (torch.arange(0 , _lowercase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
SCREAMING_SNAKE_CASE : Union[str, Any] = params["n_kv_heads"] # for GQA / MQA
SCREAMING_SNAKE_CASE : Dict = n_heads_per_shard // num_key_value_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = dim // num_key_value_heads
else: # compatibility with other checkpoints
SCREAMING_SNAKE_CASE : Dict = n_heads
SCREAMING_SNAKE_CASE : Any = n_heads_per_shard
SCREAMING_SNAKE_CASE : Optional[Any] = dim
# permute for sliced rotary
def permute(_lowercase , _lowercase=n_heads , _lowercase=dim , _lowercase=dim ):
return w.view(_lowercase , dima // n_heads // 2 , 2 , _lowercase ).transpose(1 , 2 ).reshape(_lowercase , _lowercase )
print(f"""Fetching all parameters from the checkpoint at {input_base_path}.""" )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
SCREAMING_SNAKE_CASE : Dict = torch.load(os.path.join(_lowercase , '''consolidated.00.pth''' ) , map_location='''cpu''' )
else:
# Sharded
SCREAMING_SNAKE_CASE : List[Any] = [
torch.load(os.path.join(_lowercase , f"""consolidated.{i:02d}.pth""" ) , map_location='''cpu''' )
for i in range(_lowercase )
]
SCREAMING_SNAKE_CASE : List[str] = 0
SCREAMING_SNAKE_CASE : Any = {"weight_map": {}}
for layer_i in range(_lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
SCREAMING_SNAKE_CASE : Any = {
f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
loaded[f"""layers.{layer_i}.attention.wq.weight"""] ),
f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
loaded[f"""layers.{layer_i}.attention.wk.weight"""] ),
f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""],
f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""],
f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""],
f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""],
f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""],
f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""],
f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
SCREAMING_SNAKE_CASE : List[str] = {
f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
f"""layers.{layer_i}.attention_norm.weight"""
].clone(),
f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
f"""layers.{layer_i}.ffn_norm.weight"""
].clone(),
}
SCREAMING_SNAKE_CASE : List[Any] = permute(
torch.cat(
[
loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowercase , _lowercase , _lowercase )
for i in range(_lowercase )
] , dim=0 , ).reshape(_lowercase , _lowercase ) )
SCREAMING_SNAKE_CASE : Tuple = permute(
torch.cat(
[
loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
_lowercase , _lowercase , _lowercase )
for i in range(_lowercase )
] , dim=0 , ).reshape(_lowercase , _lowercase ) , _lowercase , _lowercase , _lowercase , )
SCREAMING_SNAKE_CASE : Dict = torch.cat(
[
loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
_lowercase , _lowercase , _lowercase )
for i in range(_lowercase )
] , dim=0 , ).reshape(_lowercase , _lowercase )
SCREAMING_SNAKE_CASE : List[Any] = torch.cat(
[loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowercase )] , dim=1 )
SCREAMING_SNAKE_CASE : int = torch.cat(
[loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowercase )] , dim=0 )
SCREAMING_SNAKE_CASE : Any = torch.cat(
[loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowercase )] , dim=1 )
SCREAMING_SNAKE_CASE : int = torch.cat(
[loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowercase )] , dim=0 )
SCREAMING_SNAKE_CASE : Union[str, Any] = inv_freq
for k, v in state_dict.items():
SCREAMING_SNAKE_CASE : Optional[Any] = filename
param_count += v.numel()
torch.save(_lowercase , os.path.join(_lowercase , _lowercase ) )
SCREAMING_SNAKE_CASE : List[str] = f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
SCREAMING_SNAKE_CASE : Optional[int] = {
"model.embed_tokens.weight": loaded["tok_embeddings.weight"],
"model.norm.weight": loaded["norm.weight"],
"lm_head.weight": loaded["output.weight"],
}
else:
SCREAMING_SNAKE_CASE : Optional[int] = {
"model.norm.weight": loaded[0]["norm.weight"],
"model.embed_tokens.weight": torch.cat(
[loaded[i]['''tok_embeddings.weight'''] for i in range(_lowercase )] , dim=1 ),
"lm_head.weight": torch.cat([loaded[i]['''output.weight'''] for i in range(_lowercase )] , dim=0 ),
}
for k, v in state_dict.items():
SCREAMING_SNAKE_CASE : Dict = filename
param_count += v.numel()
torch.save(_lowercase , os.path.join(_lowercase , _lowercase ) )
# Write configs
SCREAMING_SNAKE_CASE : List[Any] = {"total_size": param_count * 2}
write_json(_lowercase , os.path.join(_lowercase , '''pytorch_model.bin.index.json''' ) )
SCREAMING_SNAKE_CASE : Tuple = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
SCREAMING_SNAKE_CASE : Tuple = params["multiple_of"] if "multiple_of" in params else 256
SCREAMING_SNAKE_CASE : Tuple = LlamaConfig(
hidden_size=_lowercase , intermediate_size=compute_intermediate_size(_lowercase , _lowercase , _lowercase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=_lowercase , )
config.save_pretrained(_lowercase )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print('''Loading the checkpoint in a Llama model.''' )
SCREAMING_SNAKE_CASE : Union[str, Any] = LlamaForCausalLM.from_pretrained(_lowercase , torch_dtype=torch.floataa , low_cpu_mem_usage=_lowercase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print('''Saving in the Transformers format.''' )
model.save_pretrained(_lowercase , safe_serialization=_lowercase )
shutil.rmtree(_lowercase )
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : int = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" )
SCREAMING_SNAKE_CASE : List[str] = tokenizer_class(_lowercase )
tokenizer.save_pretrained(_lowercase )
def A ( ):
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--input_dir''' , help='''Location of LLaMA weights, which contains tokenizer.model and model folders''' , )
parser.add_argument(
'''--model_size''' , choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only'''] , )
parser.add_argument(
'''--output_dir''' , help='''Location to write HF model and tokenizer''' , )
parser.add_argument('''--safe_serialization''' , type=_lowercase , help='''Whether or not to save using `safetensors`.''' )
SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
SCREAMING_SNAKE_CASE : Tuple = os.path.join(args.input_dir , '''tokenizer.model''' )
write_tokenizer(args.output_dir , _lowercase )
if __name__ == "__main__":
main()
| 248 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__lowercase =tempfile.mkdtemp()
# fmt: off
__lowercase =["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
__lowercase =dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__))))
__lowercase =["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
__lowercase ={"unk_token": "<unk>"}
__lowercase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'])
__lowercase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'])
with open(self.vocab_file , 'w' , encoding='utf-8') as fp:
fp.write(json.dumps(lowerCAmelCase__) + '\n')
with open(self.merges_file , 'w' , encoding='utf-8') as fp:
fp.write('\n'.join(lowerCAmelCase__))
__lowercase ={
"do_resize": True,
"size": 2_0,
"do_center_crop": True,
"crop_size": 1_8,
"do_normalize": True,
"image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
"image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
__lowercase =os.path.join(self.tmpdirname , lowerCAmelCase__)
with open(self.image_processor_file , 'w' , encoding='utf-8') as fp:
json.dump(lowerCAmelCase__ , lowerCAmelCase__)
def __lowerCamelCase ( self : Union[str, Any] , **_lowerCAmelCase : int):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__)
def __lowerCamelCase ( self : Any , **_lowerCAmelCase : str):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__)
def __lowerCamelCase ( self : List[str] , **_lowerCAmelCase : List[Any]):
'''simple docstring'''
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase__)
def __lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__lowercase =[np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta)]
__lowercase =[Image.fromarray(np.moveaxis(lowerCAmelCase__ , 0 , -1)) for x in image_inputs]
return image_inputs
def __lowerCamelCase ( self : List[str]):
'''simple docstring'''
__lowercase =self.get_tokenizer()
__lowercase =self.get_rust_tokenizer()
__lowercase =self.get_image_processor()
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
processor_slow.save_pretrained(self.tmpdirname)
__lowercase =CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCAmelCase__)
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
processor_fast.save_pretrained(self.tmpdirname)
__lowercase =CLIPProcessor.from_pretrained(self.tmpdirname)
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab())
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab())
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab())
self.assertIsInstance(processor_slow.tokenizer , lowerCAmelCase__)
self.assertIsInstance(processor_fast.tokenizer , lowerCAmelCase__)
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string())
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string())
self.assertIsInstance(processor_slow.image_processor , lowerCAmelCase__)
self.assertIsInstance(processor_fast.image_processor , lowerCAmelCase__)
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
__lowercase =self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)')
__lowercase =self.get_image_processor(do_normalize=lowerCAmelCase__ , padding_value=1.0)
__lowercase =CLIPProcessor.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 , lowerCAmelCase__)
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string())
self.assertIsInstance(processor.image_processor , lowerCAmelCase__)
def __lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__lowercase =self.get_image_processor()
__lowercase =self.get_tokenizer()
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
__lowercase =self.prepare_image_inputs()
__lowercase =image_processor(lowerCAmelCase__ , return_tensors='np')
__lowercase =processor(images=lowerCAmelCase__ , return_tensors='np')
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2)
def __lowerCamelCase ( self : str):
'''simple docstring'''
__lowercase =self.get_image_processor()
__lowercase =self.get_tokenizer()
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
__lowercase ="lower newer"
__lowercase =processor(text=lowerCAmelCase__)
__lowercase =tokenizer(lowerCAmelCase__)
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key])
def __lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__lowercase =self.get_image_processor()
__lowercase =self.get_tokenizer()
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
__lowercase ="lower newer"
__lowercase =self.prepare_image_inputs()
__lowercase =processor(text=lowerCAmelCase__ , images=lowerCAmelCase__)
self.assertListEqual(list(inputs.keys()) , ['input_ids', 'attention_mask', 'pixel_values'])
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase__):
processor()
def __lowerCamelCase ( self : int):
'''simple docstring'''
__lowercase =self.get_image_processor()
__lowercase =self.get_tokenizer()
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
__lowercase =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowercase =processor.batch_decode(lowerCAmelCase__)
__lowercase =tokenizer.batch_decode(lowerCAmelCase__)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__)
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase =self.get_image_processor()
__lowercase =self.get_tokenizer()
__lowercase =CLIPProcessor(tokenizer=lowerCAmelCase__ , image_processor=lowerCAmelCase__)
__lowercase ="lower newer"
__lowercase =self.prepare_image_inputs()
__lowercase =processor(text=lowerCAmelCase__ , images=lowerCAmelCase__)
self.assertListEqual(list(inputs.keys()) , processor.model_input_names)
| 474 |
'''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 A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''simple docstring'''
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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = 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 _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = 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
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : 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 _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = 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
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, 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
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, 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,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = 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
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, 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
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, 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 _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = 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
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
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 _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
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
snake_case_ : Dict = 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
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
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
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
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
snake_case_ : int = 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
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = 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
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __A ( unittest.TestCase ):
'''simple docstring'''
def a__ (self ) -> List[Any]:
"""simple docstring"""
_a = 10
def a__ (self ) -> int:
"""simple docstring"""
_a = [1, 2, 3, 4]
_a = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(lowerCAmelCase__ , self.block_size , 0 ) , lowerCAmelCase__ )
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(lowerCAmelCase__ , self.block_size , 0 ) , lowerCAmelCase__ )
def a__ (self ) -> Dict:
"""simple docstring"""
_a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(lowerCAmelCase__ , self.block_size , 0 ) , lowerCAmelCase__ )
def a__ (self ) -> Optional[Any]:
"""simple docstring"""
_a = "It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this."
_a = process_story(lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , [] )
def a__ (self ) -> int:
"""simple docstring"""
_a = ""
_a = process_story(lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , [] )
self.assertEqual(lowerCAmelCase__ , [] )
def a__ (self ) -> int:
"""simple docstring"""
_a = (
"It was the year of Our Lord one thousand seven hundred and "
"seventy-five\n\nSpiritual revelations were conceded to England "
"at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
)
_a = process_story(lowerCAmelCase__ )
_a = [
"It was the year of Our Lord one thousand seven hundred and seventy-five.",
"Spiritual revelations were conceded to England at that favoured period, as at this.",
]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
_a = ["It was the best of times."]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = torch.tensor([1, 2, 3, 4] )
_a = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(lowerCAmelCase__ , 0 ).numpy() , expected.numpy() )
def a__ (self ) -> int:
"""simple docstring"""
_a = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_a = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(lowerCAmelCase__ , 23 ).numpy() , expected.numpy() )
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_a = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(lowerCAmelCase__ , 1 ).numpy() , expected.numpy() )
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = 101
_a = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_a = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_a = compute_token_type_ids(lowerCAmelCase__ , lowerCAmelCase__ )
np.testing.assert_array_equal(lowerCAmelCase__ , lowerCAmelCase__ )
| 11 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
def __a ( A__ : Optional[Any] ):
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty string was passed to the function" )
SCREAMING_SNAKE_CASE = ""
while len(A__ ) % 3 != 0:
SCREAMING_SNAKE_CASE = "0" + bin_string
SCREAMING_SNAKE_CASE = [
bin_string[index : index + 3]
for index in range(len(A__ ) )
if index % 3 == 0
]
for bin_group in bin_string_in_3_list:
SCREAMING_SNAKE_CASE = 0
for index, val in enumerate(A__ ):
oct_val += int(2 ** (2 - index) * int(A__ ) )
oct_string += str(A__ )
return oct_string
if __name__ == "__main__":
from doctest import testmod
testmod() | 16 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod() | 436 |
'''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 torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class A_ (a_ ):
"""simple docstring"""
a__ = '''facebook/bart-large-mnli'''
a__ = (
'''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
'''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
'''It returns the most likely label in the list of provided `labels` for the input text.'''
)
a__ = '''text_classifier'''
a__ = AutoTokenizer
a__ = AutoModelForSequenceClassification
a__ = ['''text''', ['''text''']]
a__ = ['''text''']
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
super().setup()
snake_case_ : Optional[int] = self.model.config
snake_case_ : Any = -1
for idx, label in config.idalabel.items():
if label.lower().startswith("entail" ):
snake_case_ : Union[str, Any] = int(lowerCAmelCase__ )
if self.entailment_id == -1:
raise ValueError("Could not determine the entailment ID from the model config, please pass it at init." )
def _A ( self :Dict , lowerCAmelCase__ :int , lowerCAmelCase__ :Tuple ) -> int:
'''simple docstring'''
snake_case_ : Tuple = labels
return self.pre_processor(
[text] * len(lowerCAmelCase__ ) , [F'''This example is {label}''' for label in labels] , return_tensors="pt" , padding="max_length" , )
def _A ( self :Any , lowerCAmelCase__ :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Optional[int] = outputs.logits
snake_case_ : Tuple = torch.argmax(logits[:, 2] ).item()
return self._labels[label_id]
| 653 | 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
lowerCAmelCase = logging.get_logger(__name__)
def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ , lowercase__ ) -> Tuple[int, int]:
'''simple docstring'''
def constraint_to_multiple_of(lowercase__ , lowercase__ , lowercase__=0 , lowercase__=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(lowercase__ , lowercase__ ) else output_size
__lowercase= get_image_size(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=lowercase__ )
__lowercase= constraint_to_multiple_of(scale_width * input_width , multiple=lowercase__ )
return (new_height, new_width)
class A ( a_ ):
UpperCamelCase_ : List[str] =['''pixel_values''']
def __init__(self , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = PILImageResampling.BILINEAR , lowerCAmelCase = False , lowerCAmelCase = 1 , lowerCAmelCase = True , lowerCAmelCase = 1 / 2_5_5 , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = None , **lowerCAmelCase , ):
super().__init__(**lowerCAmelCase__ )
__lowercase= size if size is not None else {"height": 3_8_4, "width": 3_8_4}
__lowercase= get_size_dict(lowerCAmelCase__ )
__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 _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = False , lowerCAmelCase = 1 , lowerCAmelCase = PILImageResampling.BICUBIC , lowerCAmelCase = None , **lowerCAmelCase , ):
__lowercase= get_size_dict(lowerCAmelCase__ )
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(
lowerCAmelCase__ , output_size=(size['height'], size['width']) , keep_aspect_ratio=lowerCAmelCase__ , multiple=lowerCAmelCase__ , )
return resize(lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , **lowerCAmelCase , ):
return rescale(lowerCAmelCase__ , scale=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , **lowerCAmelCase , ):
return normalize(lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ , data_format=lowerCAmelCase__ , **lowerCAmelCase__ )
def _A (self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = ChannelDimension.FIRST , **lowerCAmelCase , ):
__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(lowerCAmelCase__ )
__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(lowerCAmelCase__ )
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.' )
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(lowerCAmelCase__ ) for image in images]
if do_resize:
__lowercase= [self.resize(image=lowerCAmelCase__ , size=lowerCAmelCase__ , resample=lowerCAmelCase__ ) for image in images]
if do_rescale:
__lowercase= [self.rescale(image=lowerCAmelCase__ , scale=lowerCAmelCase__ ) for image in images]
if do_normalize:
__lowercase= [self.normalize(image=lowerCAmelCase__ , mean=lowerCAmelCase__ , std=lowerCAmelCase__ ) for image in images]
__lowercase= [to_channel_dimension_format(lowerCAmelCase__ , lowerCAmelCase__ ) for image in images]
__lowercase= {"pixel_values": images}
return BatchFeature(data=lowerCAmelCase__ , tensor_type=lowerCAmelCase__ )
def _A (self , lowerCAmelCase , lowerCAmelCase = None ):
__lowercase= outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits' )
if is_torch_tensor(lowerCAmelCase__ ):
__lowercase= target_sizes.numpy()
__lowercase= []
for idx in range(len(lowerCAmelCase__ ) ):
__lowercase= torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='bilinear' , align_corners=lowerCAmelCase__ )
__lowercase= resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(lowerCAmelCase__ )
else:
__lowercase= logits.argmax(dim=1 )
__lowercase= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 230 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : Any = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''ViTFeatureExtractor''']
__lowerCamelCase : Any = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Optional[Any] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Tuple = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def A_( A : Any , A : List[Any]):
UpperCamelCase = int(A)
assert noofclusters < len(A)
# Find out the dimensionality
UpperCamelCase = len(vectors[0])
# Will help select random centroids from among the available vectors
UpperCamelCase = list(range(len(A)))
shuffle(A)
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
UpperCamelCase = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
UpperCamelCase = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
UpperCamelCase = [
tf.Variable(vectors[vector_indices[i]]) for i in range(A)
]
##These nodes will assign the centroid Variables the appropriate
##values
UpperCamelCase = tf.placeholder('float64' , [dim])
UpperCamelCase = []
for centroid in centroids:
cent_assigns.append(tf.assign(A , A))
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
UpperCamelCase = [tf.Variable(0) for i in range(len(A))]
##These nodes will assign an assignment Variable the appropriate
##value
UpperCamelCase = tf.placeholder('int32')
UpperCamelCase = []
for assignment in assignments:
cluster_assigns.append(tf.assign(A , A))
##Now lets construct the node that will compute the mean
# The placeholder for the input
UpperCamelCase = tf.placeholder('float' , [None, dim])
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
UpperCamelCase = tf.reduce_mean(A , 0)
##Node for computing Euclidean distances
# Placeholders for input
UpperCamelCase = tf.placeholder('float' , [dim])
UpperCamelCase = tf.placeholder('float' , [dim])
UpperCamelCase = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(A , A) , 2)))
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
UpperCamelCase = tf.placeholder('float' , [noofclusters])
UpperCamelCase = tf.argmin(A , 0)
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
UpperCamelCase = tf.initialize_all_variables()
# Initialize all variables
sess.run(A)
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
UpperCamelCase = 100
for _ in range(A):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(A)):
UpperCamelCase = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
UpperCamelCase = [
sess.run(A , feed_dict={va: vect, va: sess.run(A)})
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
UpperCamelCase = sess.run(
A , feed_dict={centroid_distances: distances})
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment})
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(A):
# Collect all the vectors assigned to this cluster
UpperCamelCase = [
vectors[i]
for i in range(len(A))
if sess.run(assignments[i]) == cluster_n
]
# Compute new centroid location
UpperCamelCase = sess.run(
A , feed_dict={mean_input: array(A)})
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location})
# Return centroids and assignments
UpperCamelCase = sess.run(A)
UpperCamelCase = sess.run(A)
return centroids, assignments
| 3 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A_ :
"""simple docstring"""
def __init__( self :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=2 , lowerCAmelCase__ :List[Any]=3 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :List[str]=7 , lowerCAmelCase__ :Any=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Optional[int]=True , lowerCAmelCase__ :List[str]=99 , lowerCAmelCase__ :Union[str, Any]=36 , lowerCAmelCase__ :Dict=3 , lowerCAmelCase__ :str=4 , lowerCAmelCase__ :Optional[int]=37 , lowerCAmelCase__ :Dict="gelu" , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=512 , lowerCAmelCase__ :Union[str, Any]=16 , lowerCAmelCase__ :List[Any]=2 , lowerCAmelCase__ :Any=0.0_2 , lowerCAmelCase__ :Dict=6 , lowerCAmelCase__ :Optional[int]=6 , lowerCAmelCase__ :Any=3 , lowerCAmelCase__ :int=4 , lowerCAmelCase__ :int=None , lowerCAmelCase__ :Any=1_000 , ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[int] = num_channels
snake_case_ : List[Any] = image_size
snake_case_ : Optional[int] = patch_size
snake_case_ : Union[str, Any] = text_seq_length
snake_case_ : Dict = is_training
snake_case_ : Optional[Any] = use_input_mask
snake_case_ : Union[str, Any] = use_token_type_ids
snake_case_ : Dict = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Optional[Any] = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : str = hidden_act
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[Any] = type_vocab_size
snake_case_ : Union[str, Any] = type_sequence_label_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Union[str, Any] = coordinate_size
snake_case_ : int = shape_size
snake_case_ : Tuple = num_labels
snake_case_ : List[Any] = num_choices
snake_case_ : List[str] = scope
snake_case_ : Dict = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
snake_case_ : str = text_seq_length
snake_case_ : Optional[int] = (image_size // patch_size) ** 2 + 1
snake_case_ : str = self.text_seq_length + self.image_seq_length
def _A ( self :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
snake_case_ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
snake_case_ : Optional[Any] = bbox[i, j, 3]
snake_case_ : Any = bbox[i, j, 1]
snake_case_ : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
snake_case_ : str = bbox[i, j, 2]
snake_case_ : Dict = bbox[i, j, 0]
snake_case_ : Union[str, Any] = t
snake_case_ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ : Dict = None
if self.use_input_mask:
snake_case_ : str = random_attention_mask([self.batch_size, self.text_seq_length] )
snake_case_ : Any = None
if self.use_token_type_ids:
snake_case_ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
snake_case_ : Union[str, Any] = None
snake_case_ : str = None
if self.use_labels:
snake_case_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
snake_case_ : str = LayoutLMvaConfig(
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 , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def _A ( self :Dict , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :str , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :List[str] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# text + image
snake_case_ : Tuple = model(lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : Optional[int] = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
snake_case_ : int = model(lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
snake_case_ : List[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
snake_case_ : Union[str, Any] = model(pixel_values=lowerCAmelCase__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def _A ( self :str , lowerCAmelCase__ :str , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple ) -> List[Any]:
'''simple docstring'''
snake_case_ : str = self.num_labels
snake_case_ : List[Any] = LayoutLMvaForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : Optional[int] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :int , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Any , lowerCAmelCase__ :Union[str, Any] ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.num_labels
snake_case_ : str = LayoutLMvaForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def _A ( self :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :str , lowerCAmelCase__ :int , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :str ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = LayoutLMvaForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
snake_case_ : List[Any] = model(
lowerCAmelCase__ , bbox=lowerCAmelCase__ , pixel_values=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 _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) : Optional[Any] = config_and_inputs
snake_case_ : Tuple = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = False
a__ = False
a__ = False
a__ = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
a__ = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def _A ( self :Optional[Any] , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :List[Any] , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :List[Any] ) -> List[str]:
'''simple docstring'''
return True
def _A ( self :List[Any] ) -> str:
'''simple docstring'''
snake_case_ : Tuple = LayoutLMvaModelTester(self )
snake_case_ : Optional[int] = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def _A ( self :Tuple , lowerCAmelCase__ :Optional[int] , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Union[str, Any]=False ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in get_values(lowerCAmelCase__ ):
snake_case_ : List[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
elif model_class in [
*get_values(lowerCAmelCase__ ),
]:
snake_case_ : List[str] = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCAmelCase__ , )
return inputs_dict
def _A ( self :Any ) -> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :int ) -> int:
'''simple docstring'''
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :Any ) -> Dict:
'''simple docstring'''
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def _A ( self :int ) -> str:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def _A ( self :List[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
def _A ( self :int ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
@slow
def _A ( self :Tuple ) -> List[Any]:
'''simple docstring'''
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : str = LayoutLMvaModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class A_ (unittest.TestCase ):
"""simple docstring"""
@cached_property
def _A ( self :Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
return LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase__ ) if is_vision_available() else None
@slow
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = self.default_image_processor
snake_case_ : Optional[int] = prepare_img()
snake_case_ : Union[str, Any] = image_processor(images=lowerCAmelCase__ , return_tensors="pt" ).pixel_values.to(lowerCAmelCase__ )
snake_case_ : List[str] = torch.tensor([[1, 2]] )
snake_case_ : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
snake_case_ : Any = model(
input_ids=input_ids.to(lowerCAmelCase__ ) , bbox=bbox.to(lowerCAmelCase__ ) , pixel_values=pixel_values.to(lowerCAmelCase__ ) , )
# verify the logits
snake_case_ : Optional[Any] = torch.Size((1, 199, 768) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCAmelCase__ )
snake_case_ : str = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 653 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
_UpperCAmelCase : List[str] = logging.get_logger(__name__)
class UpperCAmelCase ( a_ ):
"""simple docstring"""
def __init__( self , *_snake_case , **_snake_case ) -> None:
warnings.warn(
'''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use PoolFormerImageProcessor instead.''' , lowerCAmelCase__ , )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
| 683 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( __magic_name__ )-> int: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
snake_case_ : str = [1, 2, 3]
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=2 )
with pytest.raises(__magic_name__ ):
with parallel_backend("unsupported backend" ):
map_nested(__magic_name__ ,__magic_name__ ,num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" ,[2, -1] )
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = [1, 2]
snake_case_ : Union[str, Any] = {"a": 1, "b": 2}
snake_case_ : str = {"a": [1, 2], "b": [3, 4]}
snake_case_ : List[str] = {"a": {"1": 1}, "b": 2}
snake_case_ : Optional[int] = {"a": 1, "b": 2, "c": 3, "d": 4}
snake_case_ : Tuple = [2, 3]
snake_case_ : str = {"a": 2, "b": 3}
snake_case_ : Dict = {"a": [2, 3], "b": [4, 5]}
snake_case_ : List[Any] = {"a": {"1": 2}, "b": 3}
snake_case_ : str = {"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
assert map_nested(__magic_name__ ,__magic_name__ ,num_proc=__magic_name__ ) == expected_map_nested_sa
| 653 | 0 |
def __lowerCamelCase ( __a :Dict , __a :Union[str, Any] , __a :int ) -> int:
"""simple docstring"""
def update_area_of_max_square(__a :Tuple , __a :List[Any] ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
A__ = update_area_of_max_square(__a , col + 1 )
A__ = update_area_of_max_square(row + 1 , col + 1 )
A__ = update_area_of_max_square(row + 1 , __a )
if mat[row][col]:
A__ = 1 + min([right, diagonal, down] )
A__ = max(largest_square_area[0] , __a )
return sub_problem_sol
else:
return 0
A__ = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def __lowerCamelCase ( __a :int , __a :Tuple , __a :Union[str, Any] ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__a :Union[str, Any] , __a :Tuple , __a :Tuple ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
A__ = update_area_of_max_square_using_dp_array(__a , col + 1 , __a )
A__ = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , __a )
A__ = update_area_of_max_square_using_dp_array(row + 1 , __a , __a )
if mat[row][col]:
A__ = 1 + min([right, diagonal, down] )
A__ = max(largest_square_area[0] , __a )
A__ = sub_problem_sol
return sub_problem_sol
else:
return 0
A__ = [0]
A__ = [[-1] * cols for _ in range(__a )]
update_area_of_max_square_using_dp_array(0 , 0 , __a )
return largest_square_area[0]
def __lowerCamelCase ( __a :Tuple , __a :int , __a :Optional[int] ) -> int:
"""simple docstring"""
A__ = [[0] * (cols + 1) for _ in range(rows + 1 )]
A__ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
A__ = dp_array[row][col + 1]
A__ = dp_array[row + 1][col + 1]
A__ = dp_array[row + 1][col]
if mat[row][col] == 1:
A__ = 1 + min(__a , __a , __a )
A__ = max(dp_array[row][col] , __a )
else:
A__ = 0
return largest_square_area
def __lowerCamelCase ( __a :str , __a :int , __a :Tuple ) -> int:
"""simple docstring"""
A__ = [0] * (cols + 1)
A__ = [0] * (cols + 1)
A__ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
A__ = current_row[col + 1]
A__ = next_row[col + 1]
A__ = next_row[col]
if mat[row][col] == 1:
A__ = 1 + min(__a , __a , __a )
A__ = max(current_row[col] , __a )
else:
A__ = 0
A__ = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 176 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase : int = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''esm'''
def __init__( self :Dict , lowerCAmelCase__ :List[Any]=None , lowerCAmelCase__ :Optional[int]=None , lowerCAmelCase__ :str=None , lowerCAmelCase__ :int=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :Dict=12 , lowerCAmelCase__ :Union[str, Any]=3_072 , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Optional[Any]=0.1 , lowerCAmelCase__ :List[Any]=1_026 , lowerCAmelCase__ :int=0.0_2 , lowerCAmelCase__ :Optional[int]=1E-1_2 , lowerCAmelCase__ :List[str]="absolute" , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :List[str]=False , lowerCAmelCase__ :List[Any]=False , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=None , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
super().__init__(pad_token_id=lowerCAmelCase__ , mask_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : str = vocab_size
snake_case_ : str = hidden_size
snake_case_ : List[str] = num_hidden_layers
snake_case_ : List[str] = num_attention_heads
snake_case_ : Any = intermediate_size
snake_case_ : Optional[Any] = hidden_dropout_prob
snake_case_ : Tuple = attention_probs_dropout_prob
snake_case_ : List[Any] = max_position_embeddings
snake_case_ : str = initializer_range
snake_case_ : List[Any] = layer_norm_eps
snake_case_ : str = position_embedding_type
snake_case_ : Optional[int] = use_cache
snake_case_ : str = emb_layer_norm_before
snake_case_ : List[Any] = token_dropout
snake_case_ : str = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("No esmfold_config supplied for folding model, using default values." )
snake_case_ : Optional[Any] = EsmFoldConfig()
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case_ : Union[str, Any] = EsmFoldConfig(**lowerCAmelCase__ )
snake_case_ : Optional[Any] = esmfold_config
if vocab_list is None:
logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!" )
snake_case_ : List[str] = get_default_vocab_list()
else:
snake_case_ : List[str] = vocab_list
else:
snake_case_ : List[Any] = None
snake_case_ : int = None
if self.esmfold_config is not None and getattr(self.esmfold_config , "use_esm_attn_map" , lowerCAmelCase__ ):
raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!" )
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case_ : Any = super().to_dict()
if isinstance(self.esmfold_config , lowerCAmelCase__ ):
snake_case_ : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = None
a__ = True
a__ = False
a__ = False
a__ = False
a__ = 0
a__ = True
a__ = False
a__ = 128
a__ = None
def _A ( self :Dict ) -> int:
'''simple docstring'''
if self.trunk is None:
snake_case_ : Dict = TrunkConfig()
elif isinstance(self.trunk , lowerCAmelCase__ ):
snake_case_ : int = TrunkConfig(**self.trunk )
def _A ( self :Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = asdict(self )
snake_case_ : Optional[int] = self.trunk.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 48
a__ = 1024
a__ = 128
a__ = 32
a__ = 32
a__ = 32
a__ = 0
a__ = 0
a__ = False
a__ = 4
a__ = 128
a__ = None
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if self.structure_module is None:
snake_case_ : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCAmelCase__ ):
snake_case_ : List[str] = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.''' )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
F''' {self.sequence_state_dim} and {self.sequence_state_dim}.''' )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.''' )
snake_case_ : Dict = self.sequence_state_dim // self.sequence_head_width
snake_case_ : Optional[int] = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.''' )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.''' )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.''' )
if self.dropout >= 0.4:
raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.''' )
def _A ( self :Tuple ) -> List[str]:
'''simple docstring'''
snake_case_ : int = asdict(self )
snake_case_ : Dict = self.structure_module.to_dict()
return output
@dataclass
class A_ :
"""simple docstring"""
a__ = 384
a__ = 128
a__ = 16
a__ = 128
a__ = 12
a__ = 4
a__ = 8
a__ = 0.1
a__ = 8
a__ = 1
a__ = 2
a__ = 7
a__ = 10
a__ = 1E-8
a__ = 1E5
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( )-> int:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 653 | 0 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__A = '''src/transformers'''
# Matches is_xxx_available()
__A = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
__A = re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__A = re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
__A = re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
__A = re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__A = re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
__A = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
__A = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
__A = re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
__A = re.compile(R"""^\s*try:""")
# Catches a line with else:
__A = re.compile(R"""^\s*else:""")
def __A (_SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
"""simple docstring"""
if _re_test_backend.search(_SCREAMING_SNAKE_CASE ) is None:
return None
lowerCAmelCase__ :Union[str, Any] = [b[0] for b in _re_backend.findall(_SCREAMING_SNAKE_CASE )]
backends.sort()
return "_and_".join(_SCREAMING_SNAKE_CASE )
def __A (_SCREAMING_SNAKE_CASE ) ->Any:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCAmelCase__ :List[Any] = f.readlines()
lowerCAmelCase__ :str = 0
while line_index < len(_SCREAMING_SNAKE_CASE ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_SCREAMING_SNAKE_CASE ):
return None
# First grab the objects without a specific backend in _import_structure
lowerCAmelCase__ :List[Any] = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
lowerCAmelCase__ :Tuple = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_SCREAMING_SNAKE_CASE ):
lowerCAmelCase__ :List[Any] = _re_one_line_import_struct.search(_SCREAMING_SNAKE_CASE ).groups()[0]
lowerCAmelCase__ :Optional[Any] = re.findall('\[([^\]]+)\]' , _SCREAMING_SNAKE_CASE )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
lowerCAmelCase__ :Any = _re_import_struct_key_value.search(_SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
lowerCAmelCase__ :int = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(_SCREAMING_SNAKE_CASE ) > 0]
objects.extend(_SCREAMING_SNAKE_CASE )
elif line.startswith(' ' * 8 + '\"' ):
objects.append(line[9:-3] )
line_index += 1
lowerCAmelCase__ :Optional[int] = {"none": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
lowerCAmelCase__ :List[Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowerCAmelCase__ :Optional[int] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowerCAmelCase__ :Optional[int] = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
lowerCAmelCase__ :List[str] = lines[line_index]
if _re_import_struct_add_one.search(_SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_import_struct_add_one.search(_SCREAMING_SNAKE_CASE ).groups()[0] )
elif _re_import_struct_add_many.search(_SCREAMING_SNAKE_CASE ) is not None:
lowerCAmelCase__ :Union[str, Any] = _re_import_struct_add_many.search(_SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowerCAmelCase__ :Dict = [obj[1:-1] for obj in imports if len(_SCREAMING_SNAKE_CASE ) > 0]
objects.extend(_SCREAMING_SNAKE_CASE )
elif _re_between_brackets.search(_SCREAMING_SNAKE_CASE ) is not None:
lowerCAmelCase__ :int = _re_between_brackets.search(_SCREAMING_SNAKE_CASE ).groups()[0].split(', ' )
lowerCAmelCase__ :Optional[Any] = [obj[1:-1] for obj in imports if len(_SCREAMING_SNAKE_CASE ) > 0]
objects.extend(_SCREAMING_SNAKE_CASE )
elif _re_quote_object.search(_SCREAMING_SNAKE_CASE ) is not None:
objects.append(_re_quote_object.search(_SCREAMING_SNAKE_CASE ).groups()[0] )
elif line.startswith(' ' * 8 + '\"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '\"' ):
objects.append(line[13:-3] )
line_index += 1
lowerCAmelCase__ :List[str] = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
lowerCAmelCase__ :Optional[int] = []
while (
line_index < len(_SCREAMING_SNAKE_CASE )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
lowerCAmelCase__ :int = lines[line_index]
lowerCAmelCase__ :List[str] = _re_import.search(_SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
lowerCAmelCase__ :str = {"none": objects}
# Let's continue with backend-specific objects
while line_index < len(_SCREAMING_SNAKE_CASE ):
# If the line is an if is_backend_available, we grab all objects associated.
lowerCAmelCase__ :str = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
lowerCAmelCase__ :List[Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
lowerCAmelCase__ :Any = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
lowerCAmelCase__ :Tuple = lines[line_index]
lowerCAmelCase__ :List[str] = _re_import.search(_SCREAMING_SNAKE_CASE )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
lowerCAmelCase__ :Optional[int] = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[Any]:
"""simple docstring"""
def find_duplicates(_SCREAMING_SNAKE_CASE ):
return [k for k, v in collections.Counter(_SCREAMING_SNAKE_CASE ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
lowerCAmelCase__ :Optional[int] = []
for key in import_dict_objects.keys():
lowerCAmelCase__ :Any = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}" )
lowerCAmelCase__ :Union[str, Any] = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
lowerCAmelCase__ :Any = "base imports" if key == "none" else F"{key} backend"
errors.append(F"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F" {a} in _import_structure but not in TYPE_HINT." )
return errors
def __A () ->Optional[Any]:
"""simple docstring"""
lowerCAmelCase__ :Dict = []
for root, _, files in os.walk(_SCREAMING_SNAKE_CASE ):
if "__init__.py" in files:
lowerCAmelCase__ :Optional[int] = os.path.join(_SCREAMING_SNAKE_CASE , '__init__.py' )
lowerCAmelCase__ :Optional[int] = parse_init(_SCREAMING_SNAKE_CASE )
if objects is not None:
lowerCAmelCase__ :Union[str, Any] = analyze_results(*_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase__ :Union[str, Any] = F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append('\n'.join(_SCREAMING_SNAKE_CASE ) )
if len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError('\n\n'.join(_SCREAMING_SNAKE_CASE ) )
def __A () ->List[Any]:
"""simple docstring"""
lowerCAmelCase__ :Any = []
for path, directories, files in os.walk(_SCREAMING_SNAKE_CASE ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(_SCREAMING_SNAKE_CASE )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_SCREAMING_SNAKE_CASE ) / folder).glob('*.py' ) ) ) == 0:
continue
lowerCAmelCase__ :Optional[int] = str((Path(_SCREAMING_SNAKE_CASE ) / folder).relative_to(_SCREAMING_SNAKE_CASE ) )
lowerCAmelCase__ :Any = short_path.replace(os.path.sep , '.' )
submodules.append(_SCREAMING_SNAKE_CASE )
for fname in files:
if fname == "__init__.py":
continue
lowerCAmelCase__ :str = str((Path(_SCREAMING_SNAKE_CASE ) / fname).relative_to(_SCREAMING_SNAKE_CASE ) )
lowerCAmelCase__ :Optional[int] = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(_SCREAMING_SNAKE_CASE )
return submodules
__A = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
]
def __A () ->Union[str, Any]:
"""simple docstring"""
lowerCAmelCase__ :Optional[int] = importlib.util.spec_from_file_location(
'transformers' , os.path.join(_SCREAMING_SNAKE_CASE , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
lowerCAmelCase__ :Dict = spec.loader.load_module()
lowerCAmelCase__ :Union[str, Any] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(_SCREAMING_SNAKE_CASE ) > 0:
lowerCAmelCase__ :Optional[int] = "\n".join(F"- {module}" for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
F"{list_of_modules}\n"
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 93 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653 | 0 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class lowercase_ ( a_ ):
'''simple docstring'''
__lowerCAmelCase : str = ["vqvae"]
def __init__( self , a_ , a_ , a_ , a_ , ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , mel=lowerCAmelCase__ , vqvae=lowerCAmelCase__ )
def snake_case_ ( self ) -> int:
"""simple docstring"""
return 5_0 if isinstance(self.scheduler , lowerCAmelCase__ ) else 1_0_0_0
@torch.no_grad()
def __call__( self , a_ = 1 , a_ = None , a_ = None , a_ = 0 , a_ = 0 , a_ = None , a_ = None , a_ = 0 , a_ = 0 , a_ = None , a_ = 0 , a_ = None , a_ = None , a_=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
"""simple docstring"""
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowerCAmelCase__ )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowerCAmelCase__ , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase = self.mel.audio_slice_to_image(lowerCAmelCase__ )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='uint8' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 2_5_5) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__ , 0 ) ).latent_dist.sample(
generator=lowerCAmelCase__ )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowerCAmelCase__ , lowerCAmelCase__ , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowerCAmelCase__ , lowerCAmelCase__ , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowerCAmelCase__ ):
UpperCAmelCase = self.unet(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )["sample"]
else:
UpperCAmelCase = self.unet(lowerCAmelCase__ , lowerCAmelCase__ )["sample"]
if isinstance(self.scheduler , lowerCAmelCase__ ):
UpperCAmelCase = self.scheduler.step(
model_output=lowerCAmelCase__ , timestep=lowerCAmelCase__ , sample=lowerCAmelCase__ , eta=lowerCAmelCase__ , generator=lowerCAmelCase__ , )["prev_sample"]
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowerCAmelCase__ , timestep=lowerCAmelCase__ , sample=lowerCAmelCase__ , generator=lowerCAmelCase__ , )["prev_sample"]
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowerCAmelCase__ )["sample"]
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 2_5_5).round().astype('uint8' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowerCAmelCase__ , mode='RGB' ).convert('L' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowerCAmelCase__ ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__ )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowerCAmelCase__ ) )
@torch.no_grad()
def snake_case_ ( self , a_ , a_ = 5_0 ) -> np.ndarray:
"""simple docstring"""
assert isinstance(self.scheduler , lowerCAmelCase__ )
self.scheduler.set_timesteps(lowerCAmelCase__ )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 2_5_5) * 2 - 1
UpperCAmelCase = torch.Tensor(lowerCAmelCase__ ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowerCAmelCase__ , lowerCAmelCase__ )["sample"]
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def snake_case_ ( a_ , a_ , a_ ) -> torch.Tensor:
"""simple docstring"""
UpperCAmelCase = acos(torch.dot(torch.flatten(lowerCAmelCase__ ) , torch.flatten(lowerCAmelCase__ ) ) / torch.norm(lowerCAmelCase__ ) / torch.norm(lowerCAmelCase__ ) )
return sin((1 - alpha) * theta ) * xa / sin(lowerCAmelCase__ ) + sin(alpha * theta ) * xa / sin(lowerCAmelCase__ )
| 447 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
__lowerCamelCase : Optional[int] = 1E-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class A_ :
"""simple docstring"""
def __init__( self :Tuple , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[Any]=16 , lowerCAmelCase__ :Any=13 , lowerCAmelCase__ :Optional[Any]=7 , lowerCAmelCase__ :str=14 , lowerCAmelCase__ :Union[str, Any]=10 , lowerCAmelCase__ :Tuple=19 , lowerCAmelCase__ :Optional[Any]=5 , lowerCAmelCase__ :Dict=4 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Any=16 , lowerCAmelCase__ :str=2 , lowerCAmelCase__ :List[Any]=4 , lowerCAmelCase__ :Any=4 , lowerCAmelCase__ :str="gelu" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Dict=0.1 , lowerCAmelCase__ :Optional[int]=[1, 2, 3, 4, 5] , lowerCAmelCase__ :str=25 , lowerCAmelCase__ :Optional[Any]=5 , ) -> Dict:
'''simple docstring'''
snake_case_ : List[str] = d_model
snake_case_ : Dict = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : Optional[Any] = prediction_length
snake_case_ : str = context_length
snake_case_ : Tuple = cardinality
snake_case_ : List[str] = num_time_features
snake_case_ : Optional[Any] = lags_sequence
snake_case_ : Union[str, Any] = embedding_dimension
snake_case_ : Optional[Any] = is_training
snake_case_ : Optional[Any] = hidden_size
snake_case_ : Any = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Any = hidden_act
snake_case_ : Union[str, Any] = hidden_dropout_prob
snake_case_ : Dict = attention_probs_dropout_prob
snake_case_ : List[str] = context_length
snake_case_ : Any = prediction_length + label_length
snake_case_ : Union[str, Any] = label_length
snake_case_ : List[Any] = moving_average
snake_case_ : str = autocorrelation_factor
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :Optional[Any] ) -> Dict:
'''simple docstring'''
snake_case_ : Any = config.context_length + max(config.lags_sequence )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
snake_case_ : Optional[int] = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, _past_length] )
snake_case_ : Dict = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
snake_case_ : List[Any] = floats_tensor([self.batch_size, config.prediction_length] )
snake_case_ : int = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def _A ( self :Dict ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.get_config()
snake_case_ : int = self.prepare_autoformer_inputs_dict(lowerCAmelCase__ )
return config, inputs_dict
def _A ( self :Optional[int] ) -> Dict:
'''simple docstring'''
snake_case_, snake_case_ : Union[str, Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _A ( self :Tuple , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case_ : Dict = AutoformerModel(config=lowerCAmelCase__ ).to(lowerCAmelCase__ ).eval()
snake_case_ : Optional[int] = model(**lowerCAmelCase__ )
snake_case_ : Any = outputs.encoder_last_hidden_state
snake_case_ : Dict = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[Any] = model.get_encoder()
encoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : Tuple = AutoformerEncoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : List[str] = model.create_network_inputs(**lowerCAmelCase__ )
snake_case_, snake_case_ : Optional[int] = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
snake_case_ : List[Any] = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
snake_case_ : Optional[int] = encoder(inputs_embeds=lowerCAmelCase__ )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3 )
snake_case_ : Any = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
snake_case_ : List[str] = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
snake_case_ : Optional[Any] = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
snake_case_ : Any = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : List[Any] = model.get_decoder()
decoder.save_pretrained(lowerCAmelCase__ )
snake_case_ : int = AutoformerDecoder.from_pretrained(lowerCAmelCase__ ).to(lowerCAmelCase__ )
snake_case_ : Tuple = decoder(
trend=lowerCAmelCase__ , inputs_embeds=lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3 )
@require_torch
class A_ (a_ , a_ , unittest.TestCase ):
"""simple docstring"""
a__ = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
a__ = (AutoformerForPrediction,) if is_torch_available() else ()
a__ = {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = AutoformerModelTester(self )
snake_case_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ )
def _A ( self :List[str] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def _A ( self :List[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_, snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
snake_case_ : List[Any] = model_class(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(lowerCAmelCase__ )
snake_case_, snake_case_ : str = model_class.from_pretrained(lowerCAmelCase__ , output_loading_info=lowerCAmelCase__ )
self.assertEqual(info["missing_keys"] , [] )
def _A ( self :Optional[int] ) -> Tuple:
'''simple docstring'''
snake_case_ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__ )
@unittest.skip(reason="Model has no tokens embeddings" )
def _A ( self :str ) -> str:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : List[Any] = inspect.signature(getattr(lowerCAmelCase__ , "forward" ) )
# The main input is the name of the argument after `self`
snake_case_ : Dict = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , lowerCAmelCase__ )
def _A ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ : Tuple = model_class(lowerCAmelCase__ )
snake_case_ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ : Optional[Any] = [*signature.parameters.keys()]
snake_case_ : Dict = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(lowerCAmelCase__ )] , lowerCAmelCase__ )
def _A ( self :int ) -> Any:
'''simple docstring'''
snake_case_, snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ : Union[str, Any] = True
snake_case_ : List[str] = getattr(self.model_tester , "seq_length" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "decoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "encoder_seq_length" , lowerCAmelCase__ )
snake_case_ : Union[str, Any] = getattr(self.model_tester , "d_model" , lowerCAmelCase__ )
snake_case_ : Dict = getattr(self.model_tester , "num_attention_heads" , lowerCAmelCase__ )
snake_case_ : Optional[int] = d_model // num_attention_heads
for model_class in self.all_model_classes:
snake_case_ : Any = True
snake_case_ : Any = False
snake_case_ : Dict = True
snake_case_ : List[str] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : Tuple = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : Union[str, Any] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
snake_case_ : Optional[int] = True
snake_case_ : Any = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
snake_case_ : str = outputs.encoder_attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
snake_case_ : Tuple = len(lowerCAmelCase__ )
snake_case_ : List[str] = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# decoder attentions
snake_case_ : Optional[int] = outputs.decoder_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
snake_case_ : List[Any] = outputs.cross_attentions
self.assertIsInstance(lowerCAmelCase__ , (list, tuple) )
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
snake_case_ : Optional[int] = True
snake_case_ : List[Any] = True
snake_case_ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
snake_case_ : List[Any] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 2 , len(lowerCAmelCase__ ) )
snake_case_ : Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def _A ( self :Any ) -> Optional[Any]:
'''simple docstring'''
super().test_retain_grad_hidden_states_attentions()
def __UpperCAmelCase ( __magic_name__="train-batch.pt" )-> int:
"""simple docstring"""
snake_case_ : List[str] = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" ,filename=__magic_name__ ,repo_type="dataset" )
snake_case_ : List[str] = torch.load(__magic_name__ ,map_location=__magic_name__ )
return batch
@require_torch
@slow
class A_ (unittest.TestCase ):
"""simple docstring"""
def _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : Optional[int] = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : List[str] = prepare_batch()
with torch.no_grad():
snake_case_ : int = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
snake_case_ : Optional[int] = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Optional[Any] = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : str = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : Optional[Any] = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Tuple = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
snake_case_ : Dict = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , lowerCAmelCase__ )
snake_case_ : Any = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=lowerCAmelCase__ )
self.assertTrue(torch.allclose(output[0, :3, :3] , lowerCAmelCase__ , atol=lowerCAmelCase__ ) )
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(lowerCAmelCase__ )
snake_case_ : str = prepare_batch("val-batch.pt" )
with torch.no_grad():
snake_case_ : Optional[Any] = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
snake_case_ : List[Any] = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , lowerCAmelCase__ )
snake_case_ : Dict = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=lowerCAmelCase__ )
snake_case_ : Optional[Any] = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , lowerCAmelCase__ , rtol=1E-1 ) )
| 653 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCamelCase : Optional[Any] = 16
__UpperCamelCase : List[Any] = 32
def A ( _lowercase , _lowercase = 16 ):
SCREAMING_SNAKE_CASE : Tuple = AutoTokenizer.from_pretrained('''bert-base-cased''' )
SCREAMING_SNAKE_CASE : List[str] = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_lowercase ):
# max_length=None => use the model max length (it's actually the default)
SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_lowercase , max_length=_lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : List[Any] = datasets.map(
_lowercase , batched=_lowercase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
SCREAMING_SNAKE_CASE : int = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
SCREAMING_SNAKE_CASE : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
SCREAMING_SNAKE_CASE : Union[str, Any] = 16
elif accelerator.mixed_precision != "no":
SCREAMING_SNAKE_CASE : Tuple = 8
else:
SCREAMING_SNAKE_CASE : Dict = None
return tokenizer.pad(
_lowercase , padding='''longest''' , max_length=_lowercase , pad_to_multiple_of=_lowercase , return_tensors='''pt''' , )
# Instantiate dataloaders.
SCREAMING_SNAKE_CASE : List[str] = DataLoader(
tokenized_datasets['''train'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
SCREAMING_SNAKE_CASE : List[Any] = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_lowercase , collate_fn=_lowercase , batch_size=_lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__UpperCamelCase : Tuple = mocked_dataloaders # noqa: F811
def A ( _lowercase , _lowercase ):
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _lowercase ) == "1":
SCREAMING_SNAKE_CASE : Tuple = 2
# New Code #
SCREAMING_SNAKE_CASE : List[Any] = int(args.gradient_accumulation_steps )
SCREAMING_SNAKE_CASE : Optional[Any] = int(args.local_sgd_steps )
# Initialize accelerator
SCREAMING_SNAKE_CASE : int = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_lowercase )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
SCREAMING_SNAKE_CASE : int = config["lr"]
SCREAMING_SNAKE_CASE : Optional[Any] = int(config['''num_epochs'''] )
SCREAMING_SNAKE_CASE : int = int(config['''seed'''] )
SCREAMING_SNAKE_CASE : List[str] = int(config['''batch_size'''] )
SCREAMING_SNAKE_CASE : Any = evaluate.load('''glue''' , '''mrpc''' )
set_seed(_lowercase )
SCREAMING_SNAKE_CASE : Dict = get_dataloaders(_lowercase , _lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
SCREAMING_SNAKE_CASE : Any = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
SCREAMING_SNAKE_CASE : Optional[Any] = model.to(accelerator.device )
# Instantiate optimizer
SCREAMING_SNAKE_CASE : int = AdamW(params=model.parameters() , lr=_lowercase )
# Instantiate scheduler
SCREAMING_SNAKE_CASE : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowercase , num_warmup_steps=100 , num_training_steps=(len(_lowercase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
SCREAMING_SNAKE_CASE : List[str] = accelerator.prepare(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
# Now we train the model
for epoch in range(_lowercase ):
model.train()
with LocalSGD(
accelerator=_lowercase , model=_lowercase , local_sgd_steps=_lowercase , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = model(**_lowercase )
SCREAMING_SNAKE_CASE : Any = output.loss
accelerator.backward(_lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(_lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[Any] = model(**_lowercase )
SCREAMING_SNAKE_CASE : int = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE : Tuple = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_lowercase , references=_lowercase , )
SCREAMING_SNAKE_CASE : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , _lowercase )
def A ( ):
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_lowercase , default=_lowercase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=_lowercase , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument(
'''--local_sgd_steps''' , type=_lowercase , default=8 , help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
SCREAMING_SNAKE_CASE : str = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(_lowercase , _lowercase )
if __name__ == "__main__":
main()
| 248 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = RobertaTokenizer
a__ = RobertaTokenizerFast
a__ = True
a__ = {'''cls_token''': '''<s>'''}
def _A ( self :Optional[int] ) -> List[Any]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : List[Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
snake_case_ : Tuple = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
snake_case_ : List[Any] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
snake_case_ : int = {"unk_token": "<unk>"}
snake_case_ : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(lowerCAmelCase__ ) )
def _A ( self :Optional[Any] , **lowerCAmelCase__ :str ) -> str:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Any , **lowerCAmelCase__ :Tuple ) -> Optional[int]:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return RobertaTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :str ) -> Optional[int]:
'''simple docstring'''
snake_case_ : int = "lower newer"
snake_case_ : Tuple = "lower newer"
return input_text, output_text
def _A ( self :Tuple ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Dict = "lower newer"
snake_case_ : int = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
snake_case_ : str = tokenizer.tokenize(lowerCAmelCase__ ) # , add_prefix_space=True)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokens + [tokenizer.unk_token]
snake_case_ : Optional[int] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
def _A ( self :Any ) -> str:
'''simple docstring'''
snake_case_ : List[str] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode("Hello world!" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode("Hello world! cécé herlolip 418" , add_special_tokens=lowerCAmelCase__ ) , [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2] , )
@slow
def _A ( self :str ) -> List[str]:
'''simple docstring'''
snake_case_ : Tuple = self.tokenizer_class.from_pretrained("roberta-base" )
snake_case_ : List[str] = tokenizer.encode("sequence builders" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(
"sequence builders" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer.encode(
"sequence builders" , "multi-sequence build" , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ )
snake_case_ : Any = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__ , lowerCAmelCase__ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : Optional[Any] = self.get_tokenizer()
snake_case_ : Tuple = "Encode this sequence."
snake_case_ : Optional[Any] = tokenizer.byte_encoder[" ".encode("utf-8" )[0]]
# Testing encoder arguments
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ )
snake_case_ : str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
tokenizer.add_special_tokens({"bos_token": "<s>"} )
snake_case_ : str = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
# Testing spaces after special tokens
snake_case_ : List[Any] = "<mask>"
tokenizer.add_special_tokens(
{"mask_token": AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ )} ) # mask token has a left space
snake_case_ : str = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
snake_case_ : List[str] = "Encode <mask> sequence"
snake_case_ : List[Any] = "Encode <mask>sequence"
snake_case_ : Tuple = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : int = encoded.index(lowerCAmelCase__ )
snake_case_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
snake_case_ : List[str] = tokenizer.encode(lowerCAmelCase__ )
snake_case_ : Union[str, Any] = encoded.index(lowerCAmelCase__ )
snake_case_ : int = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
pass
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : List[Any] = self.tokenizer_class.from_pretrained(lowerCAmelCase__ , **lowerCAmelCase__ )
snake_case_ : Any = "A, <mask> AllenNLP sentence."
snake_case_ : str = tokenizer_r.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
snake_case_ : int = tokenizer_p.encode_plus(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ , return_token_type_ids=lowerCAmelCase__ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
snake_case_ : List[Any] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
snake_case_ : str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
lowerCAmelCase__ , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
def _A ( self :int ) -> Tuple:
'''simple docstring'''
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
snake_case_ : str = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
snake_case_ : Any = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["add_prefix_space"] , lowerCAmelCase__ )
self.assertEqual(post_processor_state["trim_offsets"] , lowerCAmelCase__ )
def _A ( self :List[str] ) -> List[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
snake_case_ : str = "hello" # `hello` is a token in the vocabulary of `pretrained_name`
snake_case_ : Tuple = F'''{text_of_1_token} {text_of_1_token}'''
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : List[str] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Tuple = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ) + 1, len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : str = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(lowerCAmelCase__ ), len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Tuple = F''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
snake_case_ : Dict = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Union[str, Any] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ) + 1, 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Any = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Any = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
snake_case_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(
lowerCAmelCase__ , use_fast=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ )
snake_case_ : Optional[int] = tokenizer_r(lowerCAmelCase__ , return_offsets_mapping=lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(lowerCAmelCase__ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(lowerCAmelCase__ ), 1 + len(lowerCAmelCase__ ) + 1 + len(lowerCAmelCase__ )) , )
| 653 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {'''vocab_file''': '''spiece.model'''}
lowerCamelCase = {
'''vocab_file''': {
'''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
}
}
class _UpperCamelCase ( a_ ):
'''simple docstring'''
def __init__( self : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=False , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : List[str]=False , _lowerCAmelCase : Optional[int]="<s>" , _lowerCAmelCase : Tuple="</s>" , _lowerCAmelCase : Tuple="<unk>" , _lowerCAmelCase : Optional[Any]="<sep>" , _lowerCAmelCase : Union[str, Any]="<pad>" , _lowerCAmelCase : List[Any]="<cls>" , _lowerCAmelCase : int="<mask>" , _lowerCAmelCase : Optional[int]=["<eop>", "<eod>"] , _lowerCAmelCase : Optional[Dict[str, Any]] = None , **_lowerCAmelCase : Any , ):
'''simple docstring'''
__lowercase =AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else mask_token
__lowercase ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCAmelCase__ , remove_space=lowerCAmelCase__ , keep_accents=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase__ , )
__lowercase =3
__lowercase =do_lower_case
__lowercase =remove_space
__lowercase =keep_accents
__lowercase =vocab_file
__lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(lowerCAmelCase__)
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
'See https://pypi.org/project/jieba/ for installation.')
__lowercase =jieba
__lowercase =str.maketrans(' \n' , '\u2582\u2583')
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
return len(self.sp_model)
def __lowerCamelCase ( self : Tuple):
'''simple docstring'''
__lowercase ={self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : Optional[Any]):
'''simple docstring'''
__lowercase =self.__dict__.copy()
__lowercase =None
return state
def __setstate__( self : Optional[Any] , _lowerCAmelCase : Optional[int]):
'''simple docstring'''
__lowercase =d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs'):
__lowercase ={}
__lowercase =spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def __lowerCamelCase ( self : Tuple , _lowerCAmelCase : Any):
'''simple docstring'''
if self.remove_space:
__lowercase =" ".join(inputs.strip().split())
else:
__lowercase =inputs
__lowercase =outputs.replace('``' , '\"').replace('\'\'' , '\"')
if not self.keep_accents:
__lowercase =unicodedata.normalize('NFKD' , lowerCAmelCase__)
__lowercase ="".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__)])
if self.do_lower_case:
__lowercase =outputs.lower()
return outputs
def __lowerCamelCase ( self : str , _lowerCAmelCase : str):
'''simple docstring'''
__lowercase =self.preprocess_text(lowerCAmelCase__)
__lowercase =self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__)
__lowercase =[]
for piece in pieces:
if len(lowerCAmelCase__) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
__lowercase =self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__ , ''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
__lowercase =cur_pieces[1:]
else:
__lowercase =cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(lowerCAmelCase__)
else:
new_pieces.append(lowerCAmelCase__)
return new_pieces
def __lowerCamelCase ( self : Dict , _lowerCAmelCase : Union[str, Any]):
'''simple docstring'''
return self.sp_model.PieceToId(lowerCAmelCase__)
def __lowerCamelCase ( self : int , _lowerCAmelCase : Union[str, Any]):
'''simple docstring'''
return self.sp_model.IdToPiece(lowerCAmelCase__)
def __lowerCamelCase ( self : List[Any] , _lowerCAmelCase : Any):
'''simple docstring'''
__lowercase ="".join(lowerCAmelCase__).replace(lowerCAmelCase__ , ' ').strip()
return out_string
def __lowerCamelCase ( self : int , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None):
'''simple docstring'''
__lowercase =[self.sep_token_id]
__lowercase =[self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __lowerCamelCase ( self : Union[str, Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None , _lowerCAmelCase : bool = False):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__)
if token_ids_a is not None:
return ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1, 1]
return ([0] * len(lowerCAmelCase__)) + [1, 1]
def __lowerCamelCase ( self : int , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None):
'''simple docstring'''
__lowercase =[self.sep_token_id]
__lowercase =[2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def __lowerCamelCase ( self : List[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(lowerCAmelCase__):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
return
__lowercase =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:
__lowercase =self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__)
return (out_vocab_file,)
def __lowerCamelCase ( self : Union[str, Any] , *_lowerCAmelCase : List[Any] , **_lowerCAmelCase : str):
'''simple docstring'''
__lowercase =super()._decode(*lowerCAmelCase__ , **lowerCAmelCase__)
__lowercase =text.replace(' ' , '').replace('\u2582' , ' ').replace('\u2583' , '\n')
return text
| 474 |
'''simple docstring'''
import math
def __UpperCAmelCase ( __magic_name__ )-> bool:
"""simple docstring"""
snake_case_ : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__magic_name__ )
def __UpperCAmelCase ( __magic_name__ = 1 / 1_2345 )-> int:
"""simple docstring"""
snake_case_ : Any = 0
snake_case_ : int = 0
snake_case_ : Union[str, Any] = 3
while True:
snake_case_ : Any = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(__magic_name__ ):
snake_case_ : Optional[Any] = int(__magic_name__ )
total_partitions += 1
if check_partition_perfect(__magic_name__ ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(__magic_name__ )
integer += 1
if __name__ == "__main__":
print(f'''{solution() = }''')
| 653 | 0 |
'''simple docstring'''
import math
import sys
def lowerCAmelCase (__A):
"""simple docstring"""
_a = ""
try:
with open(__A , '''rb''') as binary_file:
_a = binary_file.read()
for dat in data:
_a = F'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('''File not accessible''')
sys.exit()
def lowerCAmelCase (__A):
"""simple docstring"""
_a = {"0": "0", "1": "1"}
_a = "", ""
_a = len(__A)
for i in range(len(__A)):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
_a = lexicon[curr_string]
result += last_match_id
_a = last_match_id + "0"
if math.loga(__A).is_integer():
_a = {}
for curr_key in list(__A):
_a = lexicon.pop(__A)
_a = new_lex
_a = last_match_id + "1"
index += 1
_a = ""
return result
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = 8
try:
with open(__A , '''wb''') as opened_file:
_a = [
to_write[i : i + byte_length]
for i in range(0 , len(__A) , __A)
]
if len(result_byte_array[-1]) % byte_length == 0:
result_byte_array.append('''10000000''')
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1]) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(__A , 2).to_bytes(1 , byteorder='''big'''))
except OSError:
print('''File not accessible''')
sys.exit()
def lowerCAmelCase (__A):
"""simple docstring"""
_a = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
_a = data_bits[counter:]
_a = data_bits[counter + 1 :]
return data_bits
def lowerCAmelCase (__A , __A):
"""simple docstring"""
_a = read_file_binary(__A)
_a = remove_prefix(__A)
_a = decompress_data(__A)
write_file_binary(__A , __A)
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 11 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger()
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def _A ( self :List[Any] , lowerCAmelCase__ :Union[str, Any] , lowerCAmelCase__ :Tensor , lowerCAmelCase__ :Tensor ) -> int:
'''simple docstring'''
snake_case_ : int = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self :List[Any] , lowerCAmelCase__ :Tensor ) -> Union[str, Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class A_ :
"""simple docstring"""
a__ = 42
a__ = 42
a__ = 0
a__ = field(default_factory=a_ )
a__ = field(default_factory=a_ )
def __call__( self :Tuple , lowerCAmelCase__ :Tensor ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
snake_case_ : Tuple = Tracker(self.src )(lowerCAmelCase__ ).parametrized
snake_case_ : List[str] = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
snake_case_ : Tuple = list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ = True )-> Optional[int]:
"""simple docstring"""
print(F'''Converting {name}...''' )
with torch.no_grad():
snake_case_ : List[str] = timm.create_model(__magic_name__ ,pretrained=__magic_name__ ).eval()
snake_case_ : Optional[int] = ResNetForImageClassification(__magic_name__ ).eval()
snake_case_ : Dict = ModuleTransfer(src=__magic_name__ ,dest=__magic_name__ )
snake_case_ : Optional[int] = torch.randn((1, 3, 224, 224) )
module_transfer(__magic_name__ )
assert torch.allclose(from_model(__magic_name__ ) ,our_model(__magic_name__ ).logits ), "The model logits don't match the original one."
snake_case_ : str = F'''resnet{'-'.join(name.split('resnet' ) )}'''
print(__magic_name__ )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add model" ,use_temp_dir=__magic_name__ ,)
# we can use the convnext one
snake_case_ : Optional[Any] = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name ,commit_message="Add image processor" ,use_temp_dir=__magic_name__ ,)
print(F'''Pushed {checkpoint_name}''' )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = None ,__magic_name__ = True )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Optional[Any] = 1000
snake_case_ : List[Any] = (1, num_labels)
snake_case_ : Optional[Any] = "huggingface/label-files"
snake_case_ : Dict = num_labels
snake_case_ : List[Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : List[str] = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : Any = idalabel
snake_case_ : List[Any] = {v: k for k, v in idalabel.items()}
snake_case_ : Optional[int] = partial(__magic_name__ ,num_labels=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ )
snake_case_ : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[64, 128, 256, 512] ,layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] ,hidden_sizes=[256, 512, 1024, 2048] ,layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(__magic_name__ ,names_to_config[model_name] ,__magic_name__ ,__magic_name__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
return config, expected_shape
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default=None,
type=str,
help=(
'''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
),
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default=None,
type=Path,
required=True,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''',
default=True,
type=bool,
required=False,
help='''If True, push model and image processor to the hub.''',
)
__lowerCamelCase : Tuple = parser.parse_args()
__lowerCamelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 653 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
__A : Any = True
from torch.cuda.amp import autocast
__A : Dict = logging.getLogger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
lowerCamelCase__ = field(
default=a_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
lowerCamelCase__ = field(
default=a_ , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
lowerCamelCase__ = field(
default=a_ , metadata={"help": "Whether to log verbose messages or not."} , )
lowerCamelCase__ = field(
default=2.0 , metadata={"help": "Maximum temperature for gumbel softmax."} )
lowerCamelCase__ = field(
default=0.5 , metadata={"help": "Minimum temperature for gumbel softmax."} )
lowerCamelCase__ = field(
default=0.999_995 , metadata={"help": "Decay of gumbel temperature during training."} )
def __a ( A__ : Optional[Any] , A__ : str ):
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
SCREAMING_SNAKE_CASE = logging.WARNING
if model_args.verbose_logging:
SCREAMING_SNAKE_CASE = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
SCREAMING_SNAKE_CASE = logging.INFO
logger.setLevel(A__ )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = field(
default=a_ , metadata={"help": "The name of the dataset to use (via the datasets library)."} )
lowerCamelCase__ = field(
default=a_ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
lowerCamelCase__ = field(
default="train" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'"
} , )
lowerCamelCase__ = field(
default="validation" , metadata={
"help": (
"The name of the validation data set split to use (via the datasets library). Defaults to \'validation\'"
)
} , )
lowerCamelCase__ = field(
default="file" , metadata={"help": "Column in the dataset that contains speech file path. Defaults to \'file\'"} , )
lowerCamelCase__ = field(
default=a_ , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
lowerCamelCase__ = field(
default=1 , metadata={
"help": "The percentage of the train set used as validation set in case there\'s no validation split"
} , )
lowerCamelCase__ = field(
default=a_ , metadata={"help": "The number of processes to use for the preprocessing."} , )
lowerCamelCase__ = field(
default=20.0 , metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"} )
@dataclass
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
lowerCamelCase__ = 4_2
lowerCamelCase__ = 4_2
lowerCamelCase__ = "longest"
lowerCamelCase__ = None
lowerCamelCase__ = None
def __call__( self : Tuple , __lowerCamelCase : List[Dict[str, Union[List[int], torch.Tensor]]] ):
SCREAMING_SNAKE_CASE = self.feature_extractor.pad(
lowerCAmelCase__ , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , )
SCREAMING_SNAKE_CASE = self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1] )
SCREAMING_SNAKE_CASE = batch["input_values"].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
SCREAMING_SNAKE_CASE = self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1 ) ).to(
torch.long )
SCREAMING_SNAKE_CASE = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch["input_values"].device )
# these two operations makes sure that all values
# before the output lengths indices are attended to
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = attention_mask.flip([-1] ).cumsum(-1 ).flip([-1] ).bool()
# sample randomly masked indices
SCREAMING_SNAKE_CASE = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=lowerCAmelCase__ , min_masks=2 , )
return batch
class _SCREAMING_SNAKE_CASE ( a_ ):
'''simple docstring'''
def __init__( self : str , *__lowerCamelCase : str , __lowerCamelCase : Tuple=1 , __lowerCamelCase : int=0 , __lowerCamelCase : List[Any]=1.0 , **__lowerCamelCase : Union[str, Any] ):
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = max_gumbel_temp
SCREAMING_SNAKE_CASE = min_gumbel_temp
SCREAMING_SNAKE_CASE = gumbel_temp_decay
def _snake_case ( self : Optional[int] , __lowerCamelCase : nn.Module , __lowerCamelCase : Dict[str, Union[torch.Tensor, Any]] ):
model.train()
SCREAMING_SNAKE_CASE = self._prepare_inputs(lowerCAmelCase__ )
if self.use_amp:
with autocast():
SCREAMING_SNAKE_CASE = self.compute_loss(lowerCAmelCase__ , lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = self.compute_loss(lowerCAmelCase__ , lowerCAmelCase__ )
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
SCREAMING_SNAKE_CASE = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
SCREAMING_SNAKE_CASE = loss.sum() / (inputs["mask_time_indices"]).sum()
else:
raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']" )
if self.args.gradient_accumulation_steps > 1:
SCREAMING_SNAKE_CASE = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowerCAmelCase__ ).backward()
elif self.use_apex:
with amp.scale_loss(lowerCAmelCase__ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowerCAmelCase__ )
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp ) )
return loss.detach()
def __a ( ):
SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
configure_logger(A__ , A__ )
# Downloading and loading a dataset from the hub.
SCREAMING_SNAKE_CASE = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
SCREAMING_SNAKE_CASE = DatasetDict()
SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]" , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]" , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
SCREAMING_SNAKE_CASE = DatasetDict()
SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split="validation" , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F"{data_args.train_split_name}" , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=A__ )
def prepare_dataset(A__ : Optional[int] ):
# check that all files have the correct sampling rate
SCREAMING_SNAKE_CASE = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
SCREAMING_SNAKE_CASE = datasets.map(
A__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets["train"].column_names )
# filter audio files that are too long
SCREAMING_SNAKE_CASE = vectorized_datasets.filter(
lambda A__ : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(A__ : Optional[int] ):
return feature_extractor(batch["speech"] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
SCREAMING_SNAKE_CASE = vectorized_datasets.map(
A__ , batched=A__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets["train"].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
SCREAMING_SNAKE_CASE = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
"PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
" ``config.feat_extract_norm='layer'" )
SCREAMING_SNAKE_CASE = WavaVecaForPreTraining(A__ )
SCREAMING_SNAKE_CASE = DataCollatorForWavaVecaPretraining(model=A__ , feature_extractor=A__ )
SCREAMING_SNAKE_CASE = WavaVecaPreTrainer(
model=A__ , data_collator=A__ , args=A__ , train_dataset=vectorized_datasets["train"] , eval_dataset=vectorized_datasets["validation"] , tokenizer=A__ , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main() | 16 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''roc_bert'''
def __init__( self :Dict , lowerCAmelCase__ :Optional[Any]=30_522 , lowerCAmelCase__ :Dict=768 , lowerCAmelCase__ :str=12 , lowerCAmelCase__ :Optional[int]=12 , lowerCAmelCase__ :Optional[Any]=3_072 , lowerCAmelCase__ :Any="gelu" , lowerCAmelCase__ :int=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :List[str]=512 , lowerCAmelCase__ :int=2 , lowerCAmelCase__ :Optional[int]=0.0_2 , lowerCAmelCase__ :Tuple=1E-1_2 , lowerCAmelCase__ :Tuple=True , lowerCAmelCase__ :List[str]=0 , lowerCAmelCase__ :Optional[Any]="absolute" , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :List[str]=768 , lowerCAmelCase__ :Optional[Any]=910 , lowerCAmelCase__ :str=512 , lowerCAmelCase__ :int=24_858 , lowerCAmelCase__ :List[Any]=True , **lowerCAmelCase__ :int , ) -> List[str]:
'''simple docstring'''
snake_case_ : int = vocab_size
snake_case_ : Dict = max_position_embeddings
snake_case_ : int = hidden_size
snake_case_ : str = num_hidden_layers
snake_case_ : Union[str, Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : Optional[int] = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Dict = initializer_range
snake_case_ : str = type_vocab_size
snake_case_ : Tuple = layer_norm_eps
snake_case_ : Optional[Any] = use_cache
snake_case_ : Optional[Any] = enable_pronunciation
snake_case_ : List[Any] = enable_shape
snake_case_ : Optional[int] = pronunciation_embed_dim
snake_case_ : Dict = pronunciation_vocab_size
snake_case_ : int = shape_embed_dim
snake_case_ : Any = shape_vocab_size
snake_case_ : Optional[int] = concat_input
snake_case_ : List[Any] = position_embedding_type
snake_case_ : Any = classifier_dropout
super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( a_ ):
'''simple docstring'''
__snake_case = (UnCLIPScheduler,)
def lowerCAmelCase__ ( self: Optional[Any] , **__UpperCamelCase: Dict ) -> List[Any]:
__magic_name__ : List[Any] = {
"num_train_timesteps": 1000,
"variance_type": "fixed_small_log",
"clip_sample": True,
"clip_sample_range": 1.0,
"prediction_type": "epsilon",
}
config.update(**lowerCAmelCase__ )
return config
def lowerCAmelCase__ ( self: str ) -> Optional[Any]:
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=lowerCAmelCase__ )
def lowerCAmelCase__ ( self: str ) -> str:
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=lowerCAmelCase__ )
def lowerCAmelCase__ ( self: List[Any] ) -> Union[str, Any]:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowerCAmelCase__ )
def lowerCAmelCase__ ( self: Tuple ) -> Union[str, Any]:
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=lowerCAmelCase__ )
def lowerCAmelCase__ ( self: int ) -> Optional[Any]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=lowerCAmelCase__ )
def lowerCAmelCase__ ( self: str ) -> Any:
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=lowerCAmelCase__ , prev_timestep=lowerCAmelCase__ )
def lowerCAmelCase__ ( self: Tuple ) -> Tuple:
__magic_name__ : int = self.scheduler_classes[0]
__magic_name__ : str = self.get_scheduler_config(variance_type="fixed_small_log" )
__magic_name__ : List[str] = scheduler_class(**lowerCAmelCase__ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.0_000E-10 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_5_4_9_6_2_5 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.9_9_9_4_9_8_7 ) ) < 1E-5
def lowerCAmelCase__ ( self: Optional[Any] ) -> Optional[Any]:
__magic_name__ : List[Any] = self.scheduler_classes[0]
__magic_name__ : Tuple = self.get_scheduler_config(variance_type="learned_range" )
__magic_name__ : Tuple = scheduler_class(**lowerCAmelCase__ )
__magic_name__ : List[Any] = 0.5
assert scheduler._get_variance(1 , predicted_variance=lowerCAmelCase__ ) - -10.171_2790 < 1E-5
assert scheduler._get_variance(487 , predicted_variance=lowerCAmelCase__ ) - -5.7_9_9_8_0_5_2 < 1E-5
assert scheduler._get_variance(999 , predicted_variance=lowerCAmelCase__ ) - -0.0_0_1_0_0_1_1 < 1E-5
def lowerCAmelCase__ ( self: List[Any] ) -> Union[str, Any]:
__magic_name__ : Dict = self.scheduler_classes[0]
__magic_name__ : Optional[int] = self.get_scheduler_config()
__magic_name__ : Tuple = scheduler_class(**lowerCAmelCase__ )
__magic_name__ : Optional[int] = scheduler.timesteps
__magic_name__ : int = self.dummy_model()
__magic_name__ : List[Any] = self.dummy_sample_deter
__magic_name__ : Optional[int] = torch.manual_seed(0 )
for i, t in enumerate(lowerCAmelCase__ ):
# 1. predict noise residual
__magic_name__ : Optional[Any] = model(lowerCAmelCase__ , lowerCAmelCase__ )
# 2. predict previous mean of sample x_t-1
__magic_name__ : Dict = scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
__magic_name__ : Tuple = pred_prev_sample
__magic_name__ : int = torch.sum(torch.abs(lowerCAmelCase__ ) )
__magic_name__ : Any = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 252.268_2495 ) < 1E-2
assert abs(result_mean.item() - 0.3_2_8_4_7_4_3 ) < 1E-3
def lowerCAmelCase__ ( self: Dict ) -> List[Any]:
__magic_name__ : Optional[int] = self.scheduler_classes[0]
__magic_name__ : Optional[Any] = self.get_scheduler_config()
__magic_name__ : Any = scheduler_class(**lowerCAmelCase__ )
scheduler.set_timesteps(25 )
__magic_name__ : Optional[int] = scheduler.timesteps
__magic_name__ : Union[str, Any] = self.dummy_model()
__magic_name__ : Union[str, Any] = self.dummy_sample_deter
__magic_name__ : Union[str, Any] = torch.manual_seed(0 )
for i, t in enumerate(lowerCAmelCase__ ):
# 1. predict noise residual
__magic_name__ : List[str] = model(lowerCAmelCase__ , lowerCAmelCase__ )
if i + 1 == timesteps.shape[0]:
__magic_name__ : int = None
else:
__magic_name__ : Dict = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
__magic_name__ : str = scheduler.step(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , prev_timestep=lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
__magic_name__ : Any = pred_prev_sample
__magic_name__ : List[Any] = torch.sum(torch.abs(lowerCAmelCase__ ) )
__magic_name__ : List[Any] = torch.mean(torch.abs(lowerCAmelCase__ ) )
assert abs(result_sum.item() - 258.204_4983 ) < 1E-2
assert abs(result_mean.item() - 0.3_3_6_2_0_3_8 ) < 1E-3
def lowerCAmelCase__ ( self: List[Any] ) -> Union[str, Any]:
pass
def lowerCAmelCase__ ( self: str ) -> List[Any]:
pass | 436 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square(__magic_name__ ,__magic_name__ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_area_of_max_square(__magic_name__ ,col + 1 )
snake_case_ : Dict = update_area_of_max_square(row + 1 ,col + 1 )
snake_case_ : int = update_area_of_max_square(row + 1 ,__magic_name__ )
if mat[row][col]:
snake_case_ : str = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
return sub_problem_sol
else:
return 0
snake_case_ : Union[str, Any] = [0]
update_area_of_max_square(0 ,0 )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
__magic_name__ ,__magic_name__ ,__magic_name__ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
snake_case_ : Dict = update_area_of_max_square_using_dp_array(__magic_name__ ,col + 1 ,__magic_name__ )
snake_case_ : List[Any] = update_area_of_max_square_using_dp_array(row + 1 ,col + 1 ,__magic_name__ )
snake_case_ : Any = update_area_of_max_square_using_dp_array(row + 1 ,__magic_name__ ,__magic_name__ )
if mat[row][col]:
snake_case_ : int = 1 + min([right, diagonal, down] )
snake_case_ : Tuple = max(largest_square_area[0] ,__magic_name__ )
snake_case_ : Optional[Any] = sub_problem_sol
return sub_problem_sol
else:
return 0
snake_case_ : List[Any] = [0]
snake_case_ : Optional[int] = [[-1] * cols for _ in range(__magic_name__ )]
update_area_of_max_square_using_dp_array(0 ,0 ,__magic_name__ )
return largest_square_area[0]
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Dict = [[0] * (cols + 1) for _ in range(rows + 1 )]
snake_case_ : Dict = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : List[str] = dp_array[row][col + 1]
snake_case_ : Any = dp_array[row + 1][col + 1]
snake_case_ : Any = dp_array[row + 1][col]
if mat[row][col] == 1:
snake_case_ : Any = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : str = max(dp_array[row][col] ,__magic_name__ )
else:
snake_case_ : Optional[Any] = 0
return largest_square_area
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : str = [0] * (cols + 1)
snake_case_ : Tuple = [0] * (cols + 1)
snake_case_ : List[str] = 0
for row in range(rows - 1 ,-1 ,-1 ):
for col in range(cols - 1 ,-1 ,-1 ):
snake_case_ : Optional[Any] = current_row[col + 1]
snake_case_ : Optional[int] = next_row[col + 1]
snake_case_ : Dict = next_row[col]
if mat[row][col] == 1:
snake_case_ : Union[str, Any] = 1 + min(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Any = max(current_row[col] ,__magic_name__ )
else:
snake_case_ : Dict = 0
snake_case_ : Optional[Any] = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 653 | 0 |
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCAmelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
lowerCAmelCase = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def _lowerCamelCase( lowercase__ ) -> list[list[int]]:
'''simple docstring'''
__lowercase= []
for i in range(len(lowercase__ ) ):
__lowercase= []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
__lowercase= 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(lowercase__ ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(lowercase__ ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(lowercase__ ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
__lowercase= cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(lowercase__ )
return next_generation
def _lowerCamelCase( lowercase__ , lowercase__ ) -> list[Image.Image]:
'''simple docstring'''
__lowercase= []
for _ in range(lowercase__ ):
# Create output image
__lowercase= Image.new('RGB' , (len(cells[0] ), len(lowercase__ )) )
__lowercase= img.load()
# Save cells to image
for x in range(len(lowercase__ ) ):
for y in range(len(cells[0] ) ):
__lowercase= 2_5_5 - cells[y][x] * 2_5_5
__lowercase= (colour, colour, colour)
# Save image
images.append(lowercase__ )
__lowercase= new_generation(lowercase__ )
return images
if __name__ == "__main__":
lowerCAmelCase = generate_images(GLIDER, 1_6)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 230 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : List[str] = None
if token is not None:
snake_case_ : List[str] = {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
snake_case_ : Dict = "636036"
snake_case_ : List[str] = F'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
snake_case_ : Optional[Any] = requests.get(__magic_name__ ,headers=__magic_name__ ).json()
return result["workflow_runs"]
def __UpperCAmelCase ( __magic_name__ )-> Union[str, Any]:
"""simple docstring"""
snake_case_ : str = get_daily_ci_runs(__magic_name__ )
snake_case_ : Optional[int] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
snake_case_ : Dict = workflow_run["id"]
break
return workflow_run_id
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[Any] = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
snake_case_ : Union[str, Any] = get_artifacts_links(worflow_run_id=__magic_name__ ,token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
snake_case_ : Union[str, Any] = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ ,artifact_url=__magic_name__ ,output_dir=__magic_name__ ,token=__magic_name__ )
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : Union[str, Any] = {}
for artifact_name in artifact_names:
snake_case_ : Any = os.path.join(__magic_name__ ,F'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
snake_case_ : Tuple = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
snake_case_ : Optional[Any] = f.read().decode("UTF-8" )
return results
| 653 | 0 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
lowerCAmelCase : Optional[int] = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase : Dict = get_tests_dir('fixtures/vocab.json')
lowerCAmelCase : Optional[int] = get_tests_dir('fixtures')
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
lowerCAmelCase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
UpperCamelCase = 0
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
UpperCamelCase = AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = WavaVecaConfig()
UpperCamelCase = AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h' )
# save in new folder
model_config.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
UpperCamelCase = AutoProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(lowerCAmelCase__ , os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) )
copyfile(lowerCAmelCase__ , os.path.join(lowerCAmelCase__ , 'vocab.json' ) )
UpperCamelCase = AutoProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = WavaVecaFeatureExtractor()
UpperCamelCase = AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
UpperCamelCase = WavaVecaProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
# save in new folder
processor.save_pretrained(lowerCAmelCase__ )
# drop `processor_class` in tokenizer
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , 'r' ) as f:
UpperCamelCase = json.load(lowerCAmelCase__ )
config_dict.pop('processor_class' )
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , 'w' ) as f:
f.write(json.dumps(lowerCAmelCase__ ) )
UpperCamelCase = AutoProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = WavaVecaFeatureExtractor()
UpperCamelCase = AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h' )
UpperCamelCase = WavaVecaProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
# save in new folder
processor.save_pretrained(lowerCAmelCase__ )
# drop `processor_class` in feature extractor
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , 'r' ) as f:
UpperCamelCase = json.load(lowerCAmelCase__ )
config_dict.pop('processor_class' )
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , 'w' ) as f:
f.write(json.dumps(lowerCAmelCase__ ) )
UpperCamelCase = AutoProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase = WavaVecaConfig(processor_class='Wav2Vec2Processor' )
model_config.save_pretrained(lowerCAmelCase__ )
# copy relevant files
copyfile(lowerCAmelCase__ , os.path.join(lowerCAmelCase__ , 'vocab.json' ) )
# create emtpy sample processor
with open(os.path.join(lowerCAmelCase__ , lowerCAmelCase__ ) , 'w' ) as f:
f.write('{}' )
UpperCamelCase = AutoProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
with self.assertRaises(lowerCAmelCase__ ):
UpperCamelCase = AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCAmelCase__ ):
UpperCamelCase = AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowerCAmelCase__ )
UpperCamelCase = AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowerCAmelCase__ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
UpperCamelCase = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' )
UpperCamelCase = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizerFast' )
# Test we can also load the slow version
UpperCamelCase = AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowerCAmelCase__ , use_fast=lowerCAmelCase__ )
UpperCamelCase = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , 'NewTokenizer' )
else:
self.assertEqual(tokenizer.__class__.__name__ , 'NewTokenizer' )
def UpperCAmelCase_ ( self )-> Optional[int]:
'''simple docstring'''
try:
AutoConfig.register('custom' , lowerCAmelCase__ )
AutoFeatureExtractor.register(lowerCAmelCase__ , lowerCAmelCase__ )
AutoTokenizer.register(lowerCAmelCase__ , slow_tokenizer_class=lowerCAmelCase__ )
AutoProcessor.register(lowerCAmelCase__ , lowerCAmelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCAmelCase__ ):
AutoProcessor.register(lowerCAmelCase__ , lowerCAmelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
UpperCamelCase = CustomFeatureExtractor.from_pretrained(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase = os.path.join(lowerCAmelCase__ , 'vocab.txt' )
with open(lowerCAmelCase__ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
UpperCamelCase = CustomTokenizer(lowerCAmelCase__ )
UpperCamelCase = CustomProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(lowerCAmelCase__ )
UpperCamelCase = AutoProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
class SCREAMING_SNAKE_CASE__ ( a_):
lowerCAmelCase_ = False
class SCREAMING_SNAKE_CASE__ ( a_):
lowerCAmelCase_ = False
class SCREAMING_SNAKE_CASE__ ( a_):
lowerCAmelCase_ = """AutoFeatureExtractor"""
lowerCAmelCase_ = """AutoTokenizer"""
lowerCAmelCase_ = False
try:
AutoConfig.register('custom' , lowerCAmelCase__ )
AutoFeatureExtractor.register(lowerCAmelCase__ , lowerCAmelCase__ )
AutoTokenizer.register(lowerCAmelCase__ , slow_tokenizer_class=lowerCAmelCase__ )
AutoProcessor.register(lowerCAmelCase__ , lowerCAmelCase__ )
# If remote code is not set, the default is to use local classes.
UpperCamelCase = AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor' )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
UpperCamelCase = AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
UpperCamelCase = AutoProcessor.from_pretrained(
'hf-internal-testing/test_dynamic_processor' , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(processor.__class__.__name__ , 'NewProcessor' )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase = AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert' )
self.assertEqual(processor.__class__.__name__ , 'BertTokenizerFast' )
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
UpperCamelCase = AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext' )
self.assertEqual(processor.__class__.__name__ , 'ConvNextImageProcessor' )
@is_staging_test
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
lowerCAmelCase_ = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
@classmethod
def UpperCAmelCase_ ( cls )-> Any:
'''simple docstring'''
UpperCamelCase = TOKEN
HfFolder.save_token(lowerCAmelCase__ )
@classmethod
def UpperCAmelCase_ ( cls )-> int:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-processor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-processor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-processor' )
except HTTPError:
pass
def UpperCAmelCase_ ( self )-> Tuple:
'''simple docstring'''
UpperCamelCase = WavaVecaProcessor.from_pretrained(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowerCAmelCase__ , 'test-processor' ) , push_to_hub=lowerCAmelCase__ , use_auth_token=self._token )
UpperCamelCase = WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor''' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowerCAmelCase__ , getattr(new_processor.feature_extractor , lowerCAmelCase__ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase = WavaVecaProcessor.from_pretrained(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(lowerCAmelCase__ , 'test-processor-org' ) , push_to_hub=lowerCAmelCase__ , use_auth_token=self._token , organization='valid_org' , )
UpperCamelCase = WavaVecaProcessor.from_pretrained('valid_org/test-processor-org' )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(lowerCAmelCase__ , getattr(new_processor.feature_extractor , lowerCAmelCase__ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
UpperCamelCase = CustomFeatureExtractor.from_pretrained(lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase = os.path.join(lowerCAmelCase__ , 'vocab.txt' )
with open(lowerCAmelCase__ , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens] ) )
UpperCamelCase = CustomTokenizer(lowerCAmelCase__ )
UpperCamelCase = CustomProcessor(lowerCAmelCase__ , lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F'''{USER}/test-dynamic-processor''' , token=self._token )
UpperCamelCase = Repository(lowerCAmelCase__ , clone_from=F'''{USER}/test-dynamic-processor''' , token=self._token )
processor.save_pretrained(lowerCAmelCase__ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(lowerCAmelCase__ , 'tokenizer_config.json' ) ) as f:
UpperCamelCase = json.load(lowerCAmelCase__ )
self.assertDictEqual(
tokenizer_config['auto_map'] , {
'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
'AutoProcessor': 'custom_processing.CustomProcessor',
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ , 'custom_feature_extraction.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ , 'custom_tokenization.py' ) ) )
self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ , 'custom_processing.py' ) ) )
repo.push_to_hub()
UpperCamelCase = AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor''' , trust_remote_code=lowerCAmelCase__ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , 'CustomProcessor' )
| 3 |
'''simple docstring'''
from string import ascii_uppercase
__lowerCamelCase : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
__lowerCamelCase : List[str] = dict(enumerate(ascii_uppercase))
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Tuple = len(__magic_name__ )
snake_case_ : str = 0
while True:
if x == i:
snake_case_ : List[str] = 0
if len(__magic_name__ ) == len(__magic_name__ ):
break
key += key[i]
i += 1
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : str = ""
snake_case_ : List[Any] = 0
for letter in message:
if letter == " ":
cipher_text += " "
else:
snake_case_ : Optional[Any] = (dicta[letter] - dicta[key_new[i]]) % 26
i += 1
cipher_text += dicta[x]
return cipher_text
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = ""
snake_case_ : Dict = 0
for letter in cipher_text:
if letter == " ":
or_txt += " "
else:
snake_case_ : str = (dicta[letter] + dicta[key_new[i]] + 26) % 26
i += 1
or_txt += dicta[x]
return or_txt
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
snake_case_ : List[str] = "THE GERMAN ATTACK"
snake_case_ : List[str] = "SECRET"
snake_case_ : Optional[int] = generate_key(__magic_name__ ,__magic_name__ )
snake_case_ : Any = cipher_text(__magic_name__ ,__magic_name__ )
print(F'''Encrypted Text = {s}''' )
print(F'''Original Text = {original_text(__magic_name__ ,__magic_name__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 653 | 0 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
AutoFeatureExtractor,
AutoModelForAudioClassification,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCAmelCase : Optional[int] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")
def snake_case__ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 1_60_00 ) -> Optional[int]:
_UpperCamelCase : List[str] = int(round(sample_rate * max_length ) )
if len(UpperCamelCase ) <= sample_length:
return wav
_UpperCamelCase : str = randint(0 ,len(UpperCamelCase ) - sample_length - 1 )
return wav[random_offset : random_offset + sample_length]
@dataclass
class UpperCAmelCase :
"""simple docstring"""
A__ : Union[str, Any] = field(default=a_ , metadata={'help': 'Name of a dataset from the datasets package'} )
A__ : int = field(
default=a_ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
A__ : Optional[Any] = field(
default=a_ , metadata={'help': 'A file containing the training audio paths and labels.'} )
A__ : int = field(
default=a_ , metadata={'help': 'A file containing the validation audio paths and labels.'} )
A__ : Optional[int] = field(
default='train' , metadata={
'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
} , )
A__ : str = field(
default='validation' , metadata={
'help': (
'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
)
} , )
A__ : List[str] = field(
default='audio' , metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''} , )
A__ : Tuple = field(
default='label' , metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''} )
A__ : Tuple = field(
default=a_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
A__ : List[Any] = field(
default=a_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
A__ : List[str] = field(
default=20 , metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'} , )
@dataclass
class UpperCAmelCase :
"""simple docstring"""
A__ : Tuple = field(
default='facebook/wav2vec2-base' , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} , )
A__ : Dict = field(
default=a_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
A__ : List[Any] = field(
default=a_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'} )
A__ : Tuple = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
A__ : str = field(
default=a_ , metadata={'help': 'Name or path of preprocessor config.'} )
A__ : Optional[int] = field(
default=a_ , metadata={'help': 'Whether to freeze the feature encoder layers of the model.'} )
A__ : Optional[int] = field(
default=a_ , metadata={'help': 'Whether to generate an attention mask in the feature extractor.'} )
A__ : Optional[Any] = field(
default=a_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
A__ : Tuple = field(
default=a_ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
A__ : List[str] = field(
default=a_ , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def _lowercase ( self ) -> str:
if not self.freeze_feature_extractor and self.freeze_feature_encoder:
warnings.warn(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''will be removed in a future version. Use `--freeze_feature_encoder`'''
'''instead. Setting `freeze_feature_encoder==True`.''' , lowerCAmelCase__ , )
if self.freeze_feature_extractor and not self.freeze_feature_encoder:
raise ValueError(
'''The argument `--freeze_feature_extractor` is deprecated and '''
'''should not be used in combination with `--freeze_feature_encoder`.'''
'''Only make use of `--freeze_feature_encoder`.''' )
def snake_case__ ( ) -> Tuple:
_UpperCamelCase : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_UpperCamelCase : Union[str, Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCamelCase : str = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_audio_classification''' ,UpperCamelCase ,UpperCamelCase )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,datefmt='''%m/%d/%Y %H:%M:%S''' ,handlers=[logging.StreamHandler(sys.stdout )] ,)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_UpperCamelCase : Optional[Any] = training_args.get_process_log_level()
logger.setLevel(UpperCamelCase )
transformers.utils.logging.set_verbosity(UpperCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Detecting last checkpoint.
_UpperCamelCase : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_UpperCamelCase : str = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'''Use --overwrite_output_dir to train from scratch.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset and prepare it for the audio classification task.
_UpperCamelCase : int = DatasetDict()
_UpperCamelCase : Dict = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.train_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
_UpperCamelCase : Optional[Any] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=data_args.eval_split_name ,use_auth_token=True if model_args.use_auth_token else None ,)
if data_args.audio_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'''--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
'''Make sure to set `--audio_column_name` to the correct audio column - one of '''
f'''{', '.join(raw_datasets['train'].column_names )}.''' )
if data_args.label_column_name not in raw_datasets["train"].column_names:
raise ValueError(
f'''--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '''
'''Make sure to set `--label_column_name` to the correct text column - one of '''
f'''{', '.join(raw_datasets['train'].column_names )}.''' )
# Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
# transformer outputs in the classifier, but it doesn't always lead to better accuracy
_UpperCamelCase : List[str] = AutoFeatureExtractor.from_pretrained(
model_args.feature_extractor_name or model_args.model_name_or_path ,return_attention_mask=model_args.attention_mask ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
# `datasets` takes care of automatically loading and resampling the audio,
# so we just need to set the correct target sampling rate.
_UpperCamelCase : List[str] = raw_datasets.cast_column(
data_args.audio_column_name ,datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate ) )
_UpperCamelCase : Optional[Any] = feature_extractor.model_input_names[0]
def train_transforms(UpperCamelCase ):
_UpperCamelCase : List[str] = []
for audio in batch[data_args.audio_column_name]:
_UpperCamelCase : List[Any] = random_subsample(
audio['''array'''] ,max_length=data_args.max_length_seconds ,sample_rate=feature_extractor.sampling_rate )
subsampled_wavs.append(UpperCamelCase )
_UpperCamelCase : Any = feature_extractor(UpperCamelCase ,sampling_rate=feature_extractor.sampling_rate )
_UpperCamelCase : Optional[Any] = {model_input_name: inputs.get(UpperCamelCase )}
_UpperCamelCase : Dict = list(batch[data_args.label_column_name] )
return output_batch
def val_transforms(UpperCamelCase ):
_UpperCamelCase : Tuple = [audio["array"] for audio in batch[data_args.audio_column_name]]
_UpperCamelCase : Union[str, Any] = feature_extractor(UpperCamelCase ,sampling_rate=feature_extractor.sampling_rate )
_UpperCamelCase : Dict = {model_input_name: inputs.get(UpperCamelCase )}
_UpperCamelCase : Union[str, Any] = list(batch[data_args.label_column_name] )
return output_batch
# Prepare label mappings.
# We'll include these in the model's config to get human readable labels in the Inference API.
_UpperCamelCase : str = raw_datasets["train"].features[data_args.label_column_name].names
_UpperCamelCase : Tuple = {}, {}
for i, label in enumerate(UpperCamelCase ):
_UpperCamelCase : Dict = str(UpperCamelCase )
_UpperCamelCase : Union[str, Any] = label
# Load the accuracy metric from the datasets package
_UpperCamelCase : int = evaluate.load('''accuracy''' )
# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
# `predictions` and `label_ids` fields) and has to return a dictionary string to float.
def compute_metrics(UpperCamelCase ):
_UpperCamelCase : Tuple = np.argmax(eval_pred.predictions ,axis=1 )
return metric.compute(predictions=UpperCamelCase ,references=eval_pred.label_ids )
_UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name or model_args.model_name_or_path ,num_labels=len(UpperCamelCase ) ,labelaid=UpperCamelCase ,idalabel=UpperCamelCase ,finetuning_task='''audio-classification''' ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,)
_UpperCamelCase : Optional[Any] = AutoModelForAudioClassification.from_pretrained(
model_args.model_name_or_path ,from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,config=UpperCamelCase ,cache_dir=model_args.cache_dir ,revision=model_args.model_revision ,use_auth_token=True if model_args.use_auth_token else None ,ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,)
# freeze the convolutional waveform encoder
if model_args.freeze_feature_encoder:
model.freeze_feature_encoder()
if training_args.do_train:
if data_args.max_train_samples is not None:
_UpperCamelCase : int = (
raw_datasets["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
)
# Set the training transforms
raw_datasets["train"].set_transform(UpperCamelCase ,output_all_columns=UpperCamelCase )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
_UpperCamelCase : Tuple = (
raw_datasets["eval"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
raw_datasets["eval"].set_transform(UpperCamelCase ,output_all_columns=UpperCamelCase )
# Initialize our trainer
_UpperCamelCase : List[str] = Trainer(
model=UpperCamelCase ,args=UpperCamelCase ,train_dataset=raw_datasets['''train'''] if training_args.do_train else None ,eval_dataset=raw_datasets['''eval'''] if training_args.do_eval else None ,compute_metrics=UpperCamelCase ,tokenizer=UpperCamelCase ,)
# Training
if training_args.do_train:
_UpperCamelCase : Union[str, Any] = None
if training_args.resume_from_checkpoint is not None:
_UpperCamelCase : str = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_UpperCamelCase : List[Any] = last_checkpoint
_UpperCamelCase : str = trainer.train(resume_from_checkpoint=UpperCamelCase )
trainer.save_model()
trainer.log_metrics('''train''' ,train_result.metrics )
trainer.save_metrics('''train''' ,train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_UpperCamelCase : List[Any] = trainer.evaluate()
trainer.log_metrics('''eval''' ,UpperCamelCase )
trainer.save_metrics('''eval''' ,UpperCamelCase )
# Write model card and (optionally) push to hub
_UpperCamelCase : Dict = {
"finetuned_from": model_args.model_name_or_path,
"tasks": "audio-classification",
"dataset": data_args.dataset_name,
"tags": ["audio-classification"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**UpperCamelCase )
else:
trainer.create_model_card(**UpperCamelCase )
if __name__ == "__main__":
main()
| 683 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")
snake_case_ : Union[str, Any] = (
("layer.", "layer_"),
("word_embeddings.weight", "word_embeddings"),
("position_embeddings.weight", "position_embeddings"),
("token_type_embeddings.weight", "token_type_embeddings"),
(".", "/"),
("LayerNorm/weight", "LayerNorm/gamma"),
("LayerNorm/bias", "LayerNorm/beta"),
("weight", "kernel"),
)
if not os.path.isdir(__magic_name__ ):
os.makedirs(__magic_name__ )
snake_case_ : str = model.state_dict()
def to_tf_var_name(__magic_name__ ):
for patt, repl in iter(__magic_name__ ):
snake_case_ : List[str] = name.replace(__magic_name__ ,__magic_name__ )
return F'''bert/{name}'''
def create_tf_var(__magic_name__ ,__magic_name__ ,__magic_name__ ):
snake_case_ : List[Any] = tf.dtypes.as_dtype(tensor.dtype )
snake_case_ : Union[str, Any] = tf.get_variable(dtype=__magic_name__ ,shape=tensor.shape ,name=__magic_name__ ,initializer=tf.zeros_initializer() )
session.run(tf.variables_initializer([tf_var] ) )
session.run(__magic_name__ )
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
snake_case_ : Optional[int] = to_tf_var_name(__magic_name__ )
snake_case_ : Dict = state_dict[var_name].numpy()
if any(x in var_name for x in tensors_to_transpose ):
snake_case_ : List[Any] = torch_tensor.T
snake_case_ : Union[str, Any] = create_tf_var(tensor=__magic_name__ ,name=__magic_name__ ,session=__magic_name__ )
tf.keras.backend.set_value(__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = session.run(__magic_name__ )
print(F'''Successfully created {tf_name}: {np.allclose(__magic_name__ ,__magic_name__ )}''' )
snake_case_ : Any = tf.train.Saver(tf.trainable_variables() )
saver.save(__magic_name__ ,os.path.join(__magic_name__ ,model_name.replace("-" ,"_" ) + ".ckpt" ) )
def __UpperCAmelCase ( __magic_name__=None )-> Optional[Any]:
"""simple docstring"""
snake_case_ : Any = argparse.ArgumentParser()
parser.add_argument("--model_name" ,type=__magic_name__ ,required=__magic_name__ ,help="model name e.g. bert-base-uncased" )
parser.add_argument(
"--cache_dir" ,type=__magic_name__ ,default=__magic_name__ ,required=__magic_name__ ,help="Directory containing pytorch model" )
parser.add_argument("--pytorch_model_path" ,type=__magic_name__ ,required=__magic_name__ ,help="/path/to/<pytorch-model-name>.bin" )
parser.add_argument("--tf_cache_dir" ,type=__magic_name__ ,required=__magic_name__ ,help="Directory in which to save tensorflow model" )
snake_case_ : Optional[int] = parser.parse_args(__magic_name__ )
snake_case_ : Optional[int] = BertModel.from_pretrained(
pretrained_model_name_or_path=args.model_name ,state_dict=torch.load(args.pytorch_model_path ) ,cache_dir=args.cache_dir ,)
convert_pytorch_checkpoint_to_tf(model=__magic_name__ ,ckpt_dir=args.tf_cache_dir ,model_name=args.model_name )
if __name__ == "__main__":
main()
| 653 | 0 |
import os
import sys
import transformers
A : Any = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 176 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class A_ (a_ ):
"""simple docstring"""
def __init__( self :List[str] , *lowerCAmelCase__ :Optional[Any] , **lowerCAmelCase__ :Dict ) -> Union[str, Any]:
'''simple docstring'''
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int , lowerCAmelCase__ :Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Optional[int] = deque([] ) if self.values[key] is None else self.values[key]
self.values[key].appendleft(lowerCAmelCase__ )
snake_case_ : Tuple = self.values[key]
def _A ( self :int ) -> Dict:
'''simple docstring'''
return (
sum(self.charge_factor - len(lowerCAmelCase__ ) for slot in self.values )
/ self.size_table
* self.charge_factor
)
def _A ( self :str , lowerCAmelCase__ :Optional[Any] , lowerCAmelCase__ :Tuple=None ) -> Any:
'''simple docstring'''
if not (
len(self.values[key] ) == self.charge_factor and self.values.count(lowerCAmelCase__ ) == 0
):
return key
return super()._collision_resolution(lowerCAmelCase__ , lowerCAmelCase__ )
| 653 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
lowerCAmelCase__ :str = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
lowerCAmelCase__ :int = {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
}
lowerCAmelCase__ :List[Any] = {
"feature_size": 1,
"padding_value": 0.0,
"sampling_rate": 1_6_0_0_0,
"return_attention_mask": False,
"do_normalize": True,
}
lowerCAmelCase__ :Optional[int] = tempfile.mkdtemp()
lowerCAmelCase__ :Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCAmelCase__ :Optional[Any] = os.path.join(self.tmpdirname , lowerCAmelCase__ )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + '\n' )
with open(self.feature_extraction_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + '\n' )
# load decoder from hub
lowerCAmelCase__ :str = "hf-internal-testing/ngram-beam-search-decoder"
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :List[Any] = self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCAmelCase__ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def snake_case ( self , **__UpperCAmelCase ):
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase__ :Optional[Any] = self.get_feature_extractor()
lowerCAmelCase__ :str = self.get_decoder()
lowerCAmelCase__ :Optional[int] = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase__ :Any = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase__ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , lowerCAmelCase__ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
lowerCAmelCase__ :Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['xx'] )
with self.assertRaisesRegex(lowerCAmelCase__ , 'include' ):
WavaVecaProcessorWithLM(
tokenizer=lowerCAmelCase__ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = self.get_feature_extractor()
lowerCAmelCase__ :Tuple = self.get_tokenizer()
lowerCAmelCase__ :Optional[Any] = self.get_decoder()
lowerCAmelCase__ :Optional[Any] = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
lowerCAmelCase__ :Optional[Any] = floats_list((3, 1_0_0_0) )
lowerCAmelCase__ :Tuple = feature_extractor(lowerCAmelCase__ , return_tensors='np' )
lowerCAmelCase__ :List[str] = processor(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 snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.get_feature_extractor()
lowerCAmelCase__ :Any = self.get_tokenizer()
lowerCAmelCase__ :List[str] = self.get_decoder()
lowerCAmelCase__ :Optional[int] = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
lowerCAmelCase__ :List[str] = "This is a test string"
lowerCAmelCase__ :int = processor(text=lowerCAmelCase__ )
lowerCAmelCase__ :Tuple = tokenizer(lowerCAmelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def snake_case ( self , __UpperCAmelCase=(2, 1_0, 1_6) , __UpperCAmelCase=7_7 ):
'''simple docstring'''
np.random.seed(lowerCAmelCase__ )
return np.random.rand(*lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.get_feature_extractor()
lowerCAmelCase__ :str = self.get_tokenizer()
lowerCAmelCase__ :Union[str, Any] = self.get_decoder()
lowerCAmelCase__ :Optional[int] = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
lowerCAmelCase__ :Optional[int] = self._get_dummy_logits(shape=(1_0, 1_6) , seed=1_3 )
lowerCAmelCase__ :Union[str, Any] = processor.decode(lowerCAmelCase__ )
lowerCAmelCase__ :Tuple = decoder.decode_beams(lowerCAmelCase__ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('</s> <s> </s>' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['fork'], ['spawn']] )
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = self.get_feature_extractor()
lowerCAmelCase__ :Optional[Any] = self.get_tokenizer()
lowerCAmelCase__ :List[str] = self.get_decoder()
lowerCAmelCase__ :Optional[Any] = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
lowerCAmelCase__ :Optional[int] = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
lowerCAmelCase__ :int = processor.batch_decode(lowerCAmelCase__ )
else:
with get_context(lowerCAmelCase__ ).Pool() as pool:
lowerCAmelCase__ :Union[str, Any] = processor.batch_decode(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase__ :Optional[int] = list(lowerCAmelCase__ )
with get_context('fork' ).Pool() as p:
lowerCAmelCase__ :List[str] = decoder.decode_beams_batch(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase__ :List[str] = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCAmelCase__ , decoded_processor.text )
self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>'] , decoded_processor.text )
self.assertListEqual(lowerCAmelCase__ , decoded_processor.logit_score )
self.assertListEqual(lowerCAmelCase__ , decoded_processor.lm_score )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = self.get_feature_extractor()
lowerCAmelCase__ :List[str] = self.get_tokenizer()
lowerCAmelCase__ :Optional[int] = self.get_decoder()
lowerCAmelCase__ :str = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
lowerCAmelCase__ :Union[str, Any] = self._get_dummy_logits()
lowerCAmelCase__ :int = 1_5
lowerCAmelCase__ :Optional[int] = -2_0.0
lowerCAmelCase__ :Any = -4.0
lowerCAmelCase__ :Any = processor.batch_decode(
lowerCAmelCase__ , beam_width=lowerCAmelCase__ , beam_prune_logp=lowerCAmelCase__ , token_min_logp=lowerCAmelCase__ , )
lowerCAmelCase__ :int = decoded_processor_out.text
lowerCAmelCase__ :Any = list(lowerCAmelCase__ )
with get_context('fork' ).Pool() as pool:
lowerCAmelCase__ :Dict = decoder.decode_beams_batch(
lowerCAmelCase__ , lowerCAmelCase__ , beam_width=lowerCAmelCase__ , beam_prune_logp=lowerCAmelCase__ , token_min_logp=lowerCAmelCase__ , )
lowerCAmelCase__ :Optional[int] = [d[0][0] for d in decoded_decoder_out]
lowerCAmelCase__ :str = [d[0][2] for d in decoded_decoder_out]
lowerCAmelCase__ :Dict = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>'] , lowerCAmelCase__ )
self.assertTrue(np.array_equal(lowerCAmelCase__ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] , lowerCAmelCase__ , atol=1E-3 ) )
self.assertTrue(np.array_equal(lowerCAmelCase__ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] , lowerCAmelCase__ , atol=1E-3 ) )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = self.get_feature_extractor()
lowerCAmelCase__ :Any = self.get_tokenizer()
lowerCAmelCase__ :List[str] = self.get_decoder()
lowerCAmelCase__ :Any = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
lowerCAmelCase__ :int = self._get_dummy_logits()
lowerCAmelCase__ :List[str] = 2.0
lowerCAmelCase__ :Any = 5.0
lowerCAmelCase__ :int = -2_0.0
lowerCAmelCase__ :Dict = True
lowerCAmelCase__ :Optional[int] = processor.batch_decode(
lowerCAmelCase__ , alpha=lowerCAmelCase__ , beta=lowerCAmelCase__ , unk_score_offset=lowerCAmelCase__ , lm_score_boundary=lowerCAmelCase__ , )
lowerCAmelCase__ :Union[str, Any] = decoded_processor_out.text
lowerCAmelCase__ :List[str] = list(lowerCAmelCase__ )
decoder.reset_params(
alpha=lowerCAmelCase__ , beta=lowerCAmelCase__ , unk_score_offset=lowerCAmelCase__ , lm_score_boundary=lowerCAmelCase__ , )
with get_context('fork' ).Pool() as pool:
lowerCAmelCase__ :Union[str, Any] = decoder.decode_beams_batch(
lowerCAmelCase__ , lowerCAmelCase__ , )
lowerCAmelCase__ :str = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'] , lowerCAmelCase__ )
lowerCAmelCase__ :Any = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -2_0.0 )
self.assertEqual(lm_model.score_boundary , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
lowerCAmelCase__ :str = processor.decoder.model_container[processor.decoder._model_key]
lowerCAmelCase__ :Tuple = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
lowerCAmelCase__ :Optional[int] = os.listdir(lowerCAmelCase__ )
lowerCAmelCase__ :Union[str, Any] = ["alphabet.json", "language_model"]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = snapshot_download('hf-internal-testing/processor_with_lm' )
lowerCAmelCase__ :Any = WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase__ )
lowerCAmelCase__ :Dict = processor.decoder.model_container[processor.decoder._model_key]
lowerCAmelCase__ :List[Any] = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
lowerCAmelCase__ :Tuple = os.listdir(lowerCAmelCase__ )
lowerCAmelCase__ :int = os.listdir(lowerCAmelCase__ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :int = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
lowerCAmelCase__ :List[str] = AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm' )
lowerCAmelCase__ :List[Any] = floats_list((3, 1_0_0_0) )
lowerCAmelCase__ :int = processor_wavaveca(lowerCAmelCase__ , return_tensors='np' )
lowerCAmelCase__ :Union[str, Any] = processor_auto(lowerCAmelCase__ , return_tensors='np' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
lowerCAmelCase__ :List[str] = self._get_dummy_logits()
lowerCAmelCase__ :List[Any] = processor_wavaveca.batch_decode(lowerCAmelCase__ )
lowerCAmelCase__ :Dict = processor_auto.batch_decode(lowerCAmelCase__ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Optional[int] = self.get_feature_extractor()
lowerCAmelCase__ :Dict = self.get_tokenizer()
lowerCAmelCase__ :List[Any] = self.get_decoder()
lowerCAmelCase__ :List[str] = WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , decoder=lowerCAmelCase__ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
@staticmethod
def snake_case ( __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Optional[Any] = [d[key] for d in offsets]
return retrieved_list
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Dict = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
lowerCAmelCase__ :List[Any] = self._get_dummy_logits()[0]
lowerCAmelCase__ :List[Any] = processor.decode(lowerCAmelCase__ , output_word_offsets=lowerCAmelCase__ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets'] , 'word' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'end_offset' ) , [1, 3, 5] )
def snake_case ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
lowerCAmelCase__ :List[Any] = self._get_dummy_logits()
lowerCAmelCase__ :Union[str, Any] = processor.batch_decode(lowerCAmelCase__ , output_word_offsets=lowerCAmelCase__ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertListEqual(
[' '.join(self.get_from_offsets(lowerCAmelCase__ , 'word' ) ) for o in outputs['word_offsets']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'end_offset' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def snake_case ( self ):
'''simple docstring'''
import torch
lowerCAmelCase__ :Optional[int] = load_dataset('common_voice' , 'en' , split='train' , streaming=lowerCAmelCase__ )
lowerCAmelCase__ :Tuple = ds.cast_column('audio' , datasets.Audio(sampling_rate=1_6_0_0_0 ) )
lowerCAmelCase__ :str = iter(lowerCAmelCase__ )
lowerCAmelCase__ :Dict = next(lowerCAmelCase__ )
lowerCAmelCase__ :Dict = AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
lowerCAmelCase__ :Any = WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
lowerCAmelCase__ :List[Any] = processor(sample['audio']['array'] , return_tensors='pt' ).input_values
with torch.no_grad():
lowerCAmelCase__ :int = model(lowerCAmelCase__ ).logits.cpu().numpy()
lowerCAmelCase__ :Tuple = processor.decode(logits[0] , output_word_offsets=lowerCAmelCase__ )
lowerCAmelCase__ :Tuple = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
lowerCAmelCase__ :List[str] = [
{
"start_time": d["start_offset"] * time_offset,
"end_time": d["end_offset"] * time_offset,
"word": d["word"],
}
for d in output["word_offsets"]
]
lowerCAmelCase__ :str = "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"
# output words
self.assertEqual(' '.join(self.get_from_offsets(lowerCAmelCase__ , 'word' ) ) , lowerCAmelCase__ )
self.assertEqual(' '.join(self.get_from_offsets(lowerCAmelCase__ , 'word' ) ) , output.text )
# output times
lowerCAmelCase__ :List[str] = torch.tensor(self.get_from_offsets(lowerCAmelCase__ , 'start_time' ) )
lowerCAmelCase__ :Any = torch.tensor(self.get_from_offsets(lowerCAmelCase__ , 'end_time' ) )
# fmt: off
lowerCAmelCase__ :int = torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99] )
lowerCAmelCase__ :List[Any] = torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=0.01 ) )
self.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=0.01 ) )
| 93 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
__lowerCamelCase : Dict = TypeVar('''KEY''')
__lowerCamelCase : int = TypeVar('''VAL''')
@dataclass(frozen=a_ , slots=a_ )
class A_ (Generic[KEY, VAL] ):
"""simple docstring"""
a__ = 42
a__ = 42
class A_ (_Item ):
"""simple docstring"""
def __init__( self :List[Any] ) -> None:
'''simple docstring'''
super().__init__(lowerCAmelCase__ , lowerCAmelCase__ )
def __bool__( self :Optional[int] ) -> bool:
'''simple docstring'''
return False
__lowerCamelCase : Dict = _DeletedItem()
class A_ (MutableMapping[KEY, VAL] ):
"""simple docstring"""
def __init__( self :Dict , lowerCAmelCase__ :int = 8 , lowerCAmelCase__ :float = 0.7_5 ) -> None:
'''simple docstring'''
snake_case_ : Any = initial_block_size
snake_case_ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
snake_case_ : Tuple = capacity_factor
snake_case_ : List[Any] = 0
def _A ( self :Tuple , lowerCAmelCase__ :KEY ) -> int:
'''simple docstring'''
return hash(lowerCAmelCase__ ) % len(self._buckets )
def _A ( self :Any , lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def _A ( self :str , lowerCAmelCase__ :int , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> bool:
'''simple docstring'''
snake_case_ : Optional[int] = self._buckets[ind]
if not stored:
snake_case_ : int = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
self._len += 1
return True
elif stored.key == key:
snake_case_ : Optional[int] = _Item(lowerCAmelCase__ , lowerCAmelCase__ )
return True
else:
return False
def _A ( self :int ) -> bool:
'''simple docstring'''
snake_case_ : Any = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCAmelCase__ )
def _A ( self :Any ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
snake_case_ : Optional[int] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _A ( self :Tuple , lowerCAmelCase__ :int ) -> None:
'''simple docstring'''
snake_case_ : Tuple = self._buckets
snake_case_ : int = [None] * new_size
snake_case_ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _A ( self :Optional[int] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def _A ( self :str ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def _A ( self :Optional[int] , lowerCAmelCase__ :KEY ) -> Iterator[int]:
'''simple docstring'''
snake_case_ : str = self._get_bucket_index(lowerCAmelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
snake_case_ : List[Any] = self._get_next_ind(lowerCAmelCase__ )
def _A ( self :Union[str, Any] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
if self._try_set(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
break
def __setitem__( self :Optional[int] , lowerCAmelCase__ :KEY , lowerCAmelCase__ :VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCAmelCase__ , lowerCAmelCase__ )
def __delitem__( self :List[Any] , lowerCAmelCase__ :KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : int = self._buckets[ind]
if item is None:
raise KeyError(lowerCAmelCase__ )
if item is _deleted:
continue
if item.key == key:
snake_case_ : List[str] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self :List[str] , lowerCAmelCase__ :KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCAmelCase__ ):
snake_case_ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(lowerCAmelCase__ )
def __len__( self :Optional[Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self :List[Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self :Any ) -> str:
'''simple docstring'''
snake_case_ : Dict = " ,".join(
F'''{item.key}: {item.val}''' for item in self._buckets if item )
return F'''HashMap({val_string})'''
| 653 | 0 |
'''simple docstring'''
import math
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : List[str] ):
UpperCAmelCase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : Dict = 1 / 1_2345 ):
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 3
while True:
UpperCAmelCase = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(SCREAMING_SNAKE_CASE ):
UpperCAmelCase = int(SCREAMING_SNAKE_CASE )
total_partitions += 1
if check_partition_perfect(SCREAMING_SNAKE_CASE ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(SCREAMING_SNAKE_CASE )
integer += 1
if __name__ == "__main__":
print(F'''{solution() = }''')
| 447 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Tuple = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''gpt_bigcode'''
a__ = ['''past_key_values''']
a__ = {
'''hidden_size''': '''n_embd''',
'''max_position_embeddings''': '''n_positions''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self :List[Any] , lowerCAmelCase__ :Any=50_257 , lowerCAmelCase__ :Dict=1_024 , lowerCAmelCase__ :Optional[int]=768 , lowerCAmelCase__ :Tuple=12 , lowerCAmelCase__ :int=12 , lowerCAmelCase__ :Optional[Any]=None , lowerCAmelCase__ :List[str]="gelu_pytorch_tanh" , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :Tuple=0.1 , lowerCAmelCase__ :str=0.1 , lowerCAmelCase__ :Any=1E-5 , lowerCAmelCase__ :Union[str, Any]=0.0_2 , lowerCAmelCase__ :Union[str, Any]=True , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :int=50_256 , lowerCAmelCase__ :List[str]=50_256 , lowerCAmelCase__ :List[Any]=True , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=True , **lowerCAmelCase__ :Union[str, Any] , ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = n_positions
snake_case_ : Any = n_embd
snake_case_ : Optional[Any] = n_layer
snake_case_ : List[Any] = n_head
snake_case_ : Tuple = n_inner
snake_case_ : str = activation_function
snake_case_ : Union[str, Any] = resid_pdrop
snake_case_ : Optional[Any] = embd_pdrop
snake_case_ : Any = attn_pdrop
snake_case_ : List[Any] = layer_norm_epsilon
snake_case_ : Tuple = initializer_range
snake_case_ : int = scale_attn_weights
snake_case_ : Union[str, Any] = use_cache
snake_case_ : Dict = attention_softmax_in_fpaa
snake_case_ : Any = scale_attention_softmax_in_fpaa
snake_case_ : List[str] = multi_query
snake_case_ : List[str] = bos_token_id
snake_case_ : Any = eos_token_id
super().__init__(bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
| 653 | 0 |
from __future__ import annotations
def A ( _lowercase ):
return len(set(_lowercase ) ) == len(_lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 248 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase : Union[str, Any] = logging.getLogger(__name__)
def __UpperCAmelCase ( __magic_name__ )-> str:
"""simple docstring"""
snake_case_ : Dict = git.Repo(search_parent_directories=__magic_name__ )
snake_case_ : Optional[int] = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
}
with open(os.path.join(__magic_name__ ,"git_log.json" ) ,"w" ) as f:
json.dump(__magic_name__ ,__magic_name__ ,indent=4 )
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
if params.n_gpu <= 0:
snake_case_ : Any = 0
snake_case_ : Any = -1
snake_case_ : Tuple = True
snake_case_ : List[str] = False
return
assert torch.cuda.is_available()
logger.info("Initializing GPUs" )
if params.n_gpu > 1:
assert params.local_rank != -1
snake_case_ : Optional[int] = int(os.environ["WORLD_SIZE"] )
snake_case_ : int = int(os.environ["N_GPU_NODE"] )
snake_case_ : Any = int(os.environ["RANK"] )
# number of nodes / node ID
snake_case_ : Dict = params.world_size // params.n_gpu_per_node
snake_case_ : Optional[int] = params.global_rank // params.n_gpu_per_node
snake_case_ : Tuple = True
assert params.n_nodes == int(os.environ["N_NODES"] )
assert params.node_id == int(os.environ["NODE_RANK"] )
# local job (single GPU)
else:
assert params.local_rank == -1
snake_case_ : Optional[int] = 1
snake_case_ : str = 0
snake_case_ : List[Any] = 0
snake_case_ : int = 0
snake_case_ : Dict = 1
snake_case_ : Optional[Any] = 1
snake_case_ : str = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
snake_case_ : str = params.node_id == 0 and params.local_rank == 0
snake_case_ : str = params.n_nodes > 1
# summary
snake_case_ : str = F'''--- Global rank: {params.global_rank} - '''
logger.info(PREFIX + "Number of nodes: %i" % params.n_nodes )
logger.info(PREFIX + "Node ID : %i" % params.node_id )
logger.info(PREFIX + "Local rank : %i" % params.local_rank )
logger.info(PREFIX + "World size : %i" % params.world_size )
logger.info(PREFIX + "GPUs per node : %i" % params.n_gpu_per_node )
logger.info(PREFIX + "Master : %s" % str(params.is_master ) )
logger.info(PREFIX + "Multi-node : %s" % str(params.multi_node ) )
logger.info(PREFIX + "Multi-GPU : %s" % str(params.multi_gpu ) )
logger.info(PREFIX + "Hostname : %s" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("Initializing PyTorch distributed" )
torch.distributed.init_process_group(
init_method="env://" ,backend="nccl" ,)
def __UpperCAmelCase ( __magic_name__ )-> Dict:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 653 | 0 |
'''simple docstring'''
import socket
def _A ( ):
"""simple docstring"""
__lowercase =socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__lowercase =socket.gethostname()
__lowercase =12_312
sock.connect((host, port) )
sock.send(b'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('File opened' )
print('Receiving data...' )
while True:
__lowercase =sock.recv(1_024 )
if not data:
break
out_file.write(_lowerCAmelCase )
print('Successfully received the file' )
sock.close()
print('Connection closed' )
if __name__ == "__main__":
main()
| 474 |
'''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 A_ (unittest.TestCase ):
"""simple docstring"""
def __init__( self :Any , lowerCAmelCase__ :Dict , lowerCAmelCase__ :Dict=7 , lowerCAmelCase__ :Union[str, Any]=3 , lowerCAmelCase__ :List[str]=30 , lowerCAmelCase__ :List[str]=400 , lowerCAmelCase__ :Optional[Any]=True , lowerCAmelCase__ :Dict=None , lowerCAmelCase__ :str=True , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :Optional[int]=[0.5, 0.5, 0.5] , lowerCAmelCase__ :str=True , lowerCAmelCase__ :int=1 / 255 , lowerCAmelCase__ :int=True , ) -> str:
'''simple docstring'''
snake_case_ : List[Any] = size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
snake_case_ : Dict = parent
snake_case_ : Union[str, Any] = batch_size
snake_case_ : Optional[Any] = num_channels
snake_case_ : str = min_resolution
snake_case_ : Dict = max_resolution
snake_case_ : Optional[Any] = do_resize
snake_case_ : str = size
snake_case_ : Optional[int] = do_normalize
snake_case_ : Dict = image_mean
snake_case_ : Optional[int] = image_std
snake_case_ : List[str] = do_rescale
snake_case_ : Dict = rescale_factor
snake_case_ : str = do_pad
def _A ( self :List[Any] ) -> Dict:
'''simple docstring'''
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 _A ( self :Dict , lowerCAmelCase__ :List[str] , lowerCAmelCase__ :List[str]=False ) -> str:
'''simple docstring'''
if not batched:
snake_case_ : List[str] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
snake_case_, snake_case_ : int = image.size
else:
snake_case_, snake_case_ : Any = image.shape[1], image.shape[2]
if w < h:
snake_case_ : int = int(self.size["shortest_edge"] * h / w )
snake_case_ : List[Any] = self.size["shortest_edge"]
elif w > h:
snake_case_ : Optional[int] = self.size["shortest_edge"]
snake_case_ : str = int(self.size["shortest_edge"] * w / h )
else:
snake_case_ : Tuple = self.size["shortest_edge"]
snake_case_ : Dict = self.size["shortest_edge"]
else:
snake_case_ : List[str] = []
for image in image_inputs:
snake_case_, snake_case_ : Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case_ : str = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
snake_case_ : int = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class A_ (a_ , unittest.TestCase ):
"""simple docstring"""
a__ = YolosImageProcessor if is_vision_available() else None
def _A ( self :Optional[Any] ) -> str:
'''simple docstring'''
snake_case_ : int = YolosImageProcessingTester(self )
@property
def _A ( self :List[str] ) -> Any:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self :List[str] ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : Tuple = 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 _A ( self :List[Any] ) -> Any:
'''simple docstring'''
snake_case_ : List[Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
snake_case_ : Optional[int] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def _A ( self :List[str] ) -> int:
'''simple docstring'''
pass
def _A ( self :Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case_ : List[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
snake_case_ : Optional[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, snake_case_ : int = 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
snake_case_, snake_case_ : Any = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
snake_case_ : 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 _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case_ : Any = 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
snake_case_ : Union[str, Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, 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
snake_case_ : Tuple = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, 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,
) , )
def _A ( self :Tuple ) -> Tuple:
'''simple docstring'''
snake_case_ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case_ : str = 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
snake_case_ : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
snake_case_, 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
snake_case_ : List[Any] = image_processing(lowerCAmelCase__ , return_tensors="pt" ).pixel_values
snake_case_, 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 _A ( self :Tuple ) -> Dict:
'''simple docstring'''
snake_case_ : str = self.image_processing_class(**self.image_processor_dict )
snake_case_ : List[Any] = self.image_processing_class(do_resize=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_rescale=lowerCAmelCase__ )
# create random PyTorch tensors
snake_case_ : Tuple = 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
snake_case_ : Tuple = image_processing_a.pad(lowerCAmelCase__ , return_tensors="pt" )
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 _A ( self :str ) -> Any:
'''simple docstring'''
snake_case_ : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
snake_case_ : int = json.loads(f.read() )
snake_case_ : Optional[int] = {"image_id": 39_769, "annotations": target}
# encode them
snake_case_ : Tuple = YolosImageProcessor.from_pretrained("hustvl/yolos-small" )
snake_case_ : Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
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
snake_case_ : Dict = 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
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
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
snake_case_ : Dict = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : int = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : List[str] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify orig_size
snake_case_ : Any = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
@slow
def _A ( self :Dict ) -> int:
'''simple docstring'''
snake_case_ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
snake_case_ : Optional[int] = json.loads(f.read() )
snake_case_ : Tuple = {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}
snake_case_ : Any = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
snake_case_ : int = YolosImageProcessor(format="coco_panoptic" )
snake_case_ : Union[str, Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors="pt" )
# verify pixel values
snake_case_ : Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["pixel_values"].shape , lowerCAmelCase__ )
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
snake_case_ : int = 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
snake_case_ : Optional[int] = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , lowerCAmelCase__ )
snake_case_ : List[str] = 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
snake_case_ : List[str] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , lowerCAmelCase__ ) )
# verify is_crowd
snake_case_ : Dict = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , lowerCAmelCase__ ) )
# verify class_labels
snake_case_ : str = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , lowerCAmelCase__ ) )
# verify masks
snake_case_ : Any = 822_873
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , lowerCAmelCase__ )
# verify orig_size
snake_case_ : int = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , lowerCAmelCase__ ) )
# verify size
snake_case_ : Union[str, Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , lowerCAmelCase__ ) )
| 653 | 0 |
'''simple docstring'''
import os
import platform
import sys
lowercase_ = '''3'''
print("Python version:", sys.version)
print("OS platform:", platform.platform())
print("OS architecture:", platform.machine())
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
except ImportError:
print("Torch version:", None)
try:
import transformers
print("transformers version:", transformers.__version__)
except ImportError:
print("transformers version:", None)
| 11 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(__magic_name__ ,__magic_name__ ) or not number >= 1:
raise ValueError(
"starting number must be\n and integer and be more than 0" )
if not iterations >= 1:
raise ValueError("Iterations must be done more than 0 times to play FizzBuzz" )
snake_case_ : Dict = ""
while number <= iterations:
if number % 3 == 0:
out += "Fizz"
if number % 5 == 0:
out += "Buzz"
if 0 not in (number % 3, number % 5):
out += str(__magic_name__ )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 653 | 0 |
def __a ( A__ : str , A__ : Dict ):
SCREAMING_SNAKE_CASE = ""
for word_or_phrase in separated:
if not isinstance(A__ , A__ ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separator
return joined.strip(A__ )
if __name__ == "__main__":
from doctest import testmod
testmod() | 16 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase : Tuple = 16
__lowerCamelCase : Optional[int] = 32
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ = 16 )-> int:
"""simple docstring"""
snake_case_ : Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
snake_case_ : str = load_dataset("glue" ,"mrpc" )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
snake_case_ : Dict = tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=__magic_name__ ,max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
snake_case_ : Any = datasets.map(
__magic_name__ ,batched=__magic_name__ ,remove_columns=["idx", "sentence1", "sentence2"] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
snake_case_ : List[Any] = tokenized_datasets.rename_column("label" ,"labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
snake_case_ : int = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
snake_case_ : Tuple = 16
elif accelerator.mixed_precision != "no":
snake_case_ : str = 8
else:
snake_case_ : Optional[Any] = None
return tokenizer.pad(
__magic_name__ ,padding="longest" ,max_length=__magic_name__ ,pad_to_multiple_of=__magic_name__ ,return_tensors="pt" ,)
# Instantiate dataloaders.
snake_case_ : str = DataLoader(
tokenized_datasets["train"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
snake_case_ : Optional[Any] = DataLoader(
tokenized_datasets["validation"] ,shuffle=__magic_name__ ,collate_fn=__magic_name__ ,batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__lowerCamelCase : Optional[Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> Dict:
"""simple docstring"""
if os.environ.get("TESTING_MOCKED_DATALOADERS" ,__magic_name__ ) == "1":
snake_case_ : List[str] = 2
# Initialize accelerator
snake_case_ : Union[str, Any] = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case_ : List[str] = config["lr"]
snake_case_ : Dict = int(config["num_epochs"] )
snake_case_ : Dict = int(config["seed"] )
snake_case_ : Optional[int] = int(config["batch_size"] )
snake_case_ : Dict = evaluate.load("glue" ,"mrpc" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=__magic_name__ )
def inner_training_loop(__magic_name__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(__magic_name__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=__magic_name__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case_ : Optional[int] = model.to(accelerator.device )
# Instantiate optimizer
snake_case_ : List[Any] = AdamW(params=model.parameters() ,lr=__magic_name__ )
snake_case_, snake_case_ : int = get_dataloaders(__magic_name__ ,__magic_name__ )
# Instantiate scheduler
snake_case_ : Tuple = get_linear_schedule_with_warmup(
optimizer=__magic_name__ ,num_warmup_steps=100 ,num_training_steps=(len(__magic_name__ ) * num_epochs) ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case_, snake_case_, snake_case_, snake_case_, snake_case_ : Tuple = accelerator.prepare(
__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )
# Now we train the model
for epoch in range(__magic_name__ ):
model.train()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Any = outputs.loss
accelerator.backward(__magic_name__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(__magic_name__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
snake_case_ : Union[str, Any] = model(**__magic_name__ )
snake_case_ : List[str] = outputs.logits.argmax(dim=-1 )
snake_case_, snake_case_ : Union[str, Any] = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=__magic_name__ ,references=__magic_name__ ,)
snake_case_ : Tuple = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' ,__magic_name__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __UpperCAmelCase ( )-> List[str]:
"""simple docstring"""
snake_case_ : List[Any] = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" ,type=__magic_name__ ,default=__magic_name__ ,choices=["no", "fp16", "bf16", "fp8"] ,help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." ,)
parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU." )
snake_case_ : str = parser.parse_args()
snake_case_ : Optional[int] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(__magic_name__ ,__magic_name__ )
if __name__ == "__main__":
main()
| 653 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : int = CustomTokenizer
pass
| 654 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : Optional[int] = IFInpaintingSuperResolutionPipeline
_lowercase : int = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_lowercase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_lowercase : int = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
return self._get_superresolution_dummy_components()
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int]=0):
'''simple docstring'''
if str(UpperCamelCase__).startswith("""mps"""):
snake_case__ = torch.manual_seed(UpperCamelCase__)
else:
snake_case__ = torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
snake_case__ = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
snake_case__ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
snake_case__ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
snake_case__ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __magic_name__ ( self : Dict):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
def __magic_name__ ( self : int):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""")
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1)
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
self._test_save_load_local()
def __magic_name__ ( self : str):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 654 | 1 |
import heapq
import sys
import numpy as np
a__ = tuple[int, int]
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[Any]):
'''simple docstring'''
snake_case__ = []
snake_case__ = set()
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
if not self.empty():
return self.elements[0][0]
else:
return float("""inf""")
def __magic_name__ ( self : str):
'''simple docstring'''
return len(self.elements) == 0
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : int):
'''simple docstring'''
if item not in self.set:
heapq.heappush(self.elements , (priority, item))
self.set.add(UpperCamelCase__)
else:
# update
# print("update", item)
snake_case__ = []
((snake_case__) , (snake_case__)) = heapq.heappop(self.elements)
while x != item:
temp.append((pri, x))
((snake_case__) , (snake_case__)) = heapq.heappop(self.elements)
temp.append((priority, item))
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx))
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Dict):
'''simple docstring'''
if item in self.set:
self.set.remove(UpperCamelCase__)
snake_case__ = []
((snake_case__) , (snake_case__)) = heapq.heappop(self.elements)
while x != item:
temp.append((pro, x))
((snake_case__) , (snake_case__)) = heapq.heappop(self.elements)
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
return self.elements[0][1]
def __magic_name__ ( self : int):
'''simple docstring'''
((snake_case__) , (snake_case__)) = heapq.heappop(self.elements)
self.set.remove(UpperCamelCase__)
return (priority, item)
def _UpperCAmelCase ( a : TPos , a : TPos ):
# euclidean distance
snake_case__ = np.array(a )
snake_case__ = np.array(a )
return np.linalg.norm(a - b )
def _UpperCAmelCase ( a : TPos , a : TPos ):
# integer division by time variable
return consistent_heuristic(a , a ) // t
def _UpperCAmelCase ( a : TPos , a : TPos ):
# manhattan distance
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def _UpperCAmelCase ( a : TPos , a : int , a : TPos , a : dict[TPos, float] ):
snake_case__ = g_function[start] + Wa * heuristics[i](a , a )
return ans
def _UpperCAmelCase ( a : Optional[Any] , a : str , a : Tuple ):
snake_case__ = np.chararray((n, n) )
for i in range(a ):
for j in range(a ):
snake_case__ = """*"""
for i in range(a ):
for j in range(a ):
if (j, (n - 1) - i) in blocks:
snake_case__ = """#"""
snake_case__ = """-"""
snake_case__ = back_pointer[goal]
while x != start:
((snake_case__) , (snake_case__)) = x
# print(x)
snake_case__ = """-"""
snake_case__ = back_pointer[x]
snake_case__ = """-"""
for i in range(a ):
for j in range(a ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=""" """ )
print("""<-- End position""" , end=""" """ )
else:
print(grid[i][j] , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
print("""PATH TAKEN BY THE ALGORITHM IS:-""" )
snake_case__ = back_pointer[goal]
while x != start:
print(a , end=""" """ )
snake_case__ = back_pointer[x]
print(a )
sys.exit()
def _UpperCAmelCase ( a : TPos ):
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def _UpperCAmelCase ( a : List[Any] , a : Tuple , a : Optional[Any] , a : Tuple , a : Any , a : Dict , a : Tuple , a : Optional[Any] , ):
for itera in range(a ):
open_list[itera].remove_element(a )
# print("s", s)
# print("j", j)
((snake_case__) , (snake_case__)) = s
snake_case__ = (x - 1, y)
snake_case__ = (x + 1, y)
snake_case__ = (x, y + 1)
snake_case__ = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(a ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(a )
snake_case__ = -1
snake_case__ = float("""inf""" )
if valid(a ) and g_function[neighbours] > g_function[s] + 1:
snake_case__ = g_function[s] + 1
snake_case__ = s
if neighbours not in close_list_anchor:
open_list[0].put(a , key(a , 0 , a , a ) )
if neighbours not in close_list_inad:
for var in range(1 , a ):
if key(a , a , a , a ) <= Wa * key(
a , 0 , a , a ):
open_list[j].put(
a , key(a , a , a , a ) )
def _UpperCAmelCase ( ):
snake_case__ = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
a__ = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
a__ = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(1_0, 1),
(1_1, 1),
(1_2, 1),
(1_3, 1),
(1_4, 1),
(1_5, 1),
(1_6, 1),
(1_7, 1),
(1_8, 1),
(1_9, 1),
]
a__ = make_common_ground()
a__ = blocks_blk
# hyper parameters
a__ = 1
a__ = 1
a__ = 2_0
a__ = 3 # one consistent and two other inconsistent
# start and end destination
a__ = (0, 0)
a__ = (n - 1, n - 1)
a__ = 1
def _UpperCAmelCase ( a : TPos , a : TPos , a : int ):
snake_case__ = {start: 0, goal: float("""inf""" )}
snake_case__ = {start: -1, goal: -1}
snake_case__ = []
snake_case__ = set()
for i in range(a ):
open_list.append(PriorityQueue() )
open_list[i].put(a , key(a , a , a , a ) )
snake_case__ = []
snake_case__ = []
while open_list[0].minkey() < float("""inf""" ):
for i in range(1 , a ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("""inf""" ):
do_something(a , a , a )
else:
snake_case__ , snake_case__ = open_list[i].top_show()
visited.add(a )
expand_state(
a , a , a , a , a , a , a , a , )
close_list_inad.append(a )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("""inf""" ):
do_something(a , a , a )
else:
snake_case__ = open_list[0].top_show()
visited.add(a )
expand_state(
a , 0 , a , a , a , a , a , a , )
close_list_anchor.append(a )
print("""No path found to goal""" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(a ):
if (j, i) in blocks:
print("""#""" , end=""" """ )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("""*""" , end=""" """ )
else:
print("""-""" , end=""" """ )
else:
print("""*""" , end=""" """ )
if (j, i) == (n - 1, n - 1):
print("""<-- End position""" , end=""" """ )
print()
print("""^""" )
print("""Start position""" )
print()
print("""# is an obstacle""" )
print("""- is the path taken by algorithm""" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 654 |
a__ = [0, 2, 4, 6, 8]
a__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( a : int , a : int , a : list[int] , a : int ):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
snake_case__ = 0
for digit in range(10 ):
snake_case__ = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , a , a )
return result
snake_case__ = 0
for digita in range(10 ):
snake_case__ = digita
if (remainder + digita) % 2 == 0:
snake_case__ = ODD_DIGITS
else:
snake_case__ = EVEN_DIGITS
for digita in other_parity_digits:
snake_case__ = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , a , a , )
return result
def _UpperCAmelCase ( a : int = 9 ):
snake_case__ = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(a , 0 , [0] * length , a )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 654 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
a__ = ["""gpt2"""]
a__ = """gpt2"""
if is_tf_available():
class _lowerCAmelCase ( tf.Module ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : int):
'''simple docstring'''
super().__init__()
snake_case__ = tokenizer
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase__)
snake_case__ = TFGPTaLMHeadModel.from_config(UpperCamelCase__)
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text"""),))
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = self.tokenizer(UpperCamelCase__)
snake_case__ = tokenized["""input_ids"""].to_tensor()
snake_case__ = tf.cast(input_ids_dense > 0 , tf.intaa)
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
snake_case__ = self.model(input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__)["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
super().setUp()
snake_case__ = [GPTaTokenizer.from_pretrained(UpperCamelCase__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
snake_case__ = [TFGPTaTokenizer.from_pretrained(UpperCamelCase__) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
snake_case__ = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
snake_case__ = list(zip(self.test_sentences , self.test_sentences[::-1]))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers):
for test_inputs in self.test_sentences:
snake_case__ = tokenizer([test_inputs] , return_tensors="""tf""")
snake_case__ = tf_tokenizer([test_inputs])
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
snake_case__ = python_outputs[key].numpy()
snake_case__ = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__ , tf.intaa) == tf_outputs_values))
@slow
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = tf.function(UpperCamelCase__)
for test_inputs in self.test_sentences:
snake_case__ = tf.constant(UpperCamelCase__)
snake_case__ = compiled_tokenizer(UpperCamelCase__)
snake_case__ = tf_tokenizer(UpperCamelCase__)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = ModelToSave(tokenizer=UpperCamelCase__)
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = model.serving(UpperCamelCase__) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
snake_case__ = Path(UpperCamelCase__) / """saved.model"""
tf.saved_model.save(UpperCamelCase__ , UpperCamelCase__ , signatures={"""serving_default""": model.serving})
snake_case__ = tf.saved_model.load(UpperCamelCase__)
snake_case__ = loaded_model.signatures["""serving_default"""](UpperCamelCase__)["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output))
@slow
def __magic_name__ ( self : Tuple):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = tf_tokenizer(UpperCamelCase__) # Build model with some sample inputs
snake_case__ = tf_tokenizer.get_config()
snake_case__ = TFGPTaTokenizer.from_config(UpperCamelCase__)
snake_case__ = model_from_config(UpperCamelCase__)
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))
@slow
def __magic_name__ ( self : Dict):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
snake_case__ = 1_2_3_1_2_3
for max_length in [3, 5, 1_0_2_4]:
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = tf_tokenizer(UpperCamelCase__ , max_length=UpperCamelCase__)
snake_case__ = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 654 |
# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
a__ = {
"""Acehnese Arabic""": """ace_Arab""",
"""Acehnese Latin""": """ace_Latn""",
"""Mesopotamian Arabic""": """acm_Arab""",
"""Ta'izzi-Adeni Arabic""": """acq_Arab""",
"""Tunisian Arabic""": """aeb_Arab""",
"""Afrikaans""": """afr_Latn""",
"""South Levantine Arabic""": """ajp_Arab""",
"""Akan""": """aka_Latn""",
"""Amharic""": """amh_Ethi""",
"""North Levantine Arabic""": """apc_Arab""",
"""Modern Standard Arabic""": """arb_Arab""",
"""Modern Standard Arabic Romanized""": """arb_Latn""",
"""Najdi Arabic""": """ars_Arab""",
"""Moroccan Arabic""": """ary_Arab""",
"""Egyptian Arabic""": """arz_Arab""",
"""Assamese""": """asm_Beng""",
"""Asturian""": """ast_Latn""",
"""Awadhi""": """awa_Deva""",
"""Central Aymara""": """ayr_Latn""",
"""South Azerbaijani""": """azb_Arab""",
"""North Azerbaijani""": """azj_Latn""",
"""Bashkir""": """bak_Cyrl""",
"""Bambara""": """bam_Latn""",
"""Balinese""": """ban_Latn""",
"""Belarusian""": """bel_Cyrl""",
"""Bemba""": """bem_Latn""",
"""Bengali""": """ben_Beng""",
"""Bhojpuri""": """bho_Deva""",
"""Banjar Arabic""": """bjn_Arab""",
"""Banjar Latin""": """bjn_Latn""",
"""Standard Tibetan""": """bod_Tibt""",
"""Bosnian""": """bos_Latn""",
"""Buginese""": """bug_Latn""",
"""Bulgarian""": """bul_Cyrl""",
"""Catalan""": """cat_Latn""",
"""Cebuano""": """ceb_Latn""",
"""Czech""": """ces_Latn""",
"""Chokwe""": """cjk_Latn""",
"""Central Kurdish""": """ckb_Arab""",
"""Crimean Tatar""": """crh_Latn""",
"""Welsh""": """cym_Latn""",
"""Danish""": """dan_Latn""",
"""German""": """deu_Latn""",
"""Southwestern Dinka""": """dik_Latn""",
"""Dyula""": """dyu_Latn""",
"""Dzongkha""": """dzo_Tibt""",
"""Greek""": """ell_Grek""",
"""English""": """eng_Latn""",
"""Esperanto""": """epo_Latn""",
"""Estonian""": """est_Latn""",
"""Basque""": """eus_Latn""",
"""Ewe""": """ewe_Latn""",
"""Faroese""": """fao_Latn""",
"""Fijian""": """fij_Latn""",
"""Finnish""": """fin_Latn""",
"""Fon""": """fon_Latn""",
"""French""": """fra_Latn""",
"""Friulian""": """fur_Latn""",
"""Nigerian Fulfulde""": """fuv_Latn""",
"""Scottish Gaelic""": """gla_Latn""",
"""Irish""": """gle_Latn""",
"""Galician""": """glg_Latn""",
"""Guarani""": """grn_Latn""",
"""Gujarati""": """guj_Gujr""",
"""Haitian Creole""": """hat_Latn""",
"""Hausa""": """hau_Latn""",
"""Hebrew""": """heb_Hebr""",
"""Hindi""": """hin_Deva""",
"""Chhattisgarhi""": """hne_Deva""",
"""Croatian""": """hrv_Latn""",
"""Hungarian""": """hun_Latn""",
"""Armenian""": """hye_Armn""",
"""Igbo""": """ibo_Latn""",
"""Ilocano""": """ilo_Latn""",
"""Indonesian""": """ind_Latn""",
"""Icelandic""": """isl_Latn""",
"""Italian""": """ita_Latn""",
"""Javanese""": """jav_Latn""",
"""Japanese""": """jpn_Jpan""",
"""Kabyle""": """kab_Latn""",
"""Jingpho""": """kac_Latn""",
"""Kamba""": """kam_Latn""",
"""Kannada""": """kan_Knda""",
"""Kashmiri Arabic""": """kas_Arab""",
"""Kashmiri Devanagari""": """kas_Deva""",
"""Georgian""": """kat_Geor""",
"""Central Kanuri Arabic""": """knc_Arab""",
"""Central Kanuri Latin""": """knc_Latn""",
"""Kazakh""": """kaz_Cyrl""",
"""Kabiyè""": """kbp_Latn""",
"""Kabuverdianu""": """kea_Latn""",
"""Khmer""": """khm_Khmr""",
"""Kikuyu""": """kik_Latn""",
"""Kinyarwanda""": """kin_Latn""",
"""Kyrgyz""": """kir_Cyrl""",
"""Kimbundu""": """kmb_Latn""",
"""Northern Kurdish""": """kmr_Latn""",
"""Kikongo""": """kon_Latn""",
"""Korean""": """kor_Hang""",
"""Lao""": """lao_Laoo""",
"""Ligurian""": """lij_Latn""",
"""Limburgish""": """lim_Latn""",
"""Lingala""": """lin_Latn""",
"""Lithuanian""": """lit_Latn""",
"""Lombard""": """lmo_Latn""",
"""Latgalian""": """ltg_Latn""",
"""Luxembourgish""": """ltz_Latn""",
"""Luba-Kasai""": """lua_Latn""",
"""Ganda""": """lug_Latn""",
"""Luo""": """luo_Latn""",
"""Mizo""": """lus_Latn""",
"""Standard Latvian""": """lvs_Latn""",
"""Magahi""": """mag_Deva""",
"""Maithili""": """mai_Deva""",
"""Malayalam""": """mal_Mlym""",
"""Marathi""": """mar_Deva""",
"""Minangkabau Arabic """: """min_Arab""",
"""Minangkabau Latin""": """min_Latn""",
"""Macedonian""": """mkd_Cyrl""",
"""Plateau Malagasy""": """plt_Latn""",
"""Maltese""": """mlt_Latn""",
"""Meitei Bengali""": """mni_Beng""",
"""Halh Mongolian""": """khk_Cyrl""",
"""Mossi""": """mos_Latn""",
"""Maori""": """mri_Latn""",
"""Burmese""": """mya_Mymr""",
"""Dutch""": """nld_Latn""",
"""Norwegian Nynorsk""": """nno_Latn""",
"""Norwegian Bokmål""": """nob_Latn""",
"""Nepali""": """npi_Deva""",
"""Northern Sotho""": """nso_Latn""",
"""Nuer""": """nus_Latn""",
"""Nyanja""": """nya_Latn""",
"""Occitan""": """oci_Latn""",
"""West Central Oromo""": """gaz_Latn""",
"""Odia""": """ory_Orya""",
"""Pangasinan""": """pag_Latn""",
"""Eastern Panjabi""": """pan_Guru""",
"""Papiamento""": """pap_Latn""",
"""Western Persian""": """pes_Arab""",
"""Polish""": """pol_Latn""",
"""Portuguese""": """por_Latn""",
"""Dari""": """prs_Arab""",
"""Southern Pashto""": """pbt_Arab""",
"""Ayacucho Quechua""": """quy_Latn""",
"""Romanian""": """ron_Latn""",
"""Rundi""": """run_Latn""",
"""Russian""": """rus_Cyrl""",
"""Sango""": """sag_Latn""",
"""Sanskrit""": """san_Deva""",
"""Santali""": """sat_Olck""",
"""Sicilian""": """scn_Latn""",
"""Shan""": """shn_Mymr""",
"""Sinhala""": """sin_Sinh""",
"""Slovak""": """slk_Latn""",
"""Slovenian""": """slv_Latn""",
"""Samoan""": """smo_Latn""",
"""Shona""": """sna_Latn""",
"""Sindhi""": """snd_Arab""",
"""Somali""": """som_Latn""",
"""Southern Sotho""": """sot_Latn""",
"""Spanish""": """spa_Latn""",
"""Tosk Albanian""": """als_Latn""",
"""Sardinian""": """srd_Latn""",
"""Serbian""": """srp_Cyrl""",
"""Swati""": """ssw_Latn""",
"""Sundanese""": """sun_Latn""",
"""Swedish""": """swe_Latn""",
"""Swahili""": """swh_Latn""",
"""Silesian""": """szl_Latn""",
"""Tamil""": """tam_Taml""",
"""Tatar""": """tat_Cyrl""",
"""Telugu""": """tel_Telu""",
"""Tajik""": """tgk_Cyrl""",
"""Tagalog""": """tgl_Latn""",
"""Thai""": """tha_Thai""",
"""Tigrinya""": """tir_Ethi""",
"""Tamasheq Latin""": """taq_Latn""",
"""Tamasheq Tifinagh""": """taq_Tfng""",
"""Tok Pisin""": """tpi_Latn""",
"""Tswana""": """tsn_Latn""",
"""Tsonga""": """tso_Latn""",
"""Turkmen""": """tuk_Latn""",
"""Tumbuka""": """tum_Latn""",
"""Turkish""": """tur_Latn""",
"""Twi""": """twi_Latn""",
"""Central Atlas Tamazight""": """tzm_Tfng""",
"""Uyghur""": """uig_Arab""",
"""Ukrainian""": """ukr_Cyrl""",
"""Umbundu""": """umb_Latn""",
"""Urdu""": """urd_Arab""",
"""Northern Uzbek""": """uzn_Latn""",
"""Venetian""": """vec_Latn""",
"""Vietnamese""": """vie_Latn""",
"""Waray""": """war_Latn""",
"""Wolof""": """wol_Latn""",
"""Xhosa""": """xho_Latn""",
"""Eastern Yiddish""": """ydd_Hebr""",
"""Yoruba""": """yor_Latn""",
"""Yue Chinese""": """yue_Hant""",
"""Chinese Simplified""": """zho_Hans""",
"""Chinese Traditional""": """zho_Hant""",
"""Standard Malay""": """zsm_Latn""",
"""Zulu""": """zul_Latn""",
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[str] = '''facebook/nllb-200-distilled-600M'''
_lowercase : List[Any] = (
'''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
'''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
'''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
'''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
)
_lowercase : Optional[int] = '''translator'''
_lowercase : Optional[Any] = AutoTokenizer
_lowercase : Dict = AutoModelForSeqaSeqLM
_lowercase : List[str] = LANGUAGE_CODES
_lowercase : Optional[Any] = ['''text''', '''text''', '''text''']
_lowercase : Tuple = ['''text''']
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(F'''{src_lang} is not a supported language.''')
if tgt_lang not in self.lang_to_code:
raise ValueError(F'''{tgt_lang} is not a supported language.''')
snake_case__ = self.lang_to_code[src_lang]
snake_case__ = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
UpperCamelCase__ , return_tensors="""pt""" , src_lang=UpperCamelCase__ , tgt_lang=UpperCamelCase__)
def __magic_name__ ( self : Dict , UpperCamelCase__ : Dict):
'''simple docstring'''
return self.model.generate(**UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : Dict):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=UpperCamelCase__)
| 654 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
a__ = logging.get_logger(__name__)
@add_end_docstrings(lowercase_ )
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : List[Any] , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : Optional[int]):
'''simple docstring'''
super().__init__(*UpperCamelCase__ , **UpperCamelCase__)
requires_backends(self , """decord""")
self.check_model_type(UpperCamelCase__)
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Any=None , UpperCamelCase__ : str=None , UpperCamelCase__ : Union[str, Any]=None):
'''simple docstring'''
snake_case__ = {}
if frame_sampling_rate is not None:
snake_case__ = frame_sampling_rate
if num_frames is not None:
snake_case__ = num_frames
snake_case__ = {}
if top_k is not None:
snake_case__ = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : Optional[int] , UpperCamelCase__ : Union[str, List[str]] , **UpperCamelCase__ : List[str]):
'''simple docstring'''
return super().__call__(UpperCamelCase__ , **UpperCamelCase__)
def __magic_name__ ( self : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : str=None , UpperCamelCase__ : List[str]=1):
'''simple docstring'''
if num_frames is None:
snake_case__ = self.model.config.num_frames
if video.startswith("""http://""") or video.startswith("""https://"""):
snake_case__ = BytesIO(requests.get(UpperCamelCase__).content)
snake_case__ = VideoReader(UpperCamelCase__)
videoreader.seek(0)
snake_case__ = 0
snake_case__ = num_frames * frame_sampling_rate - 1
snake_case__ = np.linspace(UpperCamelCase__ , UpperCamelCase__ , num=UpperCamelCase__ , dtype=np.intaa)
snake_case__ = videoreader.get_batch(UpperCamelCase__).asnumpy()
snake_case__ = list(UpperCamelCase__)
snake_case__ = self.image_processor(UpperCamelCase__ , return_tensors=self.framework)
return model_inputs
def __magic_name__ ( self : Optional[Any] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = self.model(**UpperCamelCase__)
return model_outputs
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any]=5):
'''simple docstring'''
if top_k > self.model.config.num_labels:
snake_case__ = self.model.config.num_labels
if self.framework == "pt":
snake_case__ = model_outputs.logits.softmax(-1)[0]
snake_case__ , snake_case__ = probs.topk(UpperCamelCase__)
else:
raise ValueError(F'''Unsupported framework: {self.framework}''')
snake_case__ = scores.tolist()
snake_case__ = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__ , UpperCamelCase__)]
| 654 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def _UpperCAmelCase ( a : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : nn.Module , UpperCamelCase__ : int):
'''simple docstring'''
super().__init__()
snake_case__ = module
snake_case__ = nn.Sequential(
nn.Linear(module.in_features , UpperCamelCase__ , bias=UpperCamelCase__) , nn.Linear(UpperCamelCase__ , module.out_features , bias=UpperCamelCase__) , )
snake_case__ = (2.0 / (5 * min(module.in_features , module.out_features))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=UpperCamelCase__)
nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str):
'''simple docstring'''
return self.module(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__) + self.adapter(UpperCamelCase__)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowercase : Dict = '''bigscience/bloom-1b7'''
# Constant values
_lowercase : Any = 2.109_6595_5269_2574
_lowercase : Tuple = '''Hello my name is'''
_lowercase : List[Any] = set()
EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I''' )
EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n''' )
EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University''' )
_lowercase : List[str] = 10
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = AutoTokenizer.from_pretrained(self.model_name)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : str):
'''simple docstring'''
super().setUp()
# Models and tokenizer
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="""auto""")
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
def __magic_name__ ( self : Tuple):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = self.model_abit.config
self.assertTrue(hasattr(UpperCamelCase__ , """quantization_config"""))
snake_case__ = config.to_dict()
snake_case__ = config.to_diff_dict()
snake_case__ = config.to_json_string()
def __magic_name__ ( self : Dict):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
snake_case__ = self.model_fpaa.get_memory_footprint()
snake_case__ = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE)
snake_case__ = get_some_linear_layer(self.model_abit)
self.assertTrue(linear.weight.__class__ == Paramsabit)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(UpperCamelCase__ , torch.nn.Linear):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta)
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = BitsAndBytesConfig()
snake_case__ = True
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = model_abit_from_config.generate(
input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(UpperCamelCase__)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = BitsAndBytesConfig()
with self.assertRaises(UpperCamelCase__):
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , load_in_abit=UpperCamelCase__ , device_map="""auto""" , bnb_abit_quant_type="""nf4""" , )
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__):
# Tries with `str`
self.model_abit.to("""cpu""")
with self.assertRaises(UpperCamelCase__):
# Tries with a `dtype``
self.model_abit.to(torch.floataa)
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.to(torch.device("""cuda:0"""))
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = self.model_fpaa.to(torch.floataa)
snake_case__ = self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
# Check this does not throw an error
snake_case__ = self.model_fpaa.to("""cpu""")
# Check this does not throw an error
snake_case__ = self.model_fpaa.half()
# Check this does not throw an error
snake_case__ = self.model_fpaa.float()
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" , load_in_abit=UpperCamelCase__ , device_map="""auto""")
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __magic_name__ ( cls : Optional[Any]):
'''simple docstring'''
snake_case__ = """t5-small"""
snake_case__ = """google/flan-t5-small""" # flan-t5 uses dense-act instead of dense-relu-dense
snake_case__ = AutoTokenizer.from_pretrained(cls.model_name)
snake_case__ = """Translate in German: Hello, my dog is cute"""
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Any):
'''simple docstring'''
from transformers import TaForConditionalGeneration
snake_case__ = TaForConditionalGeneration._keep_in_fpaa_modules
snake_case__ = None
# test with `t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
# test with `flan-t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
snake_case__ = modules
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit))
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
# test with `flan-t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : int):
'''simple docstring'''
super().setUp()
# model_name
snake_case__ = """bigscience/bloom-560m"""
snake_case__ = """t5-small"""
# Different types of model
snake_case__ = AutoModel.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# Sequence classification model
snake_case__ = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# CausalLM model
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# Seq2seq model
snake_case__ = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
def __magic_name__ ( self : List[str]):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Tuple):
'''simple docstring'''
super().setUp()
def __magic_name__ ( self : int):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Tuple):
'''simple docstring'''
snake_case__ = pipeline(
"""text-generation""" , model=self.model_name , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
snake_case__ = self.pipe(self.input_text)
self.assertIn(pipeline_output[0]["""generated_text"""] , self.EXPECTED_OUTPUTS)
@require_torch_multi_gpu
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
super().setUp()
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="""balanced""")
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values()) , {0, 1})
# Check that inference pass works on the model
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
# Second real batch
snake_case__ = model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = """facebook/opt-350m"""
super().setUp()
def __magic_name__ ( self : Any):
'''simple docstring'''
if version.parse(importlib.metadata.version("""bitsandbytes""")) < version.parse("""0.37.0"""):
return
# Step 1: freeze all parameters
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__)
self.assertEqual(set(model.hf_device_map.values()) , {torch.cuda.current_device()})
for param in model.parameters():
snake_case__ = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
snake_case__ = param.data.to(torch.floataa)
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(UpperCamelCase__)):
snake_case__ = LoRALayer(module.q_proj , rank=1_6)
snake_case__ = LoRALayer(module.k_proj , rank=1_6)
snake_case__ = LoRALayer(module.v_proj , rank=1_6)
# Step 3: dummy batch
snake_case__ = self.tokenizer("""Test batch """ , return_tensors="""pt""").to(0)
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
snake_case__ = model.forward(**UpperCamelCase__)
out.logits.norm().backward()
for module in model.modules():
if isinstance(UpperCamelCase__ , UpperCamelCase__):
self.assertTrue(module.adapter[1].weight.grad is not None)
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
elif isinstance(UpperCamelCase__ , nn.Embedding):
self.assertTrue(module.weight.grad is None)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[Any] = '''gpt2-xl'''
_lowercase : Any = 3.3191_8548_5415_2187
| 654 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
),
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[str] = '''audio-spectrogram-transformer'''
def __init__( self : Union[str, Any] , UpperCamelCase__ : int=7_6_8 , UpperCamelCase__ : List[str]=1_2 , UpperCamelCase__ : str=1_2 , UpperCamelCase__ : str=3_0_7_2 , UpperCamelCase__ : Tuple="gelu" , UpperCamelCase__ : int=0.0 , UpperCamelCase__ : List[str]=0.0 , UpperCamelCase__ : Dict=0.02 , UpperCamelCase__ : str=1E-12 , UpperCamelCase__ : Tuple=1_6 , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : List[str]=1_0 , UpperCamelCase__ : List[str]=1_0 , UpperCamelCase__ : Any=1_0_2_4 , UpperCamelCase__ : Optional[int]=1_2_8 , **UpperCamelCase__ : List[Any] , ):
'''simple docstring'''
super().__init__(**UpperCamelCase__)
snake_case__ = hidden_size
snake_case__ = num_hidden_layers
snake_case__ = num_attention_heads
snake_case__ = intermediate_size
snake_case__ = hidden_act
snake_case__ = hidden_dropout_prob
snake_case__ = attention_probs_dropout_prob
snake_case__ = initializer_range
snake_case__ = layer_norm_eps
snake_case__ = patch_size
snake_case__ = qkv_bias
snake_case__ = frequency_stride
snake_case__ = time_stride
snake_case__ = max_length
snake_case__ = num_mel_bins
| 654 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a__ = """"""
a__ = """"""
a__ = """"""
a__ = 1 # (0 is vertical, 1 is horizontal)
def _UpperCAmelCase ( ):
snake_case__ , snake_case__ = get_dataset(a , a )
print("""Processing...""" )
snake_case__ , snake_case__ , snake_case__ = update_image_and_anno(a , a , a )
for index, image in enumerate(a ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case__ = random_chars(32 )
snake_case__ = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
snake_case__ = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , a , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(a )} with {file_name}''' )
snake_case__ = []
for anno in new_annos[index]:
snake_case__ = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(a )
with open(F'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def _UpperCAmelCase ( a : str , a : str ):
snake_case__ = []
snake_case__ = []
for label_file in glob.glob(os.path.join(a , """*.txt""" ) ):
snake_case__ = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(a ) as in_file:
snake_case__ = in_file.readlines()
snake_case__ = os.path.join(a , F'''{label_name}.jpg''' )
snake_case__ = []
for obj_list in obj_lists:
snake_case__ = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(a )
labels.append(a )
return img_paths, labels
def _UpperCAmelCase ( a : list , a : list , a : int = 1 ):
snake_case__ = []
snake_case__ = []
snake_case__ = []
for idx in range(len(a ) ):
snake_case__ = []
snake_case__ = img_list[idx]
path_list.append(a )
snake_case__ = anno_list[idx]
snake_case__ = cva.imread(a )
if flip_type == 1:
snake_case__ = cva.flip(a , a )
for bbox in img_annos:
snake_case__ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
snake_case__ = cva.flip(a , a )
for bbox in img_annos:
snake_case__ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(a )
new_imgs_list.append(a )
return new_imgs_list, new_annos_lists, path_list
def _UpperCAmelCase ( a : int = 32 ):
assert number_char > 1, "The number of character should greater than 1"
snake_case__ = ascii_lowercase + digits
return "".join(random.choice(a ) for _ in range(a ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 654 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
a__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : Optional[int] , *UpperCamelCase__ : Tuple , **UpperCamelCase__ : List[str]):
'''simple docstring'''
warnings.warn(
"""The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DPTImageProcessor instead.""" , UpperCamelCase__ , )
super().__init__(*UpperCamelCase__ , **UpperCamelCase__)
| 654 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a__ = 5_0_0_0_0_0
a__ , a__ = os.path.split(__file__)
a__ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def _UpperCAmelCase ( a : datasets.Dataset , **a : Tuple ):
snake_case__ = dataset.map(**a )
@get_duration
def _UpperCAmelCase ( a : datasets.Dataset , **a : Optional[Any] ):
snake_case__ = dataset.filter(**a )
def _UpperCAmelCase ( ):
snake_case__ = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
snake_case__ = generate_example_dataset(
os.path.join(a , """dataset.arrow""" ) , a , num_examples=a )
snake_case__ = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=a )
def tokenize(a : Union[str, Any] ):
return tokenizer(examples["""text"""] )
snake_case__ = map(a )
snake_case__ = map(a , batched=a )
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""numpy""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""pandas""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
snake_case__ = map(a , function=a , batched=a )
snake_case__ = filter(a )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(a , """wb""" ) as f:
f.write(json.dumps(a ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 654 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
a__ = logging.get_logger(__name__)
# General docstring
a__ = """ResNetConfig"""
# Base docstring
a__ = """microsoft/resnet-50"""
a__ = [1, 2_0_4_8, 7, 7]
# Image classification docstring
a__ = """microsoft/resnet-50"""
a__ = """tiger cat"""
a__ = [
"""microsoft/resnet-50""",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int = 3 , UpperCamelCase__ : int = 1 , UpperCamelCase__ : str = "relu"):
'''simple docstring'''
super().__init__()
snake_case__ = nn.Convad(
UpperCamelCase__ , UpperCamelCase__ , kernel_size=UpperCamelCase__ , stride=UpperCamelCase__ , padding=kernel_size // 2 , bias=UpperCamelCase__)
snake_case__ = nn.BatchNormad(UpperCamelCase__)
snake_case__ = ACTaFN[activation] if activation is not None else nn.Identity()
def __magic_name__ ( self : Tuple , UpperCamelCase__ : Tensor):
'''simple docstring'''
snake_case__ = self.convolution(UpperCamelCase__)
snake_case__ = self.normalization(UpperCamelCase__)
snake_case__ = self.activation(UpperCamelCase__)
return hidden_state
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : ResNetConfig):
'''simple docstring'''
super().__init__()
snake_case__ = ResNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=7 , stride=2 , activation=config.hidden_act)
snake_case__ = nn.MaxPoolad(kernel_size=3 , stride=2 , padding=1)
snake_case__ = config.num_channels
def __magic_name__ ( self : int , UpperCamelCase__ : Tensor):
'''simple docstring'''
snake_case__ = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"""Make sure that the channel dimension of the pixel values match with the one set in the configuration.""")
snake_case__ = self.embedder(UpperCamelCase__)
snake_case__ = self.pooler(UpperCamelCase__)
return embedding
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int = 2):
'''simple docstring'''
super().__init__()
snake_case__ = nn.Convad(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , stride=UpperCamelCase__ , bias=UpperCamelCase__)
snake_case__ = nn.BatchNormad(UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : Tensor):
'''simple docstring'''
snake_case__ = self.convolution(UpperCamelCase__)
snake_case__ = self.normalization(UpperCamelCase__)
return hidden_state
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int = 1 , UpperCamelCase__ : str = "relu"):
'''simple docstring'''
super().__init__()
snake_case__ = in_channels != out_channels or stride != 1
snake_case__ = (
ResNetShortCut(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__) if should_apply_shortcut else nn.Identity()
)
snake_case__ = nn.Sequential(
ResNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__) , ResNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , activation=UpperCamelCase__) , )
snake_case__ = ACTaFN[activation]
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = hidden_state
snake_case__ = self.layer(UpperCamelCase__)
snake_case__ = self.shortcut(UpperCamelCase__)
hidden_state += residual
snake_case__ = self.activation(UpperCamelCase__)
return hidden_state
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int = 1 , UpperCamelCase__ : str = "relu" , UpperCamelCase__ : int = 4):
'''simple docstring'''
super().__init__()
snake_case__ = in_channels != out_channels or stride != 1
snake_case__ = out_channels // reduction
snake_case__ = (
ResNetShortCut(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__) if should_apply_shortcut else nn.Identity()
)
snake_case__ = nn.Sequential(
ResNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1) , ResNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__) , ResNetConvLayer(UpperCamelCase__ , UpperCamelCase__ , kernel_size=1 , activation=UpperCamelCase__) , )
snake_case__ = ACTaFN[activation]
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = hidden_state
snake_case__ = self.layer(UpperCamelCase__)
snake_case__ = self.shortcut(UpperCamelCase__)
hidden_state += residual
snake_case__ = self.activation(UpperCamelCase__)
return hidden_state
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : ResNetConfig , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int = 2 , UpperCamelCase__ : int = 2 , ):
'''simple docstring'''
super().__init__()
snake_case__ = ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer
snake_case__ = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(UpperCamelCase__ , UpperCamelCase__ , stride=UpperCamelCase__ , activation=config.hidden_act) , *[layer(UpperCamelCase__ , UpperCamelCase__ , activation=config.hidden_act) for _ in range(depth - 1)] , )
def __magic_name__ ( self : Dict , UpperCamelCase__ : Tensor):
'''simple docstring'''
snake_case__ = input
for layer in self.layers:
snake_case__ = layer(UpperCamelCase__)
return hidden_state
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , UpperCamelCase__ : ResNetConfig):
'''simple docstring'''
super().__init__()
snake_case__ = nn.ModuleList([])
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
UpperCamelCase__ , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ))
snake_case__ = zip(config.hidden_sizes , config.hidden_sizes[1:])
for (in_channels, out_channels), depth in zip(UpperCamelCase__ , config.depths[1:]):
self.stages.append(ResNetStage(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , depth=UpperCamelCase__))
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Tensor , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = True):
'''simple docstring'''
snake_case__ = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
snake_case__ = hidden_states + (hidden_state,)
snake_case__ = stage_module(UpperCamelCase__)
if output_hidden_states:
snake_case__ = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=UpperCamelCase__ , hidden_states=UpperCamelCase__ , )
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : str = ResNetConfig
_lowercase : Any = '''resnet'''
_lowercase : List[str] = '''pixel_values'''
_lowercase : Union[str, Any] = True
def __magic_name__ ( self : List[str] , UpperCamelCase__ : Dict):
'''simple docstring'''
if isinstance(UpperCamelCase__ , nn.Convad):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""")
elif isinstance(UpperCamelCase__ , (nn.BatchNormad, nn.GroupNorm)):
nn.init.constant_(module.weight , 1)
nn.init.constant_(module.bias , 0)
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Any , UpperCamelCase__ : List[Any]=False):
'''simple docstring'''
if isinstance(UpperCamelCase__ , UpperCamelCase__):
snake_case__ = value
a__ = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
a__ = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare ResNet model outputting raw features without any specific head on top.''' , lowercase_ , )
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Tuple):
'''simple docstring'''
super().__init__(UpperCamelCase__)
snake_case__ = config
snake_case__ = ResNetEmbeddings(UpperCamelCase__)
snake_case__ = ResNetEncoder(UpperCamelCase__)
snake_case__ = nn.AdaptiveAvgPoolad((1, 1))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCamelCase__)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def __magic_name__ ( self : int , UpperCamelCase__ : Tensor , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[bool] = None):
'''simple docstring'''
snake_case__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case__ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case__ = self.embedder(UpperCamelCase__)
snake_case__ = self.encoder(
UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__)
snake_case__ = encoder_outputs[0]
snake_case__ = self.pooler(UpperCamelCase__)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=UpperCamelCase__ , pooler_output=UpperCamelCase__ , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , lowercase_ , )
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : List[str]):
'''simple docstring'''
super().__init__(UpperCamelCase__)
snake_case__ = config.num_labels
snake_case__ = ResNetModel(UpperCamelCase__)
# classification head
snake_case__ = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCamelCase__)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Optional[torch.FloatTensor] = None , UpperCamelCase__ : Optional[torch.LongTensor] = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[bool] = None , ):
'''simple docstring'''
snake_case__ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case__ = self.resnet(UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__)
snake_case__ = outputs.pooler_output if return_dict else outputs[1]
snake_case__ = self.classifier(UpperCamelCase__)
snake_case__ = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
snake_case__ = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
snake_case__ = """single_label_classification"""
else:
snake_case__ = """multi_label_classification"""
if self.config.problem_type == "regression":
snake_case__ = MSELoss()
if self.num_labels == 1:
snake_case__ = loss_fct(logits.squeeze() , labels.squeeze())
else:
snake_case__ = loss_fct(UpperCamelCase__ , UpperCamelCase__)
elif self.config.problem_type == "single_label_classification":
snake_case__ = CrossEntropyLoss()
snake_case__ = loss_fct(logits.view(-1 , self.num_labels) , labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
snake_case__ = BCEWithLogitsLoss()
snake_case__ = loss_fct(UpperCamelCase__ , UpperCamelCase__)
if not return_dict:
snake_case__ = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=UpperCamelCase__ , logits=UpperCamelCase__ , hidden_states=outputs.hidden_states)
@add_start_docstrings(
'''
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
''' , lowercase_ , )
class _lowerCAmelCase ( lowercase_ , lowercase_ ):
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : Dict):
'''simple docstring'''
super().__init__(UpperCamelCase__)
super()._init_backbone(UpperCamelCase__)
snake_case__ = [config.embedding_size] + config.hidden_sizes
snake_case__ = ResNetEmbeddings(UpperCamelCase__)
snake_case__ = ResNetEncoder(UpperCamelCase__)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UpperCamelCase__)
@replace_return_docstrings(output_type=UpperCamelCase__ , config_class=_CONFIG_FOR_DOC)
def __magic_name__ ( self : int , UpperCamelCase__ : Tensor , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[bool] = None):
'''simple docstring'''
snake_case__ = return_dict if return_dict is not None else self.config.use_return_dict
snake_case__ = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
snake_case__ = self.embedder(UpperCamelCase__)
snake_case__ = self.encoder(UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , return_dict=UpperCamelCase__)
snake_case__ = outputs.hidden_states
snake_case__ = ()
for idx, stage in enumerate(self.stage_names):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
snake_case__ = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=UpperCamelCase__ , hidden_states=outputs.hidden_states if output_hidden_states else None , attentions=UpperCamelCase__ , )
| 654 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def _UpperCAmelCase ( a : List[str] , a : Any=False ):
snake_case__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """deit.embeddings.cls_token"""),
("""dist_token""", """deit.embeddings.distillation_token"""),
("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """deit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
snake_case__ = [(pair[0], pair[1][4:]) if pair[1].startswith("""deit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("""norm.weight""", """deit.layernorm.weight"""),
("""norm.bias""", """deit.layernorm.bias"""),
("""head.weight""", """cls_classifier.weight"""),
("""head.bias""", """cls_classifier.bias"""),
("""head_dist.weight""", """distillation_classifier.weight"""),
("""head_dist.bias""", """distillation_classifier.bias"""),
] )
return rename_keys
def _UpperCAmelCase ( a : int , a : List[Any] , a : Union[str, Any]=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case__ = """"""
else:
snake_case__ = """deit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
snake_case__ = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ = in_proj_weight[
: config.hidden_size, :
]
snake_case__ = in_proj_bias[: config.hidden_size]
snake_case__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ = in_proj_bias[-config.hidden_size :]
def _UpperCAmelCase ( a : Dict , a : Union[str, Any] , a : int ):
snake_case__ = dct.pop(a )
snake_case__ = val
def _UpperCAmelCase ( ):
snake_case__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ = Image.open(requests.get(a , stream=a ).raw )
return im
@torch.no_grad()
def _UpperCAmelCase ( a : List[str] , a : Tuple ):
snake_case__ = DeiTConfig()
# all deit models have fine-tuned heads
snake_case__ = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
snake_case__ = 1000
snake_case__ = """huggingface/label-files"""
snake_case__ = """imagenet-1k-id2label.json"""
snake_case__ = json.load(open(hf_hub_download(a , a , repo_type="""dataset""" ) , """r""" ) )
snake_case__ = {int(a ): v for k, v in idalabel.items()}
snake_case__ = idalabel
snake_case__ = {v: k for k, v in idalabel.items()}
snake_case__ = int(deit_name[-6:-4] )
snake_case__ = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("""tiny""" ):
snake_case__ = 192
snake_case__ = 768
snake_case__ = 12
snake_case__ = 3
elif deit_name[9:].startswith("""small""" ):
snake_case__ = 384
snake_case__ = 1536
snake_case__ = 12
snake_case__ = 6
if deit_name[9:].startswith("""base""" ):
pass
elif deit_name[4:].startswith("""large""" ):
snake_case__ = 1024
snake_case__ = 4096
snake_case__ = 24
snake_case__ = 16
# load original model from timm
snake_case__ = timm.create_model(a , pretrained=a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case__ = timm_model.state_dict()
snake_case__ = create_rename_keys(a , a )
for src, dest in rename_keys:
rename_key(a , a , a )
read_in_q_k_v(a , a , a )
# load HuggingFace model
snake_case__ = DeiTForImageClassificationWithTeacher(a ).eval()
model.load_state_dict(a )
# Check outputs on an image, prepared by DeiTImageProcessor
snake_case__ = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
snake_case__ = DeiTImageProcessor(size=a , crop_size=config.image_size )
snake_case__ = image_processor(images=prepare_img() , return_tensors="""pt""" )
snake_case__ = encoding["""pixel_values"""]
snake_case__ = model(a )
snake_case__ = timm_model(a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(a , outputs.logits , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(a )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--deit_name""",
default="""vit_deit_base_distilled_patch16_224""",
type=str,
help="""Name of the DeiT timm model 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_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 654 | 1 |
import re
def _UpperCAmelCase ( a : str ):
if len(re.findall("""[ATCG]""" , a ) ) != len(a ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , """TAGC""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : torch.FloatTensor
class _lowerCAmelCase ( lowercase_ , lowercase_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Tuple , UpperCamelCase__ : int = 3_2 , UpperCamelCase__ : int = 6_4 , UpperCamelCase__ : int = 2_0 , UpperCamelCase__ : int = 7_6_8 , UpperCamelCase__ : Optional[Any]=7_7 , UpperCamelCase__ : str=4 , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : str = "silu" , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[str] = "linear" , UpperCamelCase__ : Optional[str] = "prd" , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[int] = None , ):
'''simple docstring'''
super().__init__()
snake_case__ = num_attention_heads
snake_case__ = attention_head_dim
snake_case__ = num_attention_heads * attention_head_dim
snake_case__ = additional_embeddings
snake_case__ = time_embed_dim or inner_dim
snake_case__ = embedding_proj_dim or embedding_dim
snake_case__ = clip_embed_dim or embedding_dim
snake_case__ = Timesteps(UpperCamelCase__ , UpperCamelCase__ , 0)
snake_case__ = TimestepEmbedding(UpperCamelCase__ , UpperCamelCase__ , out_dim=UpperCamelCase__ , act_fn=UpperCamelCase__)
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
if embedding_proj_norm_type is None:
snake_case__ = None
elif embedding_proj_norm_type == "layer":
snake_case__ = nn.LayerNorm(UpperCamelCase__)
else:
raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''')
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
if encoder_hid_proj_type is None:
snake_case__ = None
elif encoder_hid_proj_type == "linear":
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
else:
raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''')
snake_case__ = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , UpperCamelCase__))
if added_emb_type == "prd":
snake_case__ = nn.Parameter(torch.zeros(1 , 1 , UpperCamelCase__))
elif added_emb_type is None:
snake_case__ = None
else:
raise ValueError(
F'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''')
snake_case__ = nn.ModuleList(
[
BasicTransformerBlock(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , dropout=UpperCamelCase__ , activation_fn="""gelu""" , attention_bias=UpperCamelCase__ , )
for d in range(UpperCamelCase__)
])
if norm_in_type == "layer":
snake_case__ = nn.LayerNorm(UpperCamelCase__)
elif norm_in_type is None:
snake_case__ = None
else:
raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.''')
snake_case__ = nn.LayerNorm(UpperCamelCase__)
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0)
causal_attention_mask.triu_(1)
snake_case__ = causal_attention_mask[None, ...]
self.register_buffer("""causal_attention_mask""" , UpperCamelCase__ , persistent=UpperCamelCase__)
snake_case__ = nn.Parameter(torch.zeros(1 , UpperCamelCase__))
snake_case__ = nn.Parameter(torch.zeros(1 , UpperCamelCase__))
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = {}
def fn_recursive_add_processors(UpperCamelCase__ : str , UpperCamelCase__ : torch.nn.Module , UpperCamelCase__ : Dict[str, AttentionProcessor]):
if hasattr(UpperCamelCase__ , """set_processor"""):
snake_case__ = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F'''{name}.{sub_name}''' , UpperCamelCase__ , UpperCamelCase__)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
return processors
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
'''simple docstring'''
snake_case__ = len(self.attn_processors.keys())
if isinstance(UpperCamelCase__ , UpperCamelCase__) and len(UpperCamelCase__) != count:
raise ValueError(
F'''A dict of processors was passed, but the number of processors {len(UpperCamelCase__)} does not match the'''
F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''')
def fn_recursive_attn_processor(UpperCamelCase__ : str , UpperCamelCase__ : torch.nn.Module , UpperCamelCase__ : Optional[int]):
if hasattr(UpperCamelCase__ , """set_processor"""):
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
module.set_processor(UpperCamelCase__)
else:
module.set_processor(processor.pop(F'''{name}.processor'''))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F'''{name}.{sub_name}''' , UpperCamelCase__ , UpperCamelCase__)
for name, module in self.named_children():
fn_recursive_attn_processor(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
def __magic_name__ ( self : Dict):
'''simple docstring'''
self.set_attn_processor(AttnProcessor())
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Union[torch.Tensor, float, int] , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[torch.FloatTensor] = None , UpperCamelCase__ : Optional[torch.BoolTensor] = None , UpperCamelCase__ : bool = True , ):
'''simple docstring'''
snake_case__ = hidden_states.shape[0]
snake_case__ = timestep
if not torch.is_tensor(UpperCamelCase__):
snake_case__ = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device)
elif torch.is_tensor(UpperCamelCase__) and len(timesteps.shape) == 0:
snake_case__ = timesteps[None].to(hidden_states.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case__ = timesteps * torch.ones(UpperCamelCase__ , dtype=timesteps.dtype , device=timesteps.device)
snake_case__ = self.time_proj(UpperCamelCase__)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
snake_case__ = timesteps_projected.to(dtype=self.dtype)
snake_case__ = self.time_embedding(UpperCamelCase__)
if self.embedding_proj_norm is not None:
snake_case__ = self.embedding_proj_norm(UpperCamelCase__)
snake_case__ = self.embedding_proj(UpperCamelCase__)
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
snake_case__ = self.encoder_hidden_states_proj(UpperCamelCase__)
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set""")
snake_case__ = self.proj_in(UpperCamelCase__)
snake_case__ = self.positional_embedding.to(hidden_states.dtype)
snake_case__ = []
snake_case__ = 0
if encoder_hidden_states is not None:
additional_embeds.append(UpperCamelCase__)
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape) == 2:
snake_case__ = proj_embeddings[:, None, :]
if len(hidden_states.shape) == 2:
snake_case__ = hidden_states[:, None, :]
snake_case__ = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
snake_case__ = self.prd_embedding.to(hidden_states.dtype).expand(UpperCamelCase__ , -1 , -1)
additional_embeds.append(UpperCamelCase__)
snake_case__ = torch.cat(
UpperCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
snake_case__ = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
snake_case__ = F.pad(
UpperCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
snake_case__ = hidden_states + positional_embeddings
if attention_mask is not None:
snake_case__ = (1 - attention_mask.to(hidden_states.dtype)) * -1_00_00.0
snake_case__ = F.pad(UpperCamelCase__ , (0, self.additional_embeddings) , value=0.0)
snake_case__ = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
snake_case__ = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0)
if self.norm_in is not None:
snake_case__ = self.norm_in(UpperCamelCase__)
for block in self.transformer_blocks:
snake_case__ = block(UpperCamelCase__ , attention_mask=UpperCamelCase__)
snake_case__ = self.norm_out(UpperCamelCase__)
if self.prd_embedding is not None:
snake_case__ = hidden_states[:, -1]
else:
snake_case__ = hidden_states[:, additional_embeddings_len:]
snake_case__ = self.proj_to_clip_embeddings(UpperCamelCase__)
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase__)
def __magic_name__ ( self : Any , UpperCamelCase__ : Any):
'''simple docstring'''
snake_case__ = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 654 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[Any] = '''llama'''
_lowercase : Union[str, Any] = ['''past_key_values''']
def __init__( self : Tuple , UpperCamelCase__ : Union[str, Any]=3_2_0_0_0 , UpperCamelCase__ : Any=4_0_9_6 , UpperCamelCase__ : str=1_1_0_0_8 , UpperCamelCase__ : Optional[Any]=3_2 , UpperCamelCase__ : str=3_2 , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Dict="silu" , UpperCamelCase__ : str=2_0_4_8 , UpperCamelCase__ : Union[str, Any]=0.02 , UpperCamelCase__ : Union[str, Any]=1E-6 , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Optional[Any]=0 , UpperCamelCase__ : Optional[Any]=1 , UpperCamelCase__ : Union[str, Any]=2 , UpperCamelCase__ : Dict=1 , UpperCamelCase__ : Dict=False , UpperCamelCase__ : Tuple=None , **UpperCamelCase__ : Union[str, Any] , ):
'''simple docstring'''
snake_case__ = vocab_size
snake_case__ = max_position_embeddings
snake_case__ = hidden_size
snake_case__ = intermediate_size
snake_case__ = num_hidden_layers
snake_case__ = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
snake_case__ = num_attention_heads
snake_case__ = num_key_value_heads
snake_case__ = hidden_act
snake_case__ = initializer_range
snake_case__ = rms_norm_eps
snake_case__ = pretraining_tp
snake_case__ = use_cache
snake_case__ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , tie_word_embeddings=UpperCamelCase__ , **UpperCamelCase__ , )
def __magic_name__ ( self : Dict):
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , UpperCamelCase__) or len(self.rope_scaling) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
F'''got {self.rope_scaling}''')
snake_case__ = self.rope_scaling.get("""type""" , UpperCamelCase__)
snake_case__ = self.rope_scaling.get("""factor""" , UpperCamelCase__)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''')
if rope_scaling_factor is None or not isinstance(UpperCamelCase__ , UpperCamelCase__) or rope_scaling_factor <= 1.0:
raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')
| 654 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
a__ = ["""gpt2"""]
a__ = """gpt2"""
if is_tf_available():
class _lowerCAmelCase ( tf.Module ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : int):
'''simple docstring'''
super().__init__()
snake_case__ = tokenizer
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase__)
snake_case__ = TFGPTaLMHeadModel.from_config(UpperCamelCase__)
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text"""),))
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = self.tokenizer(UpperCamelCase__)
snake_case__ = tokenized["""input_ids"""].to_tensor()
snake_case__ = tf.cast(input_ids_dense > 0 , tf.intaa)
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
snake_case__ = self.model(input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__)["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
super().setUp()
snake_case__ = [GPTaTokenizer.from_pretrained(UpperCamelCase__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
snake_case__ = [TFGPTaTokenizer.from_pretrained(UpperCamelCase__) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
snake_case__ = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
snake_case__ = list(zip(self.test_sentences , self.test_sentences[::-1]))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers):
for test_inputs in self.test_sentences:
snake_case__ = tokenizer([test_inputs] , return_tensors="""tf""")
snake_case__ = tf_tokenizer([test_inputs])
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
snake_case__ = python_outputs[key].numpy()
snake_case__ = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__ , tf.intaa) == tf_outputs_values))
@slow
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = tf.function(UpperCamelCase__)
for test_inputs in self.test_sentences:
snake_case__ = tf.constant(UpperCamelCase__)
snake_case__ = compiled_tokenizer(UpperCamelCase__)
snake_case__ = tf_tokenizer(UpperCamelCase__)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = ModelToSave(tokenizer=UpperCamelCase__)
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = model.serving(UpperCamelCase__) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
snake_case__ = Path(UpperCamelCase__) / """saved.model"""
tf.saved_model.save(UpperCamelCase__ , UpperCamelCase__ , signatures={"""serving_default""": model.serving})
snake_case__ = tf.saved_model.load(UpperCamelCase__)
snake_case__ = loaded_model.signatures["""serving_default"""](UpperCamelCase__)["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output))
@slow
def __magic_name__ ( self : Tuple):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = tf_tokenizer(UpperCamelCase__) # Build model with some sample inputs
snake_case__ = tf_tokenizer.get_config()
snake_case__ = TFGPTaTokenizer.from_config(UpperCamelCase__)
snake_case__ = model_from_config(UpperCamelCase__)
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))
@slow
def __magic_name__ ( self : Dict):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
snake_case__ = 1_2_3_1_2_3
for max_length in [3, 5, 1_0_2_4]:
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = tf_tokenizer(UpperCamelCase__ , max_length=UpperCamelCase__)
snake_case__ = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 654 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowercase : Union[str, Any] = StableDiffusionLDMaDPipeline
_lowercase : List[Any] = TEXT_TO_IMAGE_PARAMS
_lowercase : List[str] = TEXT_TO_IMAGE_BATCH_PARAMS
_lowercase : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
torch.manual_seed(0)
snake_case__ = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=3_2 , )
snake_case__ = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0)
snake_case__ = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=6 , out_channels=6 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0)
snake_case__ = 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 , )
snake_case__ = CLIPTextModel(UpperCamelCase__)
snake_case__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")
snake_case__ = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def __magic_name__ ( self : Dict , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[Any]=0):
'''simple docstring'''
if str(UpperCamelCase__).startswith("""mps"""):
snake_case__ = torch.manual_seed(UpperCamelCase__)
else:
snake_case__ = torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
snake_case__ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
snake_case__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ = self.get_dummy_components()
snake_case__ = StableDiffusionLDMaDPipeline(**UpperCamelCase__)
snake_case__ = ldmad_pipe.to(UpperCamelCase__)
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__)
snake_case__ = self.get_dummy_inputs(UpperCamelCase__)
snake_case__ = ldmad_pipe(**UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = rgb[0, -3:, -3:, -1]
snake_case__ = depth[0, -3:, -1]
assert rgb.shape == (1, 6_4, 6_4, 3)
assert depth.shape == (1, 6_4, 6_4)
snake_case__ = np.array(
[0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62])
snake_case__ = np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36])
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1E-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1E-2
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
snake_case__ = self.get_dummy_components()
snake_case__ = StableDiffusionLDMaDPipeline(**UpperCamelCase__)
snake_case__ = ldmad_pipe.to(UpperCamelCase__)
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__)
snake_case__ = self.get_dummy_inputs(UpperCamelCase__)
snake_case__ = 3 * [inputs["""prompt"""]]
# forward
snake_case__ = ldmad_pipe(**UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = rgb_slice_a[0, -3:, -3:, -1]
snake_case__ = depth_slice_a[0, -3:, -1]
snake_case__ = self.get_dummy_inputs(UpperCamelCase__)
snake_case__ = 3 * [inputs.pop("""prompt""")]
snake_case__ = ldmad_pipe.tokenizer(
UpperCamelCase__ , padding="""max_length""" , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors="""pt""" , )
snake_case__ = text_inputs["""input_ids"""].to(UpperCamelCase__)
snake_case__ = ldmad_pipe.text_encoder(UpperCamelCase__)[0]
snake_case__ = prompt_embeds
# forward
snake_case__ = ldmad_pipe(**UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = rgb_slice_a[0, -3:, -3:, -1]
snake_case__ = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()).max() < 1E-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()).max() < 1E-4
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
snake_case__ = """cpu""" # ensure determinism for the device-dependent torch.Generator
snake_case__ = self.get_dummy_components()
snake_case__ = PNDMScheduler(skip_prk_steps=UpperCamelCase__)
snake_case__ = StableDiffusionLDMaDPipeline(**UpperCamelCase__)
snake_case__ = ldmad_pipe.to(UpperCamelCase__)
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__)
snake_case__ = self.get_dummy_inputs(UpperCamelCase__)
snake_case__ = """french fries"""
snake_case__ = ldmad_pipe(**UpperCamelCase__ , negative_prompt=UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = rgb[0, -3:, -3:, -1]
snake_case__ = depth[0, -3:, -1]
assert rgb.shape == (1, 6_4, 6_4, 3)
assert depth.shape == (1, 6_4, 6_4)
snake_case__ = np.array(
[0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17])
snake_case__ = np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35])
assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1E-2
assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1E-2
@slow
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : Dict):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any]="cpu" , UpperCamelCase__ : str=torch.floataa , UpperCamelCase__ : Tuple=0):
'''simple docstring'''
snake_case__ = torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
snake_case__ = np.random.RandomState(UpperCamelCase__).standard_normal((1, 4, 6_4, 6_4))
snake_case__ = torch.from_numpy(UpperCamelCase__).to(device=UpperCamelCase__ , dtype=UpperCamelCase__)
snake_case__ = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
snake_case__ = StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d""")
snake_case__ = ldmad_pipe.to(UpperCamelCase__)
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__)
snake_case__ = self.get_inputs(UpperCamelCase__)
snake_case__ = ldmad_pipe(**UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = rgb[0, -3:, -3:, -1].flatten()
snake_case__ = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
assert depth.shape == (1, 5_1_2, 5_1_2)
snake_case__ = np.array(
[0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06])
snake_case__ = np.array(
[0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06])
assert np.abs(rgb_slice - expected_slice_rgb).max() < 3E-3
assert np.abs(depth_slice - expected_slice_depth).max() < 3E-3
@nightly
@require_torch_gpu
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : int):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int]="cpu" , UpperCamelCase__ : str=torch.floataa , UpperCamelCase__ : Tuple=0):
'''simple docstring'''
snake_case__ = torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
snake_case__ = np.random.RandomState(UpperCamelCase__).standard_normal((1, 4, 6_4, 6_4))
snake_case__ = torch.from_numpy(UpperCamelCase__).to(device=UpperCamelCase__ , dtype=UpperCamelCase__)
snake_case__ = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 5_0,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d""").to(UpperCamelCase__)
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__)
snake_case__ = self.get_inputs(UpperCamelCase__)
snake_case__ = ldmad_pipe(**UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = 0.49_55_86
snake_case__ = 0.33_79_55_15
snake_case__ = 1_12.4_85_18
snake_case__ = 98.48_97_46
assert np.abs(expected_rgb_mean - rgb.mean()) < 1E-3
assert np.abs(expected_rgb_std - rgb.std()) < 1E-3
assert np.abs(expected_depth_mean - depth.mean()) < 1E-3
assert np.abs(expected_depth_std - depth.std()) < 1E-3
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c""").to(UpperCamelCase__)
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__)
snake_case__ = self.get_inputs(UpperCamelCase__)
snake_case__ = ldmad_pipe(**UpperCamelCase__)
snake_case__ , snake_case__ = output.rgb, output.depth
snake_case__ = 0.4_19_41_27
snake_case__ = 0.35_37_55_86
snake_case__ = 0.5_63_85_02
snake_case__ = 0.34_68_61_03
assert rgb.shape == (1, 5_1_2, 5_1_2, 3)
assert depth.shape == (1, 5_1_2, 5_1_2, 1)
assert np.abs(expected_rgb_mean - rgb.mean()) < 1E-3
assert np.abs(expected_rgb_std - rgb.std()) < 1E-3
assert np.abs(expected_depth_mean - depth.mean()) < 1E-3
assert np.abs(expected_depth_std - depth.std()) < 1E-3
| 654 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : int = (IPNDMScheduler,)
_lowercase : int = (('''num_inference_steps''', 50),)
def __magic_name__ ( self : Any , **UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = {"""num_train_timesteps""": 1_0_0_0}
config.update(**UpperCamelCase__)
return config
def __magic_name__ ( self : int , UpperCamelCase__ : Dict=0 , **UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = dict(self.forward_default_kwargs)
snake_case__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase__)
snake_case__ = self.dummy_sample
snake_case__ = 0.1 * sample
snake_case__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case__ = self.get_scheduler_config(**UpperCamelCase__)
snake_case__ = scheduler_class(**UpperCamelCase__)
scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residuals
snake_case__ = dummy_past_residuals[:]
if time_step is None:
snake_case__ = scheduler.timesteps[len(scheduler.timesteps) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase__)
snake_case__ = scheduler_class.from_pretrained(UpperCamelCase__)
new_scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residuals
snake_case__ = dummy_past_residuals[:]
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
pass
def __magic_name__ ( self : Tuple , UpperCamelCase__ : Union[str, Any]=0 , **UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = dict(self.forward_default_kwargs)
snake_case__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase__)
snake_case__ = self.dummy_sample
snake_case__ = 0.1 * sample
snake_case__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case__ = self.get_scheduler_config()
snake_case__ = scheduler_class(**UpperCamelCase__)
scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residuals (must be after setting timesteps)
snake_case__ = dummy_past_residuals[:]
if time_step is None:
snake_case__ = scheduler.timesteps[len(scheduler.timesteps) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase__)
snake_case__ = scheduler_class.from_pretrained(UpperCamelCase__)
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residual (must be after setting timesteps)
snake_case__ = dummy_past_residuals[:]
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __magic_name__ ( self : Union[str, Any] , **UpperCamelCase__ : Dict):
'''simple docstring'''
snake_case__ = self.scheduler_classes[0]
snake_case__ = self.get_scheduler_config(**UpperCamelCase__)
snake_case__ = scheduler_class(**UpperCamelCase__)
snake_case__ = 1_0
snake_case__ = self.dummy_model()
snake_case__ = self.dummy_sample_deter
scheduler.set_timesteps(UpperCamelCase__)
for i, t in enumerate(scheduler.timesteps):
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__).prev_sample
for i, t in enumerate(scheduler.timesteps):
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__).prev_sample
return sample
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = dict(self.forward_default_kwargs)
snake_case__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase__)
for scheduler_class in self.scheduler_classes:
snake_case__ = self.get_scheduler_config()
snake_case__ = scheduler_class(**UpperCamelCase__)
snake_case__ = self.dummy_sample
snake_case__ = 0.1 * sample
if num_inference_steps is not None and hasattr(UpperCamelCase__ , """set_timesteps"""):
scheduler.set_timesteps(UpperCamelCase__)
elif num_inference_steps is not None and not hasattr(UpperCamelCase__ , """set_timesteps"""):
snake_case__ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
snake_case__ = dummy_past_residuals[:]
snake_case__ = scheduler.timesteps[5]
snake_case__ = scheduler.timesteps[6]
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCamelCase__ , time_step=UpperCamelCase__)
def __magic_name__ ( self : Dict):
'''simple docstring'''
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=UpperCamelCase__ , time_step=UpperCamelCase__)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = self.full_loop()
snake_case__ = torch.mean(torch.abs(UpperCamelCase__))
assert abs(result_mean.item() - 2_5_4_0_5_2_9) < 1_0
| 654 | 1 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Accelerator
def _UpperCAmelCase ( a : Optional[int] ):
snake_case__ = fname.split(os.path.sep )[-1]
return re.search(r"""^(.*)_\d+\.jpg$""" , a ).groups()[0]
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : List[str] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Dict=None , UpperCamelCase__ : List[str]=None):
'''simple docstring'''
snake_case__ = file_names
snake_case__ = image_transform
snake_case__ = label_to_id
def __len__( self : Optional[Any]):
'''simple docstring'''
return len(self.file_names)
def __getitem__( self : Optional[Any] , UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ = self.file_names[idx]
snake_case__ = PIL.Image.open(UpperCamelCase__)
snake_case__ = raw_image.convert("""RGB""")
if self.image_transform is not None:
snake_case__ = self.image_transform(UpperCamelCase__)
snake_case__ = extract_label(UpperCamelCase__)
if self.label_to_id is not None:
snake_case__ = self.label_to_id[label]
return {"image": image, "label": label}
def _UpperCAmelCase ( a : str , a : List[str] ):
# Initialize accelerator
if args.with_tracking:
snake_case__ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="""all""" , project_dir=args.project_dir )
else:
snake_case__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ = config["""lr"""]
snake_case__ = int(config["""num_epochs"""] )
snake_case__ = int(config["""seed"""] )
snake_case__ = int(config["""batch_size"""] )
snake_case__ = config["""image_size"""]
if not isinstance(a , (list, tuple) ):
snake_case__ = (image_size, image_size)
# Parse out whether we are saving every epoch or after a certain number of batches
if hasattr(args.checkpointing_steps , """isdigit""" ):
if args.checkpointing_steps == "epoch":
snake_case__ = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
snake_case__ = int(args.checkpointing_steps )
else:
raise ValueError(
F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
else:
snake_case__ = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
snake_case__ = os.path.split(a )[-1].split(""".""" )[0]
accelerator.init_trackers(a , a )
# Grab all the image filenames
snake_case__ = [os.path.join(args.data_dir , a ) for fname in os.listdir(args.data_dir ) if fname.endswith(""".jpg""" )]
# Build the label correspondences
snake_case__ = [extract_label(a ) for fname in file_names]
snake_case__ = list(set(a ) )
id_to_label.sort()
snake_case__ = {lbl: i for i, lbl in enumerate(a )}
# Set the seed before splitting the data.
np.random.seed(a )
torch.manual_seed(a )
torch.cuda.manual_seed_all(a )
# Split our filenames between train and validation
snake_case__ = np.random.permutation(len(a ) )
snake_case__ = int(0.8 * len(a ) )
snake_case__ = random_perm[:cut]
snake_case__ = random_perm[cut:]
# For training we use a simple RandomResizedCrop
snake_case__ = Compose([RandomResizedCrop(a , scale=(0.5, 1.0) ), ToTensor()] )
snake_case__ = PetsDataset(
[file_names[i] for i in train_split] , image_transform=a , label_to_id=a )
# For evaluation, we use a deterministic Resize
snake_case__ = Compose([Resize(a ), ToTensor()] )
snake_case__ = PetsDataset([file_names[i] for i in eval_split] , image_transform=a , label_to_id=a )
# Instantiate dataloaders.
snake_case__ = DataLoader(a , shuffle=a , batch_size=a , num_workers=4 )
snake_case__ = DataLoader(a , shuffle=a , batch_size=a , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
snake_case__ = create_model("""resnet50d""" , pretrained=a , num_classes=len(a ) )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
snake_case__ = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
snake_case__ = False
for param in model.get_classifier().parameters():
snake_case__ = True
# We normalize the batches of images to be a bit faster.
snake_case__ = torch.tensor(model.default_cfg["""mean"""] )[None, :, None, None].to(accelerator.device )
snake_case__ = torch.tensor(model.default_cfg["""std"""] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
snake_case__ = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
snake_case__ = OneCycleLR(optimizer=a , max_lr=a , epochs=a , steps_per_epoch=len(a ) )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = accelerator.prepare(
a , a , a , a , a )
# We need to keep track of how many total steps we have iterated over
snake_case__ = 0
# We also need to keep track of the starting epoch so files are named properly
snake_case__ = 0
# Potentially load in the weights and states from a previous save
if args.resume_from_checkpoint:
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}''' )
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
snake_case__ = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
snake_case__ = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
snake_case__ = os.path.splitext(a )[0]
if "epoch" in training_difference:
snake_case__ = int(training_difference.replace("""epoch_""" , """""" ) ) + 1
snake_case__ = None
else:
snake_case__ = int(training_difference.replace("""step_""" , """""" ) )
snake_case__ = resume_step // len(a )
resume_step -= starting_epoch * len(a )
# Now we train the model
for epoch in range(a , a ):
model.train()
if args.with_tracking:
snake_case__ = 0
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
# We need to skip steps until we reach the resumed step
snake_case__ = accelerator.skip_first_batches(a , a )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
snake_case__ = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
snake_case__ = {k: v.to(accelerator.device ) for k, v in batch.items()}
snake_case__ = (batch["""image"""] - mean) / std
snake_case__ = model(a )
snake_case__ = torch.nn.functional.cross_entropy(a , batch["""label"""] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(a )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(a , a ):
snake_case__ = F'''step_{overall_step}'''
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
snake_case__ = os.path.join(args.output_dir , a )
accelerator.save_state(a )
model.eval()
snake_case__ = 0
snake_case__ = 0
for step, batch in enumerate(a ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
snake_case__ = {k: v.to(accelerator.device ) for k, v in batch.items()}
snake_case__ = (batch["""image"""] - mean) / std
with torch.no_grad():
snake_case__ = model(a )
snake_case__ = outputs.argmax(dim=-1 )
snake_case__ , snake_case__ = accelerator.gather_for_metrics((predictions, batch["""label"""]) )
snake_case__ = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
snake_case__ = accurate.item() / num_elems
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}''' )
if args.with_tracking:
accelerator.log(
{
"""accuracy""": 100 * eval_metric,
"""train_loss""": total_loss.item() / len(a ),
"""epoch""": epoch,
} , step=a , )
if checkpointing_steps == "epoch":
snake_case__ = F'''epoch_{epoch}'''
if args.output_dir is not None:
snake_case__ = os.path.join(args.output_dir , a )
accelerator.save_state(a )
if args.with_tracking:
accelerator.end_training()
def _UpperCAmelCase ( ):
snake_case__ = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument("""--data_dir""" , required=a , help="""The data folder on disk.""" )
parser.add_argument("""--fp16""" , action="""store_true""" , help="""If passed, will use FP16 training.""" )
parser.add_argument(
"""--mixed_precision""" , type=a , default=a , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
parser.add_argument(
"""--checkpointing_steps""" , type=a , default=a , help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.""" , )
parser.add_argument(
"""--output_dir""" , type=a , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=a , default=a , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--with_tracking""" , action="""store_true""" , help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" , )
parser.add_argument(
"""--project_dir""" , type=a , default="""logs""" , help="""Location on where to store experiment tracking logs` and relevent project information""" , )
snake_case__ = parser.parse_args()
snake_case__ = {"""lr""": 3e-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
training_function(a , a )
if __name__ == "__main__":
main()
| 654 |
# 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 _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[Any] = (
'''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.'''
)
_lowercase : Dict = '''CIDAS/clipseg-rd64-refined'''
_lowercase : List[Any] = '''image_segmenter'''
_lowercase : Tuple = CLIPSegForImageSegmentation
_lowercase : str = ['''image''', '''text''']
_lowercase : Dict = ['''image''']
def __init__( self : Optional[int] , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
requires_backends(self , ["""vision"""])
super().__init__(*UpperCamelCase__ , **UpperCamelCase__)
def __magic_name__ ( self : str , UpperCamelCase__ : "Image" , UpperCamelCase__ : str):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=UpperCamelCase__ , return_tensors="""pt""")
def __magic_name__ ( self : Any , UpperCamelCase__ : Optional[Any]):
'''simple docstring'''
with torch.no_grad():
snake_case__ = self.model(**UpperCamelCase__).logits
return logits
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = outputs.cpu().detach().numpy()
snake_case__ = 0
snake_case__ = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta))
| 654 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ = logging.get_logger(__name__)
a__ = {
"""microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}
class _lowerCAmelCase ( lowercase_ , lowercase_ ):
"""simple docstring"""
_lowercase : Tuple = '''focalnet'''
def __init__( self : Optional[int] , UpperCamelCase__ : List[Any]=2_2_4 , UpperCamelCase__ : Tuple=4 , UpperCamelCase__ : int=3 , UpperCamelCase__ : str=9_6 , UpperCamelCase__ : int=False , UpperCamelCase__ : List[Any]=[1_9_2, 3_8_4, 7_6_8, 7_6_8] , UpperCamelCase__ : Union[str, Any]=[2, 2, 6, 2] , UpperCamelCase__ : Any=[2, 2, 2, 2] , UpperCamelCase__ : Optional[Any]=[3, 3, 3, 3] , UpperCamelCase__ : Dict="gelu" , UpperCamelCase__ : Optional[Any]=4.0 , UpperCamelCase__ : Optional[int]=0.0 , UpperCamelCase__ : List[str]=0.1 , UpperCamelCase__ : Union[str, Any]=False , UpperCamelCase__ : str=1E-4 , UpperCamelCase__ : int=False , UpperCamelCase__ : int=False , UpperCamelCase__ : Tuple=False , UpperCamelCase__ : Optional[Any]=0.02 , UpperCamelCase__ : List[Any]=1E-5 , UpperCamelCase__ : Dict=3_2 , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : str=None , **UpperCamelCase__ : Optional[int] , ):
'''simple docstring'''
super().__init__(**UpperCamelCase__)
snake_case__ = image_size
snake_case__ = patch_size
snake_case__ = num_channels
snake_case__ = embed_dim
snake_case__ = use_conv_embed
snake_case__ = hidden_sizes
snake_case__ = depths
snake_case__ = focal_levels
snake_case__ = focal_windows
snake_case__ = hidden_act
snake_case__ = mlp_ratio
snake_case__ = hidden_dropout_prob
snake_case__ = drop_path_rate
snake_case__ = use_layerscale
snake_case__ = layerscale_value
snake_case__ = use_post_layernorm
snake_case__ = use_post_layernorm_in_modulation
snake_case__ = normalize_modulator
snake_case__ = initializer_range
snake_case__ = layer_norm_eps
snake_case__ = encoder_stride
snake_case__ = ["""stem"""] + [F'''stage{idx}''' for idx in range(1 , len(self.depths) + 1)]
snake_case__ , snake_case__ = get_aligned_output_features_output_indices(
out_features=UpperCamelCase__ , out_indices=UpperCamelCase__ , stage_names=self.stage_names)
| 654 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Dict=1_8 , UpperCamelCase__ : Any=3_0 , UpperCamelCase__ : List[Any]=4_0_0 , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : Any=None , UpperCamelCase__ : Optional[int]=True , ):
'''simple docstring'''
snake_case__ = size if size is not None else {"""height""": 1_8, """width""": 1_8}
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = num_channels
snake_case__ = image_size
snake_case__ = min_resolution
snake_case__ = max_resolution
snake_case__ = do_resize
snake_case__ = size
snake_case__ = apply_ocr
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCAmelCase ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : str = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = LayoutLMvaImageProcessingTester(self)
@property
def __magic_name__ ( self : Tuple):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase__ , """do_resize"""))
self.assertTrue(hasattr(UpperCamelCase__ , """size"""))
self.assertTrue(hasattr(UpperCamelCase__ , """apply_ocr"""))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"""height""": 1_8, """width""": 1_8})
snake_case__ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2)
self.assertEqual(image_processor.size , {"""height""": 4_2, """width""": 4_2})
def __magic_name__ ( self : List[str]):
'''simple docstring'''
pass
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image)
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors="""pt""")
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
self.assertIsInstance(encoding.words , UpperCamelCase__)
self.assertIsInstance(encoding.boxes , UpperCamelCase__)
# Test batched
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray)
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor)
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = LayoutLMvaImageProcessor()
from datasets import load_dataset
snake_case__ = load_dataset("""hf-internal-testing/fixtures_docvqa""" , split="""test""")
snake_case__ = Image.open(ds[0]["""file"""]).convert("""RGB""")
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""")
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4))
self.assertEqual(len(encoding.words) , len(encoding.boxes))
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
snake_case__ = [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]] # noqa: E231
snake_case__ = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , UpperCamelCase__)
self.assertListEqual(encoding.boxes , UpperCamelCase__)
# with apply_OCR = False
snake_case__ = LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__)
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""")
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4))
| 654 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a__ = 5_0_0_0_0_0
a__ , a__ = os.path.split(__file__)
a__ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def _UpperCAmelCase ( a : datasets.Dataset , **a : Tuple ):
snake_case__ = dataset.map(**a )
@get_duration
def _UpperCAmelCase ( a : datasets.Dataset , **a : Optional[Any] ):
snake_case__ = dataset.filter(**a )
def _UpperCAmelCase ( ):
snake_case__ = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
snake_case__ = generate_example_dataset(
os.path.join(a , """dataset.arrow""" ) , a , num_examples=a )
snake_case__ = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=a )
def tokenize(a : Union[str, Any] ):
return tokenizer(examples["""text"""] )
snake_case__ = map(a )
snake_case__ = map(a , batched=a )
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""numpy""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""pandas""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
snake_case__ = map(a , function=a , batched=a )
snake_case__ = filter(a )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(a , """wb""" ) as f:
f.write(json.dumps(a ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 654 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = params
snake_case__ = np.array(UpperCamelCase__)
snake_case__ = np.array([len(UpperCamelCase__) for t in data])
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict , UpperCamelCase__ : Any):
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self : Union[str, Any]):
'''simple docstring'''
return len(self.lengths)
def __magic_name__ ( self : str):
'''simple docstring'''
assert len(self.token_ids) == len(self.lengths)
assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.params.max_model_input_size
snake_case__ = self.lengths > max_len
logger.info(F'''Splitting {sum(UpperCamelCase__)} too long sequences.''')
def divide_chunks(UpperCamelCase__ : str , UpperCamelCase__ : Tuple):
return [l[i : i + n] for i in range(0 , len(UpperCamelCase__) , UpperCamelCase__)]
snake_case__ = []
snake_case__ = []
if self.params.mlm:
snake_case__ , snake_case__ = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
snake_case__ , snake_case__ = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_)
new_lengths.append(len_)
else:
snake_case__ = []
for sub_s in divide_chunks(seq_ , max_len - 2):
if sub_s[0] != cls_id:
snake_case__ = np.insert(UpperCamelCase__ , 0 , UpperCamelCase__)
if sub_s[-1] != sep_id:
snake_case__ = np.insert(UpperCamelCase__ , len(UpperCamelCase__) , UpperCamelCase__)
assert len(UpperCamelCase__) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(UpperCamelCase__)
new_tok_ids.extend(UpperCamelCase__)
new_lengths.extend([len(UpperCamelCase__) for l in sub_seqs])
snake_case__ = np.array(UpperCamelCase__)
snake_case__ = np.array(UpperCamelCase__)
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = len(self)
snake_case__ = self.lengths > 1_1
snake_case__ = self.token_ids[indices]
snake_case__ = self.lengths[indices]
snake_case__ = len(self)
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''')
def __magic_name__ ( self : List[str]):
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
snake_case__ = self.params.special_tok_ids["""unk_token"""]
snake_case__ = len(self)
snake_case__ = np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
snake_case__ = (unk_occs / self.lengths) < 0.5
snake_case__ = self.token_ids[indices]
snake_case__ = self.lengths[indices]
snake_case__ = len(self)
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''')
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
if not self.params.is_master:
return
logger.info(F'''{len(self)} sequences''')
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def __magic_name__ ( self : int , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
snake_case__ = [t[0] for t in batch]
snake_case__ = [t[1] for t in batch]
assert len(UpperCamelCase__) == len(UpperCamelCase__)
# Max for paddings
snake_case__ = max(UpperCamelCase__)
# Pad token ids
if self.params.mlm:
snake_case__ = self.params.special_tok_ids["""pad_token"""]
else:
snake_case__ = self.params.special_tok_ids["""unk_token"""]
snake_case__ = [list(t.astype(UpperCamelCase__)) + [pad_idx] * (max_seq_len_ - len(UpperCamelCase__)) for t in token_ids]
assert len(tk_) == len(UpperCamelCase__)
assert all(len(UpperCamelCase__) == max_seq_len_ for t in tk_)
snake_case__ = torch.tensor(tk_) # (bs, max_seq_len_)
snake_case__ = torch.tensor(UpperCamelCase__) # (bs)
return tk_t, lg_t
| 654 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
a__ = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
)
)
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
]
)
def _UpperCAmelCase ( a : Tuple , a : str , a : Tuple ):
snake_case__ = state_dict.pop(a )
snake_case__ = val
def _UpperCAmelCase ( a : List[str] ):
snake_case__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
snake_case__ = key.replace("""backbone.0.body""" , """backbone.conv_encoder.model""" )
snake_case__ = value
else:
snake_case__ = value
return new_state_dict
def _UpperCAmelCase ( a : Optional[int] , a : List[str]=False ):
snake_case__ = """"""
if is_panoptic:
snake_case__ = """conditional_detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
snake_case__ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case__ = state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ = in_proj_weight[:256, :]
snake_case__ = in_proj_bias[:256]
snake_case__ = in_proj_weight[256:512, :]
snake_case__ = in_proj_bias[256:512]
snake_case__ = in_proj_weight[-256:, :]
snake_case__ = in_proj_bias[-256:]
def _UpperCAmelCase ( ):
snake_case__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ = Image.open(requests.get(a , stream=a ).raw )
return im
@torch.no_grad()
def _UpperCAmelCase ( a : List[Any] , a : Tuple ):
snake_case__ = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
snake_case__ = """resnet101"""
if "dc5" in model_name:
snake_case__ = True
snake_case__ = """panoptic""" in model_name
if is_panoptic:
snake_case__ = 250
else:
snake_case__ = 91
snake_case__ = """huggingface/label-files"""
snake_case__ = """coco-detection-id2label.json"""
snake_case__ = json.load(open(hf_hub_download(a , a , repo_type="""dataset""" ) , """r""" ) )
snake_case__ = {int(a ): v for k, v in idalabel.items()}
snake_case__ = idalabel
snake_case__ = {v: k for k, v in idalabel.items()}
# load image processor
snake_case__ = """coco_panoptic""" if is_panoptic else """coco_detection"""
snake_case__ = ConditionalDetrImageProcessor(format=a )
# prepare image
snake_case__ = prepare_img()
snake_case__ = image_processor(images=a , return_tensors="""pt""" )
snake_case__ = encoding["""pixel_values"""]
logger.info(F'''Converting model {model_name}...''' )
# load original model from torch hub
snake_case__ = torch.hub.load("""DeppMeng/ConditionalDETR""" , a , pretrained=a ).eval()
snake_case__ = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
snake_case__ = """conditional_detr.""" + src
rename_key(a , a , a )
snake_case__ = rename_backbone_keys(a )
# query, key and value matrices need special treatment
read_in_q_k_v(a , is_panoptic=a )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case__ = """conditional_detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("""conditional_detr""" )
and not key.startswith("""class_labels_classifier""" )
and not key.startswith("""bbox_predictor""" )
):
snake_case__ = state_dict.pop(a )
snake_case__ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case__ = state_dict.pop(a )
snake_case__ = val
elif key.startswith("""bbox_attention""" ) or key.startswith("""mask_head""" ):
continue
else:
snake_case__ = state_dict.pop(a )
snake_case__ = val
else:
if not key.startswith("""class_labels_classifier""" ) and not key.startswith("""bbox_predictor""" ):
snake_case__ = state_dict.pop(a )
snake_case__ = val
# finally, create HuggingFace model and load state dict
snake_case__ = ConditionalDetrForSegmentation(a ) if is_panoptic else ConditionalDetrForObjectDetection(a )
model.load_state_dict(a )
model.eval()
model.push_to_hub(repo_id=a , organization="""DepuMeng""" , commit_message="""Add model""" )
# verify our conversion
snake_case__ = conditional_detr(a )
snake_case__ = model(a )
assert torch.allclose(outputs.logits , original_outputs["""pred_logits"""] , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs["""pred_boxes"""] , atol=1e-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["""pred_masks"""] , atol=1e-4 )
# Save model and image processor
logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(a ).mkdir(exist_ok=a )
model.save_pretrained(a )
image_processor.save_pretrained(a )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""conditional_detr_resnet50""",
type=str,
help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
a__ = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 654 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _UpperCAmelCase ( a : str ):
if "model" in orig_key:
snake_case__ = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
snake_case__ = orig_key.replace("""norm1""" , """attention.output.LayerNorm""" )
if "norm2" in orig_key:
snake_case__ = orig_key.replace("""norm2""" , """output.LayerNorm""" )
if "norm" in orig_key:
snake_case__ = orig_key.replace("""norm""" , """LayerNorm""" )
if "transformer" in orig_key:
snake_case__ = orig_key.split(""".""" )[0].split("""_""" )[-1]
snake_case__ = orig_key.replace(F'''transformer_{layer_num}''' , F'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
snake_case__ = orig_key.replace("""mha.attn""" , """attention.self""" )
if "mha" in orig_key:
snake_case__ = orig_key.replace("""mha""" , """attention""" )
if "W_q" in orig_key:
snake_case__ = orig_key.replace("""W_q""" , """self.query""" )
if "W_k" in orig_key:
snake_case__ = orig_key.replace("""W_k""" , """self.key""" )
if "W_v" in orig_key:
snake_case__ = orig_key.replace("""W_v""" , """self.value""" )
if "ff1" in orig_key:
snake_case__ = orig_key.replace("""ff1""" , """intermediate.dense""" )
if "ff2" in orig_key:
snake_case__ = orig_key.replace("""ff2""" , """output.dense""" )
if "ff" in orig_key:
snake_case__ = orig_key.replace("""ff""" , """output.dense""" )
if "mlm_class" in orig_key:
snake_case__ = orig_key.replace("""mlm.mlm_class""" , """cls.predictions.decoder""" )
if "mlm" in orig_key:
snake_case__ = orig_key.replace("""mlm""" , """cls.predictions.transform""" )
if "cls" not in orig_key:
snake_case__ = """yoso.""" + orig_key
return orig_key
def _UpperCAmelCase ( a : Tuple , a : Dict ):
for key in orig_state_dict.copy().keys():
snake_case__ = orig_state_dict.pop(a )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
snake_case__ = val
snake_case__ = orig_state_dict["""cls.predictions.decoder.bias"""]
snake_case__ = torch.arange(a ).expand((1, -1) ) + 2
return orig_state_dict
def _UpperCAmelCase ( a : int , a : List[Any] , a : List[Any] ):
snake_case__ = torch.load(a , map_location="""cpu""" )["""model_state_dict"""]
snake_case__ = YosoConfig.from_json_file(a )
snake_case__ = YosoForMaskedLM(a )
snake_case__ = convert_checkpoint_helper(config.max_position_embeddings , a )
print(model.load_state_dict(a ) )
model.eval()
model.save_pretrained(a )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The json file for YOSO model config.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a__ = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 654 | 1 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : List[str]=sys.maxsize):
'''simple docstring'''
snake_case__ = """bilinear"""
snake_case__ = max_size
snake_case__ = short_edge_length
def __call__( self : List[str] , UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = []
for img in imgs:
snake_case__ , snake_case__ = img.shape[:2]
# later: provide list and randomly choose index for resize
snake_case__ = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1)
if size == 0:
return img
snake_case__ = size * 1.0 / min(UpperCamelCase__ , UpperCamelCase__)
if h < w:
snake_case__ , snake_case__ = size, scale * w
else:
snake_case__ , snake_case__ = scale * h, size
if max(UpperCamelCase__ , UpperCamelCase__) > self.max_size:
snake_case__ = self.max_size * 1.0 / max(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = newh * scale
snake_case__ = neww * scale
snake_case__ = int(neww + 0.5)
snake_case__ = int(newh + 0.5)
if img.dtype == np.uinta:
snake_case__ = Image.fromarray(UpperCamelCase__)
snake_case__ = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR)
snake_case__ = np.asarray(UpperCamelCase__)
else:
snake_case__ = img.permute(2 , 0 , 1).unsqueeze(0) # 3, 0, 1) # hw(c) -> nchw
snake_case__ = nn.functional.interpolate(
UpperCamelCase__ , (newh, neww) , mode=self.interp_method , align_corners=UpperCamelCase__).squeeze(0)
img_augs.append(UpperCamelCase__)
return img_augs
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
snake_case__ = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST)
snake_case__ = cfg.INPUT.FORMAT
snake_case__ = cfg.SIZE_DIVISIBILITY
snake_case__ = cfg.PAD_VALUE
snake_case__ = cfg.INPUT.MAX_SIZE_TEST
snake_case__ = cfg.MODEL.DEVICE
snake_case__ = torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD) , 1 , 1)
snake_case__ = torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD) , 1 , 1)
snake_case__ = lambda UpperCamelCase__: (x - self.pixel_mean) / self.pixel_std
def __magic_name__ ( self : Dict , UpperCamelCase__ : Dict):
'''simple docstring'''
snake_case__ = tuple(max(UpperCamelCase__) for s in zip(*[img.shape for img in images]))
snake_case__ = [im.shape[-2:] for im in images]
snake_case__ = [
nn.functional.pad(
UpperCamelCase__ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(UpperCamelCase__ , UpperCamelCase__)
]
return torch.stack(UpperCamelCase__), torch.tensor(UpperCamelCase__)
def __call__( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : str=False):
'''simple docstring'''
with torch.no_grad():
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
snake_case__ = [images]
if single_image:
assert len(UpperCamelCase__) == 1
for i in range(len(UpperCamelCase__)):
if isinstance(images[i] , torch.Tensor):
images.insert(UpperCamelCase__ , images.pop(UpperCamelCase__).to(self.device).float())
elif not isinstance(images[i] , torch.Tensor):
images.insert(
UpperCamelCase__ , torch.as_tensor(img_tensorize(images.pop(UpperCamelCase__) , input_format=self.input_format))
.to(self.device)
.float() , )
# resize smallest edge
snake_case__ = torch.tensor([im.shape[:2] for im in images])
snake_case__ = self.aug(UpperCamelCase__)
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
snake_case__ = [self.normalizer(UpperCamelCase__) for x in images]
# now pad them to do the following operations
snake_case__ , snake_case__ = self.pad(UpperCamelCase__)
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
snake_case__ = torch.true_divide(UpperCamelCase__ , UpperCamelCase__)
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _UpperCAmelCase ( a : Optional[Any] , a : Any ):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _UpperCAmelCase ( a : Any , a : Tuple[int, int] ):
assert torch.isfinite(a ).all(), "Box tensor contains infinite or NaN!"
snake_case__ , snake_case__ = box_size
tensor[:, 0].clamp_(min=0 , max=a )
tensor[:, 1].clamp_(min=0 , max=a )
tensor[:, 2].clamp_(min=0 , max=a )
tensor[:, 3].clamp_(min=0 , max=a )
| 654 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = ''''''
_lowercase : str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
_lowercase : str = None # compression type in fsspec. ex: "gzip"
_lowercase : str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : List[Any] , UpperCamelCase__ : str = "" , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[dict] = None , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
super().__init__(self , **UpperCamelCase__)
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
snake_case__ = fsspec.open(
UpperCamelCase__ , mode="""rb""" , protocol=UpperCamelCase__ , compression=self.compression , client_kwargs={
"""requote_redirect_url""": False, # see https://github.com/huggingface/datasets/pull/5459
"""trust_env""": True, # Enable reading proxy env variables.
**(target_options or {}).pop("""client_kwargs""" , {}), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
snake_case__ = os.path.basename(self.file.path.split("""::""")[0])
snake_case__ = (
self.compressed_name[: self.compressed_name.rindex(""".""")]
if """.""" in self.compressed_name
else self.compressed_name
)
snake_case__ = None
@classmethod
def __magic_name__ ( cls : Union[str, Any] , UpperCamelCase__ : List[Any]):
'''simple docstring'''
return super()._strip_protocol(UpperCamelCase__).lstrip("""/""")
def __magic_name__ ( self : Dict):
'''simple docstring'''
if self.dir_cache is None:
snake_case__ = {**self.file.fs.info(self.file.path), """name""": self.uncompressed_name}
snake_case__ = {f["""name"""]: f}
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : str):
'''simple docstring'''
return self.file.open().read()
def __magic_name__ ( self : int , UpperCamelCase__ : str , UpperCamelCase__ : str = "rb" , UpperCamelCase__ : Any=None , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Optional[int]=None , **UpperCamelCase__ : Optional[Any] , ):
'''simple docstring'''
snake_case__ = self._strip_protocol(UpperCamelCase__)
if mode != "rb":
raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''')
return self.file.open()
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = '''bz2'''
_lowercase : Dict = '''bz2'''
_lowercase : Optional[int] = '''.bz2'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = '''gzip'''
_lowercase : List[str] = '''gzip'''
_lowercase : Any = '''.gz'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : str = '''lz4'''
_lowercase : List[Any] = '''lz4'''
_lowercase : Dict = '''.lz4'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = '''xz'''
_lowercase : Union[str, Any] = '''xz'''
_lowercase : Optional[int] = '''.xz'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = '''zstd'''
_lowercase : Tuple = '''zstd'''
_lowercase : Union[str, Any] = '''.zst'''
def __init__( self : str , UpperCamelCase__ : str , UpperCamelCase__ : str = "rb" , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[dict] = None , UpperCamelCase__ : int = DEFAULT_BLOCK_SIZE , **UpperCamelCase__ : int , ):
'''simple docstring'''
super().__init__(
fo=UpperCamelCase__ , mode=UpperCamelCase__ , target_protocol=UpperCamelCase__ , target_options=UpperCamelCase__ , block_size=UpperCamelCase__ , **UpperCamelCase__ , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
snake_case__ = self.file.__enter__
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : str):
'''simple docstring'''
snake_case__ = file_
def __enter__( self : List[str]):
'''simple docstring'''
self._file.__enter__()
return self
def __exit__( self : Dict , *UpperCamelCase__ : str , **UpperCamelCase__ : Optional[Any]):
'''simple docstring'''
self._file.__exit__(*UpperCamelCase__ , **UpperCamelCase__)
def __iter__( self : Any):
'''simple docstring'''
return iter(self._file)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
return next(self._file)
def __getattr__( self : Any , UpperCamelCase__ : int):
'''simple docstring'''
return getattr(self._file , UpperCamelCase__)
def fixed_enter(*UpperCamelCase__ : int , **UpperCamelCase__ : int):
return WrappedFile(_enter(*UpperCamelCase__ , **UpperCamelCase__))
snake_case__ = fixed_enter
| 654 | 1 |
# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
a__ = {
"""Acehnese Arabic""": """ace_Arab""",
"""Acehnese Latin""": """ace_Latn""",
"""Mesopotamian Arabic""": """acm_Arab""",
"""Ta'izzi-Adeni Arabic""": """acq_Arab""",
"""Tunisian Arabic""": """aeb_Arab""",
"""Afrikaans""": """afr_Latn""",
"""South Levantine Arabic""": """ajp_Arab""",
"""Akan""": """aka_Latn""",
"""Amharic""": """amh_Ethi""",
"""North Levantine Arabic""": """apc_Arab""",
"""Modern Standard Arabic""": """arb_Arab""",
"""Modern Standard Arabic Romanized""": """arb_Latn""",
"""Najdi Arabic""": """ars_Arab""",
"""Moroccan Arabic""": """ary_Arab""",
"""Egyptian Arabic""": """arz_Arab""",
"""Assamese""": """asm_Beng""",
"""Asturian""": """ast_Latn""",
"""Awadhi""": """awa_Deva""",
"""Central Aymara""": """ayr_Latn""",
"""South Azerbaijani""": """azb_Arab""",
"""North Azerbaijani""": """azj_Latn""",
"""Bashkir""": """bak_Cyrl""",
"""Bambara""": """bam_Latn""",
"""Balinese""": """ban_Latn""",
"""Belarusian""": """bel_Cyrl""",
"""Bemba""": """bem_Latn""",
"""Bengali""": """ben_Beng""",
"""Bhojpuri""": """bho_Deva""",
"""Banjar Arabic""": """bjn_Arab""",
"""Banjar Latin""": """bjn_Latn""",
"""Standard Tibetan""": """bod_Tibt""",
"""Bosnian""": """bos_Latn""",
"""Buginese""": """bug_Latn""",
"""Bulgarian""": """bul_Cyrl""",
"""Catalan""": """cat_Latn""",
"""Cebuano""": """ceb_Latn""",
"""Czech""": """ces_Latn""",
"""Chokwe""": """cjk_Latn""",
"""Central Kurdish""": """ckb_Arab""",
"""Crimean Tatar""": """crh_Latn""",
"""Welsh""": """cym_Latn""",
"""Danish""": """dan_Latn""",
"""German""": """deu_Latn""",
"""Southwestern Dinka""": """dik_Latn""",
"""Dyula""": """dyu_Latn""",
"""Dzongkha""": """dzo_Tibt""",
"""Greek""": """ell_Grek""",
"""English""": """eng_Latn""",
"""Esperanto""": """epo_Latn""",
"""Estonian""": """est_Latn""",
"""Basque""": """eus_Latn""",
"""Ewe""": """ewe_Latn""",
"""Faroese""": """fao_Latn""",
"""Fijian""": """fij_Latn""",
"""Finnish""": """fin_Latn""",
"""Fon""": """fon_Latn""",
"""French""": """fra_Latn""",
"""Friulian""": """fur_Latn""",
"""Nigerian Fulfulde""": """fuv_Latn""",
"""Scottish Gaelic""": """gla_Latn""",
"""Irish""": """gle_Latn""",
"""Galician""": """glg_Latn""",
"""Guarani""": """grn_Latn""",
"""Gujarati""": """guj_Gujr""",
"""Haitian Creole""": """hat_Latn""",
"""Hausa""": """hau_Latn""",
"""Hebrew""": """heb_Hebr""",
"""Hindi""": """hin_Deva""",
"""Chhattisgarhi""": """hne_Deva""",
"""Croatian""": """hrv_Latn""",
"""Hungarian""": """hun_Latn""",
"""Armenian""": """hye_Armn""",
"""Igbo""": """ibo_Latn""",
"""Ilocano""": """ilo_Latn""",
"""Indonesian""": """ind_Latn""",
"""Icelandic""": """isl_Latn""",
"""Italian""": """ita_Latn""",
"""Javanese""": """jav_Latn""",
"""Japanese""": """jpn_Jpan""",
"""Kabyle""": """kab_Latn""",
"""Jingpho""": """kac_Latn""",
"""Kamba""": """kam_Latn""",
"""Kannada""": """kan_Knda""",
"""Kashmiri Arabic""": """kas_Arab""",
"""Kashmiri Devanagari""": """kas_Deva""",
"""Georgian""": """kat_Geor""",
"""Central Kanuri Arabic""": """knc_Arab""",
"""Central Kanuri Latin""": """knc_Latn""",
"""Kazakh""": """kaz_Cyrl""",
"""Kabiyè""": """kbp_Latn""",
"""Kabuverdianu""": """kea_Latn""",
"""Khmer""": """khm_Khmr""",
"""Kikuyu""": """kik_Latn""",
"""Kinyarwanda""": """kin_Latn""",
"""Kyrgyz""": """kir_Cyrl""",
"""Kimbundu""": """kmb_Latn""",
"""Northern Kurdish""": """kmr_Latn""",
"""Kikongo""": """kon_Latn""",
"""Korean""": """kor_Hang""",
"""Lao""": """lao_Laoo""",
"""Ligurian""": """lij_Latn""",
"""Limburgish""": """lim_Latn""",
"""Lingala""": """lin_Latn""",
"""Lithuanian""": """lit_Latn""",
"""Lombard""": """lmo_Latn""",
"""Latgalian""": """ltg_Latn""",
"""Luxembourgish""": """ltz_Latn""",
"""Luba-Kasai""": """lua_Latn""",
"""Ganda""": """lug_Latn""",
"""Luo""": """luo_Latn""",
"""Mizo""": """lus_Latn""",
"""Standard Latvian""": """lvs_Latn""",
"""Magahi""": """mag_Deva""",
"""Maithili""": """mai_Deva""",
"""Malayalam""": """mal_Mlym""",
"""Marathi""": """mar_Deva""",
"""Minangkabau Arabic """: """min_Arab""",
"""Minangkabau Latin""": """min_Latn""",
"""Macedonian""": """mkd_Cyrl""",
"""Plateau Malagasy""": """plt_Latn""",
"""Maltese""": """mlt_Latn""",
"""Meitei Bengali""": """mni_Beng""",
"""Halh Mongolian""": """khk_Cyrl""",
"""Mossi""": """mos_Latn""",
"""Maori""": """mri_Latn""",
"""Burmese""": """mya_Mymr""",
"""Dutch""": """nld_Latn""",
"""Norwegian Nynorsk""": """nno_Latn""",
"""Norwegian Bokmål""": """nob_Latn""",
"""Nepali""": """npi_Deva""",
"""Northern Sotho""": """nso_Latn""",
"""Nuer""": """nus_Latn""",
"""Nyanja""": """nya_Latn""",
"""Occitan""": """oci_Latn""",
"""West Central Oromo""": """gaz_Latn""",
"""Odia""": """ory_Orya""",
"""Pangasinan""": """pag_Latn""",
"""Eastern Panjabi""": """pan_Guru""",
"""Papiamento""": """pap_Latn""",
"""Western Persian""": """pes_Arab""",
"""Polish""": """pol_Latn""",
"""Portuguese""": """por_Latn""",
"""Dari""": """prs_Arab""",
"""Southern Pashto""": """pbt_Arab""",
"""Ayacucho Quechua""": """quy_Latn""",
"""Romanian""": """ron_Latn""",
"""Rundi""": """run_Latn""",
"""Russian""": """rus_Cyrl""",
"""Sango""": """sag_Latn""",
"""Sanskrit""": """san_Deva""",
"""Santali""": """sat_Olck""",
"""Sicilian""": """scn_Latn""",
"""Shan""": """shn_Mymr""",
"""Sinhala""": """sin_Sinh""",
"""Slovak""": """slk_Latn""",
"""Slovenian""": """slv_Latn""",
"""Samoan""": """smo_Latn""",
"""Shona""": """sna_Latn""",
"""Sindhi""": """snd_Arab""",
"""Somali""": """som_Latn""",
"""Southern Sotho""": """sot_Latn""",
"""Spanish""": """spa_Latn""",
"""Tosk Albanian""": """als_Latn""",
"""Sardinian""": """srd_Latn""",
"""Serbian""": """srp_Cyrl""",
"""Swati""": """ssw_Latn""",
"""Sundanese""": """sun_Latn""",
"""Swedish""": """swe_Latn""",
"""Swahili""": """swh_Latn""",
"""Silesian""": """szl_Latn""",
"""Tamil""": """tam_Taml""",
"""Tatar""": """tat_Cyrl""",
"""Telugu""": """tel_Telu""",
"""Tajik""": """tgk_Cyrl""",
"""Tagalog""": """tgl_Latn""",
"""Thai""": """tha_Thai""",
"""Tigrinya""": """tir_Ethi""",
"""Tamasheq Latin""": """taq_Latn""",
"""Tamasheq Tifinagh""": """taq_Tfng""",
"""Tok Pisin""": """tpi_Latn""",
"""Tswana""": """tsn_Latn""",
"""Tsonga""": """tso_Latn""",
"""Turkmen""": """tuk_Latn""",
"""Tumbuka""": """tum_Latn""",
"""Turkish""": """tur_Latn""",
"""Twi""": """twi_Latn""",
"""Central Atlas Tamazight""": """tzm_Tfng""",
"""Uyghur""": """uig_Arab""",
"""Ukrainian""": """ukr_Cyrl""",
"""Umbundu""": """umb_Latn""",
"""Urdu""": """urd_Arab""",
"""Northern Uzbek""": """uzn_Latn""",
"""Venetian""": """vec_Latn""",
"""Vietnamese""": """vie_Latn""",
"""Waray""": """war_Latn""",
"""Wolof""": """wol_Latn""",
"""Xhosa""": """xho_Latn""",
"""Eastern Yiddish""": """ydd_Hebr""",
"""Yoruba""": """yor_Latn""",
"""Yue Chinese""": """yue_Hant""",
"""Chinese Simplified""": """zho_Hans""",
"""Chinese Traditional""": """zho_Hant""",
"""Standard Malay""": """zsm_Latn""",
"""Zulu""": """zul_Latn""",
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[str] = '''facebook/nllb-200-distilled-600M'''
_lowercase : List[Any] = (
'''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
'''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
'''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
'''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
)
_lowercase : Optional[int] = '''translator'''
_lowercase : Optional[Any] = AutoTokenizer
_lowercase : Dict = AutoModelForSeqaSeqLM
_lowercase : List[str] = LANGUAGE_CODES
_lowercase : Optional[Any] = ['''text''', '''text''', '''text''']
_lowercase : Tuple = ['''text''']
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(F'''{src_lang} is not a supported language.''')
if tgt_lang not in self.lang_to_code:
raise ValueError(F'''{tgt_lang} is not a supported language.''')
snake_case__ = self.lang_to_code[src_lang]
snake_case__ = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
UpperCamelCase__ , return_tensors="""pt""" , src_lang=UpperCamelCase__ , tgt_lang=UpperCamelCase__)
def __magic_name__ ( self : Dict , UpperCamelCase__ : Dict):
'''simple docstring'''
return self.model.generate(**UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : Dict):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=UpperCamelCase__)
| 654 |
def _UpperCAmelCase ( a : int ):
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : UNetaDModel
_lowercase : ScoreSdeVeScheduler
def __init__( self : Union[str, Any] , UpperCamelCase__ : UNetaDModel , UpperCamelCase__ : ScoreSdeVeScheduler):
'''simple docstring'''
super().__init__()
self.register_modules(unet=UpperCamelCase__ , scheduler=UpperCamelCase__)
@torch.no_grad()
def __call__( self : Union[str, Any] , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 2_0_0_0 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , **UpperCamelCase__ : List[str] , ):
'''simple docstring'''
snake_case__ = self.unet.config.sample_size
snake_case__ = (batch_size, 3, img_size, img_size)
snake_case__ = self.unet
snake_case__ = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__) * self.scheduler.init_noise_sigma
snake_case__ = sample.to(self.device)
self.scheduler.set_timesteps(UpperCamelCase__)
self.scheduler.set_sigmas(UpperCamelCase__)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
snake_case__ = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
snake_case__ = self.unet(UpperCamelCase__ , UpperCamelCase__).sample
snake_case__ = self.scheduler.step_correct(UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__).prev_sample
# prediction step
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__).sample
snake_case__ = self.scheduler.step_pred(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__)
snake_case__ , snake_case__ = output.prev_sample, output.prev_sample_mean
snake_case__ = sample_mean.clamp(0 , 1)
snake_case__ = sample.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
snake_case__ = self.numpy_to_pil(UpperCamelCase__)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=UpperCamelCase__)
| 654 |
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = size
snake_case__ = [0] * size
snake_case__ = [0] * size
@staticmethod
def __magic_name__ ( UpperCamelCase__ : int):
'''simple docstring'''
return index | (index + 1)
@staticmethod
def __magic_name__ ( UpperCamelCase__ : int):
'''simple docstring'''
return (index & (index + 1)) - 1
def __magic_name__ ( self : Any , UpperCamelCase__ : int , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = value
while index < self.size:
snake_case__ = self.get_prev(UpperCamelCase__) + 1
if current_left_border == index:
snake_case__ = value
else:
snake_case__ = max(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ = self.get_next(UpperCamelCase__)
def __magic_name__ ( self : int , UpperCamelCase__ : int , UpperCamelCase__ : int):
'''simple docstring'''
right -= 1 # Because of right is exclusive
snake_case__ = 0
while left <= right:
snake_case__ = self.get_prev(UpperCamelCase__)
if left <= current_left:
snake_case__ = max(UpperCamelCase__ , self.tree[right])
snake_case__ = current_left
else:
snake_case__ = max(UpperCamelCase__ , self.arr[right])
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : torch.FloatTensor
_lowercase : torch.FloatTensor
class _lowerCAmelCase ( lowercase_ , lowercase_ ):
"""simple docstring"""
_lowercase : str = 1
@register_to_config
def __init__( self : List[Any] , UpperCamelCase__ : int = 2_0_0_0 , UpperCamelCase__ : float = 0.15 , UpperCamelCase__ : float = 0.01 , UpperCamelCase__ : float = 13_48.0 , UpperCamelCase__ : float = 1E-5 , UpperCamelCase__ : int = 1 , ):
'''simple docstring'''
snake_case__ = sigma_max
# setable values
snake_case__ = None
self.set_sigmas(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[int] = None):
'''simple docstring'''
return sample
def __magic_name__ ( self : str , UpperCamelCase__ : int , UpperCamelCase__ : float = None , UpperCamelCase__ : Union[str, torch.device] = None):
'''simple docstring'''
snake_case__ = sampling_eps if sampling_eps is not None else self.config.sampling_eps
snake_case__ = torch.linspace(1 , UpperCamelCase__ , UpperCamelCase__ , device=UpperCamelCase__)
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : float = None , UpperCamelCase__ : float = None , UpperCamelCase__ : float = None):
'''simple docstring'''
snake_case__ = sigma_min if sigma_min is not None else self.config.sigma_min
snake_case__ = sigma_max if sigma_max is not None else self.config.sigma_max
snake_case__ = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
snake_case__ = torch.exp(torch.linspace(math.log(UpperCamelCase__) , math.log(UpperCamelCase__) , UpperCamelCase__))
snake_case__ = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps])
def __magic_name__ ( self : Optional[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device)) , self.discrete_sigmas[timesteps - 1].to(timesteps.device) , )
def __magic_name__ ( self : str , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : int , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[torch.Generator] = None , UpperCamelCase__ : bool = True , ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""")
snake_case__ = timestep * torch.ones(
sample.shape[0] , device=sample.device) # torch.repeat_interleave(timestep, sample.shape[0])
snake_case__ = (timestep * (len(self.timesteps) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
snake_case__ = timesteps.to(self.discrete_sigmas.device)
snake_case__ = self.discrete_sigmas[timesteps].to(sample.device)
snake_case__ = self.get_adjacent_sigma(UpperCamelCase__ , UpperCamelCase__).to(sample.device)
snake_case__ = torch.zeros_like(UpperCamelCase__)
snake_case__ = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
snake_case__ = diffusion.flatten()
while len(diffusion.shape) < len(sample.shape):
snake_case__ = diffusion.unsqueeze(-1)
snake_case__ = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
snake_case__ = randn_tensor(
sample.shape , layout=sample.layout , generator=UpperCamelCase__ , device=sample.device , dtype=sample.dtype)
snake_case__ = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
snake_case__ = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=UpperCamelCase__ , prev_sample_mean=UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[torch.Generator] = None , UpperCamelCase__ : bool = True , ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""")
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
snake_case__ = randn_tensor(sample.shape , layout=sample.layout , generator=UpperCamelCase__).to(sample.device)
# compute step size from the model_output, the noise, and the snr
snake_case__ = torch.norm(model_output.reshape(model_output.shape[0] , -1) , dim=-1).mean()
snake_case__ = torch.norm(noise.reshape(noise.shape[0] , -1) , dim=-1).mean()
snake_case__ = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
snake_case__ = step_size * torch.ones(sample.shape[0]).to(sample.device)
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
snake_case__ = step_size.flatten()
while len(step_size.shape) < len(sample.shape):
snake_case__ = step_size.unsqueeze(-1)
snake_case__ = sample + step_size * model_output
snake_case__ = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=UpperCamelCase__)
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : torch.FloatTensor , ):
'''simple docstring'''
snake_case__ = timesteps.to(original_samples.device)
snake_case__ = self.discrete_sigmas.to(original_samples.device)[timesteps]
snake_case__ = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(UpperCamelCase__) * sigmas[:, None, None, None]
)
snake_case__ = noise + original_samples
return noisy_samples
def __len__( self : Dict):
'''simple docstring'''
return self.config.num_train_timesteps
| 654 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class _lowerCAmelCase :
"""simple docstring"""
_lowercase : List[str] = PegasusConfig
_lowercase : Union[str, Any] = {}
_lowercase : Tuple = '''gelu'''
def __init__( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int]=1_3 , UpperCamelCase__ : Any=7 , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : int=9_9 , UpperCamelCase__ : Dict=3_2 , UpperCamelCase__ : str=2 , UpperCamelCase__ : int=4 , UpperCamelCase__ : Tuple=3_7 , UpperCamelCase__ : Union[str, Any]=0.1 , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : str=4_0 , UpperCamelCase__ : Optional[int]=2 , UpperCamelCase__ : Optional[Any]=1 , UpperCamelCase__ : Dict=0 , ):
'''simple docstring'''
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = seq_length
snake_case__ = is_training
snake_case__ = use_labels
snake_case__ = vocab_size
snake_case__ = hidden_size
snake_case__ = num_hidden_layers
snake_case__ = num_attention_heads
snake_case__ = intermediate_size
snake_case__ = hidden_dropout_prob
snake_case__ = attention_probs_dropout_prob
snake_case__ = max_position_embeddings
snake_case__ = eos_token_id
snake_case__ = pad_token_id
snake_case__ = bos_token_id
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
snake_case__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size)
snake_case__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) , 1)
snake_case__ = tf.concat([input_ids, eos_tensor] , axis=1)
snake_case__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
snake_case__ = self.config_cls(
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_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
snake_case__ = prepare_pegasus_inputs_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
return config, inputs_dict
def __magic_name__ ( self : Any , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = TFPegasusModel(config=UpperCamelCase__).get_decoder()
snake_case__ = inputs_dict["""input_ids"""]
snake_case__ = input_ids[:1, :]
snake_case__ = inputs_dict["""attention_mask"""][:1, :]
snake_case__ = inputs_dict["""head_mask"""]
snake_case__ = 1
# first forward pass
snake_case__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ , use_cache=UpperCamelCase__)
snake_case__ , snake_case__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case__ = ids_tensor((self.batch_size, 3) , config.vocab_size)
snake_case__ = tf.cast(ids_tensor((self.batch_size, 3) , 2) , tf.inta)
# append to next input_ids and
snake_case__ = tf.concat([input_ids, next_tokens] , axis=-1)
snake_case__ = tf.concat([attention_mask, next_attn_mask] , axis=-1)
snake_case__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__)[0]
snake_case__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__)[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1])
# select random slice
snake_case__ = int(ids_tensor((1,) , output_from_past.shape[-1]))
snake_case__ = output_from_no_past[:, -3:, random_slice_idx]
snake_case__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCamelCase__ , UpperCamelCase__ , rtol=1E-3)
def _UpperCAmelCase ( a : str , a : Union[str, Any] , a : List[str] , a : str=None , a : int=None , a : int=None , a : int=None , a : Optional[int]=None , ):
if attention_mask is None:
snake_case__ = tf.cast(tf.math.not_equal(a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
snake_case__ = tf.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": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : int = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_lowercase : List[Any] = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_lowercase : List[Any] = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowercase : Optional[int] = True
_lowercase : Dict = False
_lowercase : Any = False
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = TFPegasusModelTester(self)
snake_case__ = ConfigTester(self , config_class=UpperCamelCase__)
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__)
@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowercase : List[str] = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_lowercase : str = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_lowercase : int = '''google/pegasus-xsum'''
@cached_property
def __magic_name__ ( self : Dict):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.model_name)
@cached_property
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
return model
def __magic_name__ ( self : Dict , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
snake_case__ = self.translate_src_text(**UpperCamelCase__)
assert self.expected_text == generated_words
def __magic_name__ ( self : str , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
snake_case__ = self.tokenizer(self.src_text , **UpperCamelCase__ , padding=UpperCamelCase__ , return_tensors="""tf""")
snake_case__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=UpperCamelCase__ , )
snake_case__ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=UpperCamelCase__)
return generated_words
@slow
def __magic_name__ ( self : List[str]):
'''simple docstring'''
self._assert_generated_batch_equal_expected()
| 654 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
a__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Any = ['''input_features''']
def __init__( self : List[str] , UpperCamelCase__ : List[str]=8_0 , UpperCamelCase__ : List[str]=1_6_0_0_0 , UpperCamelCase__ : Union[str, Any]=1_6_0 , UpperCamelCase__ : Optional[Any]=3_0 , UpperCamelCase__ : Optional[Any]=4_0_0 , UpperCamelCase__ : Optional[int]=0.0 , UpperCamelCase__ : Dict=False , **UpperCamelCase__ : Dict , ):
'''simple docstring'''
super().__init__(
feature_size=UpperCamelCase__ , sampling_rate=UpperCamelCase__ , padding_value=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case__ = n_fft
snake_case__ = hop_length
snake_case__ = chunk_length
snake_case__ = chunk_length * sampling_rate
snake_case__ = self.n_samples // hop_length
snake_case__ = sampling_rate
snake_case__ = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 , num_mel_filters=UpperCamelCase__ , min_frequency=0.0 , max_frequency=80_00.0 , sampling_rate=UpperCamelCase__ , norm="""slaney""" , mel_scale="""slaney""" , )
def __magic_name__ ( self : Any , UpperCamelCase__ : np.array):
'''simple docstring'''
snake_case__ = spectrogram(
UpperCamelCase__ , window_function(self.n_fft , """hann""") , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="""log10""" , )
snake_case__ = log_spec[:, :-1]
snake_case__ = np.maximum(UpperCamelCase__ , log_spec.max() - 8.0)
snake_case__ = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def __magic_name__ ( UpperCamelCase__ : List[np.ndarray] , UpperCamelCase__ : List[np.ndarray] , UpperCamelCase__ : float = 0.0):
'''simple docstring'''
if attention_mask is not None:
snake_case__ = np.array(UpperCamelCase__ , np.intaa)
snake_case__ = []
for vector, length in zip(UpperCamelCase__ , attention_mask.sum(-1)):
snake_case__ = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7)
if length < normed_slice.shape[0]:
snake_case__ = padding_value
normed_input_values.append(UpperCamelCase__)
else:
snake_case__ = [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]
return normed_input_values
def __call__( self : Optional[int] , UpperCamelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , UpperCamelCase__ : bool = True , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[str] = "max_length" , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[bool] = None , **UpperCamelCase__ : str , ):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''')
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""")
snake_case__ = isinstance(UpperCamelCase__ , np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(F'''Only mono-channel audio is supported for input to {self}''')
snake_case__ = is_batched_numpy or (
isinstance(UpperCamelCase__ , (list, tuple)) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)))
)
if is_batched:
snake_case__ = [np.asarray([speech] , dtype=np.floataa).T for speech in raw_speech]
elif not is_batched and not isinstance(UpperCamelCase__ , np.ndarray):
snake_case__ = np.asarray(UpperCamelCase__ , dtype=np.floataa)
elif isinstance(UpperCamelCase__ , np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
snake_case__ = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
snake_case__ = [np.asarray([raw_speech]).T]
snake_case__ = BatchFeature({"""input_features""": raw_speech})
# convert into correct format for padding
snake_case__ = self.pad(
UpperCamelCase__ , padding=UpperCamelCase__ , max_length=max_length if max_length else self.n_samples , truncation=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_attention_mask=return_attention_mask or do_normalize , )
# zero-mean and unit-variance normalization
if do_normalize:
snake_case__ = self.zero_mean_unit_var_norm(
padded_inputs["""input_features"""] , attention_mask=padded_inputs["""attention_mask"""] , padding_value=self.padding_value , )
snake_case__ = np.stack(padded_inputs["""input_features"""] , axis=0)
# make sure list is in array format
snake_case__ = padded_inputs.get("""input_features""").transpose(2 , 0 , 1)
snake_case__ = [self._np_extract_fbank_features(UpperCamelCase__) for waveform in input_features[0]]
if isinstance(input_features[0] , UpperCamelCase__):
snake_case__ = [np.asarray(UpperCamelCase__ , dtype=np.floataa) for feature in input_features]
else:
snake_case__ = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
snake_case__ = padded_inputs["""attention_mask"""][:, :: self.hop_length]
if return_tensors is not None:
snake_case__ = padded_inputs.convert_to_tensors(UpperCamelCase__)
return padded_inputs
def __magic_name__ ( self : Tuple):
'''simple docstring'''
snake_case__ = copy.deepcopy(self.__dict__)
snake_case__ = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 654 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
a__ = logging.get_logger(__name__)
a__ = {
"""artists_file""": """artists.json""",
"""lyrics_file""": """lyrics.json""",
"""genres_file""": """genres.json""",
}
a__ = {
"""artists_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
},
"""genres_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
},
"""lyrics_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
},
}
a__ = {
"""jukebox""": 5_1_2,
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : str = VOCAB_FILES_NAMES
_lowercase : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_lowercase : str = PRETRAINED_LYRIC_TOKENS_SIZES
_lowercase : Any = ['''input_ids''', '''attention_mask''']
def __init__( self : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int=["v3", "v2", "v2"] , UpperCamelCase__ : List[str]=5_1_2 , UpperCamelCase__ : Union[str, Any]=5 , UpperCamelCase__ : List[Any]="<|endoftext|>" , **UpperCamelCase__ : List[Any] , ):
'''simple docstring'''
snake_case__ = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__ , UpperCamelCase__) else unk_token
super().__init__(
unk_token=UpperCamelCase__ , n_genres=UpperCamelCase__ , version=UpperCamelCase__ , max_n_lyric_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case__ = version
snake_case__ = max_n_lyric_tokens
snake_case__ = n_genres
with open(UpperCamelCase__ , encoding="""utf-8""") as vocab_handle:
snake_case__ = json.load(UpperCamelCase__)
with open(UpperCamelCase__ , encoding="""utf-8""") as vocab_handle:
snake_case__ = json.load(UpperCamelCase__)
with open(UpperCamelCase__ , encoding="""utf-8""") as vocab_handle:
snake_case__ = json.load(UpperCamelCase__)
snake_case__ = R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder) == 7_9:
snake_case__ = oov.replace(R"""\-'""" , R"""\-+'""")
snake_case__ = regex.compile(UpperCamelCase__)
snake_case__ = {v: k for k, v in self.artists_encoder.items()}
snake_case__ = {v: k for k, v in self.genres_encoder.items()}
snake_case__ = {v: k for k, v in self.lyrics_encoder.items()}
@property
def __magic_name__ ( self : List[str]):
'''simple docstring'''
return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder)
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = [self.artists_encoder.get(UpperCamelCase__ , 0) for artist in list_artists]
for genres in range(len(UpperCamelCase__)):
snake_case__ = [self.genres_encoder.get(UpperCamelCase__ , 0) for genre in list_genres[genres]]
snake_case__ = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))
snake_case__ = [[self.lyrics_encoder.get(UpperCamelCase__ , 0) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
return list(UpperCamelCase__)
def __magic_name__ ( self : int , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Any , **UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ , snake_case__ , snake_case__ = self.prepare_for_tokenization(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ = self._tokenize(UpperCamelCase__)
return artist, genre, lyrics
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : str , UpperCamelCase__ : bool = False):
'''simple docstring'''
for idx in range(len(self.version)):
if self.version[idx] == "v3":
snake_case__ = artists[idx].lower()
snake_case__ = [genres[idx].lower()]
else:
snake_case__ = self._normalize(artists[idx]) + """.v2"""
snake_case__ = [
self._normalize(UpperCamelCase__) + """.v2""" for genre in genres[idx].split("""_""")
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
snake_case__ = regex.compile(R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+""")
snake_case__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
snake_case__ = {vocab[index]: index + 1 for index in range(len(UpperCamelCase__))}
snake_case__ = 0
snake_case__ = len(UpperCamelCase__) + 1
snake_case__ = self.vocab
snake_case__ = {v: k for k, v in self.vocab.items()}
snake_case__ = """"""
else:
snake_case__ = regex.compile(R"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+""")
snake_case__ = self._run_strip_accents(UpperCamelCase__)
snake_case__ = lyrics.replace("""\\""" , """\n""")
snake_case__ = self.out_of_vocab.sub("""""" , UpperCamelCase__), [], []
return artists, genres, lyrics
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : str):
'''simple docstring'''
snake_case__ = unicodedata.normalize("""NFD""" , UpperCamelCase__)
snake_case__ = []
for char in text:
snake_case__ = unicodedata.category(UpperCamelCase__)
if cat == "Mn":
continue
output.append(UpperCamelCase__)
return "".join(UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : str):
'''simple docstring'''
snake_case__ = (
[chr(UpperCamelCase__) for i in range(ord("""a""") , ord("""z""") + 1)]
+ [chr(UpperCamelCase__) for i in range(ord("""A""") , ord("""Z""") + 1)]
+ [chr(UpperCamelCase__) for i in range(ord("""0""") , ord("""9""") + 1)]
+ ["""."""]
)
snake_case__ = frozenset(UpperCamelCase__)
snake_case__ = re.compile(R"""_+""")
snake_case__ = """""".join([c if c in accepted else """_""" for c in text.lower()])
snake_case__ = pattern.sub("""_""" , UpperCamelCase__).strip("""_""")
return text
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : List[str]):
'''simple docstring'''
return " ".join(UpperCamelCase__)
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : bool = False):
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
snake_case__ = TensorType(UpperCamelCase__)
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
"""Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.""")
import tensorflow as tf
snake_case__ = tf.constant
snake_case__ = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError("""Unable to convert output to PyTorch tensors format, PyTorch is not installed.""")
import torch
snake_case__ = torch.tensor
snake_case__ = torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError("""Unable to convert output to JAX tensors format, JAX is not installed.""")
import jax.numpy as jnp # noqa: F811
snake_case__ = jnp.array
snake_case__ = _is_jax
else:
snake_case__ = np.asarray
snake_case__ = _is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
snake_case__ = [inputs]
if not is_tensor(UpperCamelCase__):
snake_case__ = as_tensor(UpperCamelCase__)
except: # noqa E722
raise ValueError(
"""Unable to create tensor, you should probably activate truncation and/or padding """
"""with 'padding=True' 'truncation=True' to have batched tensors with the same length.""")
return inputs
def __call__( self : str , UpperCamelCase__ : Any , UpperCamelCase__ : Dict , UpperCamelCase__ : Any="" , UpperCamelCase__ : Dict="pt"):
'''simple docstring'''
snake_case__ = [0, 0, 0]
snake_case__ = [artist] * len(self.version)
snake_case__ = [genres] * len(self.version)
snake_case__ , snake_case__ , snake_case__ = self.tokenize(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ , snake_case__ , snake_case__ = self._convert_token_to_id(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ = [-INFINITY] * len(full_tokens[-1])
snake_case__ = [
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=UpperCamelCase__)
for i in range(len(self.version))
]
return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks})
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(UpperCamelCase__):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
return
snake_case__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""artists_file"""])
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=UpperCamelCase__))
snake_case__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""genres_file"""])
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=UpperCamelCase__))
snake_case__ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""lyrics_file"""])
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=UpperCamelCase__))
return (artists_file, genres_file, lyrics_file)
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str]):
'''simple docstring'''
snake_case__ = self.artists_decoder.get(UpperCamelCase__)
snake_case__ = [self.genres_decoder.get(UpperCamelCase__) for genre in genres_index]
snake_case__ = [self.lyrics_decoder.get(UpperCamelCase__) for character in lyric_index]
return artist, genres, lyrics
| 654 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def _UpperCAmelCase ( a : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : nn.Module , UpperCamelCase__ : int):
'''simple docstring'''
super().__init__()
snake_case__ = module
snake_case__ = nn.Sequential(
nn.Linear(module.in_features , UpperCamelCase__ , bias=UpperCamelCase__) , nn.Linear(UpperCamelCase__ , module.out_features , bias=UpperCamelCase__) , )
snake_case__ = (2.0 / (5 * min(module.in_features , module.out_features))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=UpperCamelCase__)
nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str):
'''simple docstring'''
return self.module(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__) + self.adapter(UpperCamelCase__)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowercase : Dict = '''bigscience/bloom-1b7'''
# Constant values
_lowercase : Any = 2.109_6595_5269_2574
_lowercase : Tuple = '''Hello my name is'''
_lowercase : List[Any] = set()
EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I''' )
EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n''' )
EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University''' )
_lowercase : List[str] = 10
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = AutoTokenizer.from_pretrained(self.model_name)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : str):
'''simple docstring'''
super().setUp()
# Models and tokenizer
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="""auto""")
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
def __magic_name__ ( self : Tuple):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = self.model_abit.config
self.assertTrue(hasattr(UpperCamelCase__ , """quantization_config"""))
snake_case__ = config.to_dict()
snake_case__ = config.to_diff_dict()
snake_case__ = config.to_json_string()
def __magic_name__ ( self : Dict):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
snake_case__ = self.model_fpaa.get_memory_footprint()
snake_case__ = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE)
snake_case__ = get_some_linear_layer(self.model_abit)
self.assertTrue(linear.weight.__class__ == Paramsabit)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(UpperCamelCase__ , torch.nn.Linear):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta)
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = BitsAndBytesConfig()
snake_case__ = True
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = model_abit_from_config.generate(
input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(UpperCamelCase__)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = BitsAndBytesConfig()
with self.assertRaises(UpperCamelCase__):
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , load_in_abit=UpperCamelCase__ , device_map="""auto""" , bnb_abit_quant_type="""nf4""" , )
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__):
# Tries with `str`
self.model_abit.to("""cpu""")
with self.assertRaises(UpperCamelCase__):
# Tries with a `dtype``
self.model_abit.to(torch.floataa)
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.to(torch.device("""cuda:0"""))
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = self.model_fpaa.to(torch.floataa)
snake_case__ = self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
# Check this does not throw an error
snake_case__ = self.model_fpaa.to("""cpu""")
# Check this does not throw an error
snake_case__ = self.model_fpaa.half()
# Check this does not throw an error
snake_case__ = self.model_fpaa.float()
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" , load_in_abit=UpperCamelCase__ , device_map="""auto""")
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __magic_name__ ( cls : Optional[Any]):
'''simple docstring'''
snake_case__ = """t5-small"""
snake_case__ = """google/flan-t5-small""" # flan-t5 uses dense-act instead of dense-relu-dense
snake_case__ = AutoTokenizer.from_pretrained(cls.model_name)
snake_case__ = """Translate in German: Hello, my dog is cute"""
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Any):
'''simple docstring'''
from transformers import TaForConditionalGeneration
snake_case__ = TaForConditionalGeneration._keep_in_fpaa_modules
snake_case__ = None
# test with `t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
# test with `flan-t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
snake_case__ = modules
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit))
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
# test with `flan-t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : int):
'''simple docstring'''
super().setUp()
# model_name
snake_case__ = """bigscience/bloom-560m"""
snake_case__ = """t5-small"""
# Different types of model
snake_case__ = AutoModel.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# Sequence classification model
snake_case__ = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# CausalLM model
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# Seq2seq model
snake_case__ = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
def __magic_name__ ( self : List[str]):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Tuple):
'''simple docstring'''
super().setUp()
def __magic_name__ ( self : int):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Tuple):
'''simple docstring'''
snake_case__ = pipeline(
"""text-generation""" , model=self.model_name , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
snake_case__ = self.pipe(self.input_text)
self.assertIn(pipeline_output[0]["""generated_text"""] , self.EXPECTED_OUTPUTS)
@require_torch_multi_gpu
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
super().setUp()
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="""balanced""")
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values()) , {0, 1})
# Check that inference pass works on the model
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
# Second real batch
snake_case__ = model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = """facebook/opt-350m"""
super().setUp()
def __magic_name__ ( self : Any):
'''simple docstring'''
if version.parse(importlib.metadata.version("""bitsandbytes""")) < version.parse("""0.37.0"""):
return
# Step 1: freeze all parameters
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__)
self.assertEqual(set(model.hf_device_map.values()) , {torch.cuda.current_device()})
for param in model.parameters():
snake_case__ = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
snake_case__ = param.data.to(torch.floataa)
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(UpperCamelCase__)):
snake_case__ = LoRALayer(module.q_proj , rank=1_6)
snake_case__ = LoRALayer(module.k_proj , rank=1_6)
snake_case__ = LoRALayer(module.v_proj , rank=1_6)
# Step 3: dummy batch
snake_case__ = self.tokenizer("""Test batch """ , return_tensors="""pt""").to(0)
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
snake_case__ = model.forward(**UpperCamelCase__)
out.logits.norm().backward()
for module in model.modules():
if isinstance(UpperCamelCase__ , UpperCamelCase__):
self.assertTrue(module.adapter[1].weight.grad is not None)
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
elif isinstance(UpperCamelCase__ , nn.Embedding):
self.assertTrue(module.weight.grad is None)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[Any] = '''gpt2-xl'''
_lowercase : Any = 3.3191_8548_5415_2187
| 654 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : List[str]=sys.maxsize):
'''simple docstring'''
snake_case__ = """bilinear"""
snake_case__ = max_size
snake_case__ = short_edge_length
def __call__( self : List[str] , UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = []
for img in imgs:
snake_case__ , snake_case__ = img.shape[:2]
# later: provide list and randomly choose index for resize
snake_case__ = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1)
if size == 0:
return img
snake_case__ = size * 1.0 / min(UpperCamelCase__ , UpperCamelCase__)
if h < w:
snake_case__ , snake_case__ = size, scale * w
else:
snake_case__ , snake_case__ = scale * h, size
if max(UpperCamelCase__ , UpperCamelCase__) > self.max_size:
snake_case__ = self.max_size * 1.0 / max(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = newh * scale
snake_case__ = neww * scale
snake_case__ = int(neww + 0.5)
snake_case__ = int(newh + 0.5)
if img.dtype == np.uinta:
snake_case__ = Image.fromarray(UpperCamelCase__)
snake_case__ = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR)
snake_case__ = np.asarray(UpperCamelCase__)
else:
snake_case__ = img.permute(2 , 0 , 1).unsqueeze(0) # 3, 0, 1) # hw(c) -> nchw
snake_case__ = nn.functional.interpolate(
UpperCamelCase__ , (newh, neww) , mode=self.interp_method , align_corners=UpperCamelCase__).squeeze(0)
img_augs.append(UpperCamelCase__)
return img_augs
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
snake_case__ = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST)
snake_case__ = cfg.INPUT.FORMAT
snake_case__ = cfg.SIZE_DIVISIBILITY
snake_case__ = cfg.PAD_VALUE
snake_case__ = cfg.INPUT.MAX_SIZE_TEST
snake_case__ = cfg.MODEL.DEVICE
snake_case__ = torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD) , 1 , 1)
snake_case__ = torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD) , 1 , 1)
snake_case__ = lambda UpperCamelCase__: (x - self.pixel_mean) / self.pixel_std
def __magic_name__ ( self : Dict , UpperCamelCase__ : Dict):
'''simple docstring'''
snake_case__ = tuple(max(UpperCamelCase__) for s in zip(*[img.shape for img in images]))
snake_case__ = [im.shape[-2:] for im in images]
snake_case__ = [
nn.functional.pad(
UpperCamelCase__ , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(UpperCamelCase__ , UpperCamelCase__)
]
return torch.stack(UpperCamelCase__), torch.tensor(UpperCamelCase__)
def __call__( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : str=False):
'''simple docstring'''
with torch.no_grad():
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
snake_case__ = [images]
if single_image:
assert len(UpperCamelCase__) == 1
for i in range(len(UpperCamelCase__)):
if isinstance(images[i] , torch.Tensor):
images.insert(UpperCamelCase__ , images.pop(UpperCamelCase__).to(self.device).float())
elif not isinstance(images[i] , torch.Tensor):
images.insert(
UpperCamelCase__ , torch.as_tensor(img_tensorize(images.pop(UpperCamelCase__) , input_format=self.input_format))
.to(self.device)
.float() , )
# resize smallest edge
snake_case__ = torch.tensor([im.shape[:2] for im in images])
snake_case__ = self.aug(UpperCamelCase__)
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
snake_case__ = [self.normalizer(UpperCamelCase__) for x in images]
# now pad them to do the following operations
snake_case__ , snake_case__ = self.pad(UpperCamelCase__)
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
snake_case__ = torch.true_divide(UpperCamelCase__ , UpperCamelCase__)
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _UpperCAmelCase ( a : Optional[Any] , a : Any ):
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _UpperCAmelCase ( a : Any , a : Tuple[int, int] ):
assert torch.isfinite(a ).all(), "Box tensor contains infinite or NaN!"
snake_case__ , snake_case__ = box_size
tensor[:, 0].clamp_(min=0 , max=a )
tensor[:, 1].clamp_(min=0 , max=a )
tensor[:, 2].clamp_(min=0 , max=a )
tensor[:, 3].clamp_(min=0 , max=a )
| 654 | 1 |
def _UpperCAmelCase ( a : int = 3 , a : int = 7 , a : int = 100_0000 ):
snake_case__ = 0
snake_case__ = 1
for current_denominator in range(1 , limit + 1 ):
snake_case__ = current_denominator * numerator // denominator
if current_denominator % denominator == 0:
current_numerator -= 1
if current_numerator * max_denominator > current_denominator * max_numerator:
snake_case__ = current_numerator
snake_case__ = current_denominator
return max_numerator
if __name__ == "__main__":
print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))
| 654 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = '''wavlm'''
def __init__( self : Tuple , UpperCamelCase__ : str=3_2 , UpperCamelCase__ : Any=7_6_8 , UpperCamelCase__ : Any=1_2 , UpperCamelCase__ : Tuple=1_2 , UpperCamelCase__ : str=3_0_7_2 , UpperCamelCase__ : Optional[Any]="gelu" , UpperCamelCase__ : Tuple=0.1 , UpperCamelCase__ : Union[str, Any]=0.1 , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : str=0.0 , UpperCamelCase__ : Optional[Any]=0.1 , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : Optional[int]=0.02 , UpperCamelCase__ : Optional[int]=1E-5 , UpperCamelCase__ : Any="group" , UpperCamelCase__ : List[str]="gelu" , UpperCamelCase__ : Any=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , UpperCamelCase__ : List[str]=(5, 2, 2, 2, 2, 2, 2) , UpperCamelCase__ : Dict=(1_0, 3, 3, 3, 3, 2, 2) , UpperCamelCase__ : int=False , UpperCamelCase__ : Optional[Any]=1_2_8 , UpperCamelCase__ : Optional[int]=1_6 , UpperCamelCase__ : Optional[Any]=3_2_0 , UpperCamelCase__ : Any=8_0_0 , UpperCamelCase__ : Any=False , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : Optional[Any]=0.05 , UpperCamelCase__ : Optional[Any]=1_0 , UpperCamelCase__ : Union[str, Any]=2 , UpperCamelCase__ : Optional[Any]=0.0 , UpperCamelCase__ : Tuple=1_0 , UpperCamelCase__ : Optional[int]=3_2_0 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : Tuple=1_0_0 , UpperCamelCase__ : Dict=2_5_6 , UpperCamelCase__ : Optional[int]=2_5_6 , UpperCamelCase__ : List[Any]=0.1 , UpperCamelCase__ : Tuple="mean" , UpperCamelCase__ : List[Any]=False , UpperCamelCase__ : Tuple=False , UpperCamelCase__ : Union[str, Any]=2_5_6 , UpperCamelCase__ : int=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , UpperCamelCase__ : Optional[Any]=(5, 3, 3, 1, 1) , UpperCamelCase__ : Any=(1, 2, 3, 1, 1) , UpperCamelCase__ : Dict=5_1_2 , UpperCamelCase__ : str=8_0 , UpperCamelCase__ : Optional[int]=0 , UpperCamelCase__ : Any=1 , UpperCamelCase__ : Tuple=2 , UpperCamelCase__ : str=False , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Optional[Any]=2 , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Optional[int]=None , **UpperCamelCase__ : List[str] , ):
'''simple docstring'''
super().__init__(**UpperCamelCase__ , pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__)
snake_case__ = hidden_size
snake_case__ = feat_extract_norm
snake_case__ = feat_extract_activation
snake_case__ = list(UpperCamelCase__)
snake_case__ = list(UpperCamelCase__)
snake_case__ = list(UpperCamelCase__)
snake_case__ = conv_bias
snake_case__ = num_buckets
snake_case__ = max_bucket_distance
snake_case__ = num_conv_pos_embeddings
snake_case__ = num_conv_pos_embedding_groups
snake_case__ = len(self.conv_dim)
snake_case__ = num_hidden_layers
snake_case__ = intermediate_size
snake_case__ = hidden_act
snake_case__ = num_attention_heads
snake_case__ = hidden_dropout
snake_case__ = attention_dropout
snake_case__ = activation_dropout
snake_case__ = feat_proj_dropout
snake_case__ = final_dropout
snake_case__ = layerdrop
snake_case__ = layer_norm_eps
snake_case__ = initializer_range
snake_case__ = num_ctc_classes
snake_case__ = vocab_size
snake_case__ = do_stable_layer_norm
snake_case__ = use_weighted_layer_sum
snake_case__ = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
F''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
snake_case__ = apply_spec_augment
snake_case__ = mask_time_prob
snake_case__ = mask_time_length
snake_case__ = mask_time_min_masks
snake_case__ = mask_feature_prob
snake_case__ = mask_feature_length
# parameters for pretraining with codevector quantized representations
snake_case__ = num_codevectors_per_group
snake_case__ = num_codevector_groups
snake_case__ = contrastive_logits_temperature
snake_case__ = num_negatives
snake_case__ = codevector_dim
snake_case__ = proj_codevector_dim
snake_case__ = diversity_loss_weight
# ctc loss
snake_case__ = ctc_loss_reduction
snake_case__ = ctc_zero_infinity
# adapter
snake_case__ = add_adapter
snake_case__ = adapter_kernel_size
snake_case__ = adapter_stride
snake_case__ = num_adapter_layers
snake_case__ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
snake_case__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
snake_case__ = list(UpperCamelCase__)
snake_case__ = list(UpperCamelCase__)
snake_case__ = list(UpperCamelCase__)
snake_case__ = xvector_output_dim
@property
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 654 | 1 |
# 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 _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[Any] = (
'''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.'''
)
_lowercase : Dict = '''CIDAS/clipseg-rd64-refined'''
_lowercase : List[Any] = '''image_segmenter'''
_lowercase : Tuple = CLIPSegForImageSegmentation
_lowercase : str = ['''image''', '''text''']
_lowercase : Dict = ['''image''']
def __init__( self : Optional[int] , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
requires_backends(self , ["""vision"""])
super().__init__(*UpperCamelCase__ , **UpperCamelCase__)
def __magic_name__ ( self : str , UpperCamelCase__ : "Image" , UpperCamelCase__ : str):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=UpperCamelCase__ , return_tensors="""pt""")
def __magic_name__ ( self : Any , UpperCamelCase__ : Optional[Any]):
'''simple docstring'''
with torch.no_grad():
snake_case__ = self.model(**UpperCamelCase__).logits
return logits
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = outputs.cpu().detach().numpy()
snake_case__ = 0
snake_case__ = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta))
| 654 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : UNetaDModel
_lowercase : ScoreSdeVeScheduler
def __init__( self : Union[str, Any] , UpperCamelCase__ : UNetaDModel , UpperCamelCase__ : ScoreSdeVeScheduler):
'''simple docstring'''
super().__init__()
self.register_modules(unet=UpperCamelCase__ , scheduler=UpperCamelCase__)
@torch.no_grad()
def __call__( self : Union[str, Any] , UpperCamelCase__ : int = 1 , UpperCamelCase__ : int = 2_0_0_0 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , **UpperCamelCase__ : List[str] , ):
'''simple docstring'''
snake_case__ = self.unet.config.sample_size
snake_case__ = (batch_size, 3, img_size, img_size)
snake_case__ = self.unet
snake_case__ = randn_tensor(UpperCamelCase__ , generator=UpperCamelCase__) * self.scheduler.init_noise_sigma
snake_case__ = sample.to(self.device)
self.scheduler.set_timesteps(UpperCamelCase__)
self.scheduler.set_sigmas(UpperCamelCase__)
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
snake_case__ = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device)
# correction step
for _ in range(self.scheduler.config.correct_steps):
snake_case__ = self.unet(UpperCamelCase__ , UpperCamelCase__).sample
snake_case__ = self.scheduler.step_correct(UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__).prev_sample
# prediction step
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__).sample
snake_case__ = self.scheduler.step_pred(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , generator=UpperCamelCase__)
snake_case__ , snake_case__ = output.prev_sample, output.prev_sample_mean
snake_case__ = sample_mean.clamp(0 , 1)
snake_case__ = sample.cpu().permute(0 , 2 , 3 , 1).numpy()
if output_type == "pil":
snake_case__ = self.numpy_to_pil(UpperCamelCase__)
if not return_dict:
return (sample,)
return ImagePipelineOutput(images=UpperCamelCase__)
| 654 | 1 |
from ....utils import logging
a__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str=None , UpperCamelCase__ : Union[str, Any]=2_0_4_8):
'''simple docstring'''
snake_case__ = config.__dict__
snake_case__ = modal_hidden_size
if num_labels:
snake_case__ = num_labels
| 654 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : Optional[int] = IFInpaintingSuperResolutionPipeline
_lowercase : int = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
_lowercase : Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''} )
_lowercase : int = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
return self._get_superresolution_dummy_components()
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int]=0):
'''simple docstring'''
if str(UpperCamelCase__).startswith("""mps"""):
snake_case__ = torch.manual_seed(UpperCamelCase__)
else:
snake_case__ = torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
snake_case__ = floats_tensor((1, 3, 1_6, 1_6) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
snake_case__ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
snake_case__ = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
snake_case__ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __magic_name__ ( self : Dict):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)
def __magic_name__ ( self : int):
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""")
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
super().test_save_load_floataa(expected_max_diff=1E-1)
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
self._test_save_load_local()
def __magic_name__ ( self : str):
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 654 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : List[str] , UpperCamelCase__ : Callable , UpperCamelCase__ : Optional[Features] = None , UpperCamelCase__ : str = None , UpperCamelCase__ : bool = False , UpperCamelCase__ : bool = False , UpperCamelCase__ : Optional[dict] = None , UpperCamelCase__ : Optional[int] = None , **UpperCamelCase__ : Tuple , ):
'''simple docstring'''
super().__init__(
features=UpperCamelCase__ , cache_dir=UpperCamelCase__ , keep_in_memory=UpperCamelCase__ , streaming=UpperCamelCase__ , num_proc=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case__ = Generator(
cache_dir=UpperCamelCase__ , features=UpperCamelCase__ , generator=UpperCamelCase__ , gen_kwargs=UpperCamelCase__ , **UpperCamelCase__ , )
def __magic_name__ ( self : str):
'''simple docstring'''
if self.streaming:
snake_case__ = self.builder.as_streaming_dataset(split="""train""")
# Build regular (map-style) dataset
else:
snake_case__ = None
snake_case__ = None
snake_case__ = None
snake_case__ = None
self.builder.download_and_prepare(
download_config=UpperCamelCase__ , download_mode=UpperCamelCase__ , verification_mode=UpperCamelCase__ , base_path=UpperCamelCase__ , num_proc=self.num_proc , )
snake_case__ = self.builder.as_dataset(
split="""train""" , verification_mode=UpperCamelCase__ , in_memory=self.keep_in_memory)
return dataset
| 654 |
a__ = [0, 2, 4, 6, 8]
a__ = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( a : int , a : int , a : list[int] , a : int ):
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
snake_case__ = 0
for digit in range(10 ):
snake_case__ = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , a , a )
return result
snake_case__ = 0
for digita in range(10 ):
snake_case__ = digita
if (remainder + digita) % 2 == 0:
snake_case__ = ODD_DIGITS
else:
snake_case__ = EVEN_DIGITS
for digita in other_parity_digits:
snake_case__ = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , a , a , )
return result
def _UpperCAmelCase ( a : int = 9 ):
snake_case__ = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(a , 0 , [0] * length , a )
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 654 | 1 |
import requests
a__ = """YOUR API KEY"""
def _UpperCAmelCase ( a : str , a : str = giphy_api_key ):
snake_case__ = """+""".join(query.split() )
snake_case__ = F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
snake_case__ = requests.get(a ).json()["""data"""]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 654 |
# 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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
a__ = {
"""Acehnese Arabic""": """ace_Arab""",
"""Acehnese Latin""": """ace_Latn""",
"""Mesopotamian Arabic""": """acm_Arab""",
"""Ta'izzi-Adeni Arabic""": """acq_Arab""",
"""Tunisian Arabic""": """aeb_Arab""",
"""Afrikaans""": """afr_Latn""",
"""South Levantine Arabic""": """ajp_Arab""",
"""Akan""": """aka_Latn""",
"""Amharic""": """amh_Ethi""",
"""North Levantine Arabic""": """apc_Arab""",
"""Modern Standard Arabic""": """arb_Arab""",
"""Modern Standard Arabic Romanized""": """arb_Latn""",
"""Najdi Arabic""": """ars_Arab""",
"""Moroccan Arabic""": """ary_Arab""",
"""Egyptian Arabic""": """arz_Arab""",
"""Assamese""": """asm_Beng""",
"""Asturian""": """ast_Latn""",
"""Awadhi""": """awa_Deva""",
"""Central Aymara""": """ayr_Latn""",
"""South Azerbaijani""": """azb_Arab""",
"""North Azerbaijani""": """azj_Latn""",
"""Bashkir""": """bak_Cyrl""",
"""Bambara""": """bam_Latn""",
"""Balinese""": """ban_Latn""",
"""Belarusian""": """bel_Cyrl""",
"""Bemba""": """bem_Latn""",
"""Bengali""": """ben_Beng""",
"""Bhojpuri""": """bho_Deva""",
"""Banjar Arabic""": """bjn_Arab""",
"""Banjar Latin""": """bjn_Latn""",
"""Standard Tibetan""": """bod_Tibt""",
"""Bosnian""": """bos_Latn""",
"""Buginese""": """bug_Latn""",
"""Bulgarian""": """bul_Cyrl""",
"""Catalan""": """cat_Latn""",
"""Cebuano""": """ceb_Latn""",
"""Czech""": """ces_Latn""",
"""Chokwe""": """cjk_Latn""",
"""Central Kurdish""": """ckb_Arab""",
"""Crimean Tatar""": """crh_Latn""",
"""Welsh""": """cym_Latn""",
"""Danish""": """dan_Latn""",
"""German""": """deu_Latn""",
"""Southwestern Dinka""": """dik_Latn""",
"""Dyula""": """dyu_Latn""",
"""Dzongkha""": """dzo_Tibt""",
"""Greek""": """ell_Grek""",
"""English""": """eng_Latn""",
"""Esperanto""": """epo_Latn""",
"""Estonian""": """est_Latn""",
"""Basque""": """eus_Latn""",
"""Ewe""": """ewe_Latn""",
"""Faroese""": """fao_Latn""",
"""Fijian""": """fij_Latn""",
"""Finnish""": """fin_Latn""",
"""Fon""": """fon_Latn""",
"""French""": """fra_Latn""",
"""Friulian""": """fur_Latn""",
"""Nigerian Fulfulde""": """fuv_Latn""",
"""Scottish Gaelic""": """gla_Latn""",
"""Irish""": """gle_Latn""",
"""Galician""": """glg_Latn""",
"""Guarani""": """grn_Latn""",
"""Gujarati""": """guj_Gujr""",
"""Haitian Creole""": """hat_Latn""",
"""Hausa""": """hau_Latn""",
"""Hebrew""": """heb_Hebr""",
"""Hindi""": """hin_Deva""",
"""Chhattisgarhi""": """hne_Deva""",
"""Croatian""": """hrv_Latn""",
"""Hungarian""": """hun_Latn""",
"""Armenian""": """hye_Armn""",
"""Igbo""": """ibo_Latn""",
"""Ilocano""": """ilo_Latn""",
"""Indonesian""": """ind_Latn""",
"""Icelandic""": """isl_Latn""",
"""Italian""": """ita_Latn""",
"""Javanese""": """jav_Latn""",
"""Japanese""": """jpn_Jpan""",
"""Kabyle""": """kab_Latn""",
"""Jingpho""": """kac_Latn""",
"""Kamba""": """kam_Latn""",
"""Kannada""": """kan_Knda""",
"""Kashmiri Arabic""": """kas_Arab""",
"""Kashmiri Devanagari""": """kas_Deva""",
"""Georgian""": """kat_Geor""",
"""Central Kanuri Arabic""": """knc_Arab""",
"""Central Kanuri Latin""": """knc_Latn""",
"""Kazakh""": """kaz_Cyrl""",
"""Kabiyè""": """kbp_Latn""",
"""Kabuverdianu""": """kea_Latn""",
"""Khmer""": """khm_Khmr""",
"""Kikuyu""": """kik_Latn""",
"""Kinyarwanda""": """kin_Latn""",
"""Kyrgyz""": """kir_Cyrl""",
"""Kimbundu""": """kmb_Latn""",
"""Northern Kurdish""": """kmr_Latn""",
"""Kikongo""": """kon_Latn""",
"""Korean""": """kor_Hang""",
"""Lao""": """lao_Laoo""",
"""Ligurian""": """lij_Latn""",
"""Limburgish""": """lim_Latn""",
"""Lingala""": """lin_Latn""",
"""Lithuanian""": """lit_Latn""",
"""Lombard""": """lmo_Latn""",
"""Latgalian""": """ltg_Latn""",
"""Luxembourgish""": """ltz_Latn""",
"""Luba-Kasai""": """lua_Latn""",
"""Ganda""": """lug_Latn""",
"""Luo""": """luo_Latn""",
"""Mizo""": """lus_Latn""",
"""Standard Latvian""": """lvs_Latn""",
"""Magahi""": """mag_Deva""",
"""Maithili""": """mai_Deva""",
"""Malayalam""": """mal_Mlym""",
"""Marathi""": """mar_Deva""",
"""Minangkabau Arabic """: """min_Arab""",
"""Minangkabau Latin""": """min_Latn""",
"""Macedonian""": """mkd_Cyrl""",
"""Plateau Malagasy""": """plt_Latn""",
"""Maltese""": """mlt_Latn""",
"""Meitei Bengali""": """mni_Beng""",
"""Halh Mongolian""": """khk_Cyrl""",
"""Mossi""": """mos_Latn""",
"""Maori""": """mri_Latn""",
"""Burmese""": """mya_Mymr""",
"""Dutch""": """nld_Latn""",
"""Norwegian Nynorsk""": """nno_Latn""",
"""Norwegian Bokmål""": """nob_Latn""",
"""Nepali""": """npi_Deva""",
"""Northern Sotho""": """nso_Latn""",
"""Nuer""": """nus_Latn""",
"""Nyanja""": """nya_Latn""",
"""Occitan""": """oci_Latn""",
"""West Central Oromo""": """gaz_Latn""",
"""Odia""": """ory_Orya""",
"""Pangasinan""": """pag_Latn""",
"""Eastern Panjabi""": """pan_Guru""",
"""Papiamento""": """pap_Latn""",
"""Western Persian""": """pes_Arab""",
"""Polish""": """pol_Latn""",
"""Portuguese""": """por_Latn""",
"""Dari""": """prs_Arab""",
"""Southern Pashto""": """pbt_Arab""",
"""Ayacucho Quechua""": """quy_Latn""",
"""Romanian""": """ron_Latn""",
"""Rundi""": """run_Latn""",
"""Russian""": """rus_Cyrl""",
"""Sango""": """sag_Latn""",
"""Sanskrit""": """san_Deva""",
"""Santali""": """sat_Olck""",
"""Sicilian""": """scn_Latn""",
"""Shan""": """shn_Mymr""",
"""Sinhala""": """sin_Sinh""",
"""Slovak""": """slk_Latn""",
"""Slovenian""": """slv_Latn""",
"""Samoan""": """smo_Latn""",
"""Shona""": """sna_Latn""",
"""Sindhi""": """snd_Arab""",
"""Somali""": """som_Latn""",
"""Southern Sotho""": """sot_Latn""",
"""Spanish""": """spa_Latn""",
"""Tosk Albanian""": """als_Latn""",
"""Sardinian""": """srd_Latn""",
"""Serbian""": """srp_Cyrl""",
"""Swati""": """ssw_Latn""",
"""Sundanese""": """sun_Latn""",
"""Swedish""": """swe_Latn""",
"""Swahili""": """swh_Latn""",
"""Silesian""": """szl_Latn""",
"""Tamil""": """tam_Taml""",
"""Tatar""": """tat_Cyrl""",
"""Telugu""": """tel_Telu""",
"""Tajik""": """tgk_Cyrl""",
"""Tagalog""": """tgl_Latn""",
"""Thai""": """tha_Thai""",
"""Tigrinya""": """tir_Ethi""",
"""Tamasheq Latin""": """taq_Latn""",
"""Tamasheq Tifinagh""": """taq_Tfng""",
"""Tok Pisin""": """tpi_Latn""",
"""Tswana""": """tsn_Latn""",
"""Tsonga""": """tso_Latn""",
"""Turkmen""": """tuk_Latn""",
"""Tumbuka""": """tum_Latn""",
"""Turkish""": """tur_Latn""",
"""Twi""": """twi_Latn""",
"""Central Atlas Tamazight""": """tzm_Tfng""",
"""Uyghur""": """uig_Arab""",
"""Ukrainian""": """ukr_Cyrl""",
"""Umbundu""": """umb_Latn""",
"""Urdu""": """urd_Arab""",
"""Northern Uzbek""": """uzn_Latn""",
"""Venetian""": """vec_Latn""",
"""Vietnamese""": """vie_Latn""",
"""Waray""": """war_Latn""",
"""Wolof""": """wol_Latn""",
"""Xhosa""": """xho_Latn""",
"""Eastern Yiddish""": """ydd_Hebr""",
"""Yoruba""": """yor_Latn""",
"""Yue Chinese""": """yue_Hant""",
"""Chinese Simplified""": """zho_Hans""",
"""Chinese Traditional""": """zho_Hant""",
"""Standard Malay""": """zsm_Latn""",
"""Zulu""": """zul_Latn""",
}
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[str] = '''facebook/nllb-200-distilled-600M'''
_lowercase : List[Any] = (
'''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
'''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
'''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
'''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
)
_lowercase : Optional[int] = '''translator'''
_lowercase : Optional[Any] = AutoTokenizer
_lowercase : Dict = AutoModelForSeqaSeqLM
_lowercase : List[str] = LANGUAGE_CODES
_lowercase : Optional[Any] = ['''text''', '''text''', '''text''']
_lowercase : Tuple = ['''text''']
def __magic_name__ ( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(F'''{src_lang} is not a supported language.''')
if tgt_lang not in self.lang_to_code:
raise ValueError(F'''{tgt_lang} is not a supported language.''')
snake_case__ = self.lang_to_code[src_lang]
snake_case__ = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
UpperCamelCase__ , return_tensors="""pt""" , src_lang=UpperCamelCase__ , tgt_lang=UpperCamelCase__)
def __magic_name__ ( self : Dict , UpperCamelCase__ : Dict):
'''simple docstring'''
return self.model.generate(**UpperCamelCase__)
def __magic_name__ ( self : List[str] , UpperCamelCase__ : Dict):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=UpperCamelCase__)
| 654 | 1 |
import unittest
from knapsack import knapsack as k
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
snake_case__ = 0
snake_case__ = [0]
snake_case__ = [0]
snake_case__ = len(UpperCamelCase__)
self.assertEqual(k.knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__) , 0)
snake_case__ = [6_0]
snake_case__ = [1_0]
snake_case__ = len(UpperCamelCase__)
self.assertEqual(k.knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__) , 0)
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = 3
snake_case__ = [1, 2, 3]
snake_case__ = [3, 2, 1]
snake_case__ = len(UpperCamelCase__)
self.assertEqual(k.knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__) , 5)
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = 5_0
snake_case__ = [6_0, 1_0_0, 1_2_0]
snake_case__ = [1_0, 2_0, 3_0]
snake_case__ = len(UpperCamelCase__)
self.assertEqual(k.knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__) , 2_2_0)
if __name__ == "__main__":
unittest.main()
| 654 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_utils import (
is_torch_available,
require_accelerate,
require_bitsandbytes,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
def _UpperCAmelCase ( a : Optional[int] ):
if model.config.model_type == "gpt2":
return model.transformer.h[0].mlp.c_fc
return model.transformer.h[0].mlp.dense_ah_to_h
if is_torch_available():
import torch
import torch.nn as nn
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : nn.Module , UpperCamelCase__ : int):
'''simple docstring'''
super().__init__()
snake_case__ = module
snake_case__ = nn.Sequential(
nn.Linear(module.in_features , UpperCamelCase__ , bias=UpperCamelCase__) , nn.Linear(UpperCamelCase__ , module.out_features , bias=UpperCamelCase__) , )
snake_case__ = (2.0 / (5 * min(module.in_features , module.out_features))) ** 0.5
nn.init.normal_(self.adapter[0].weight , std=UpperCamelCase__)
nn.init.zeros_(self.adapter[1].weight)
self.adapter.to(module.weight.device)
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str):
'''simple docstring'''
return self.module(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__) + self.adapter(UpperCamelCase__)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowercase : Dict = '''bigscience/bloom-1b7'''
# Constant values
_lowercase : Any = 2.109_6595_5269_2574
_lowercase : Tuple = '''Hello my name is'''
_lowercase : List[Any] = set()
EXPECTED_OUTPUTS.add('''Hello my name is John and I am a professional photographer. I''' )
EXPECTED_OUTPUTS.add('''Hello my name is John.\nI am a friend of your father.\n''' )
EXPECTED_OUTPUTS.add('''Hello my name is John Doe, I am a student at the University''' )
_lowercase : List[str] = 10
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = AutoTokenizer.from_pretrained(self.model_name)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : str):
'''simple docstring'''
super().setUp()
# Models and tokenizer
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , torch_dtype=torch.floataa , device_map="""auto""")
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
def __magic_name__ ( self : Tuple):
'''simple docstring'''
del self.model_fpaa
del self.model_abit
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = self.model_abit.config
self.assertTrue(hasattr(UpperCamelCase__ , """quantization_config"""))
snake_case__ = config.to_dict()
snake_case__ = config.to_diff_dict()
snake_case__ = config.to_json_string()
def __magic_name__ ( self : Dict):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
snake_case__ = self.model_fpaa.get_memory_footprint()
snake_case__ = self.model_abit.get_memory_footprint()
self.assertAlmostEqual(mem_fpaa / mem_abit , self.EXPECTED_RELATIVE_DIFFERENCE)
snake_case__ = get_some_linear_layer(self.model_abit)
self.assertTrue(linear.weight.__class__ == Paramsabit)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
from transformers import TaPreTrainedModel
self.model_fpaa.get_memory_footprint()
self.model_abit.get_memory_footprint()
for name, module in self.model_abit.named_modules():
if isinstance(UpperCamelCase__ , torch.nn.Linear):
if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
# 4-bit parameters are packed in uint8 variables
self.assertTrue(module.weight.dtype == torch.uinta)
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = BitsAndBytesConfig()
snake_case__ = True
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = model_abit_from_config.generate(
input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_sequences[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__), tempfile.TemporaryDirectory() as tmpdirname:
self.model_abit.save_pretrained(UpperCamelCase__)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = BitsAndBytesConfig()
with self.assertRaises(UpperCamelCase__):
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , quantization_config=UpperCamelCase__ , load_in_abit=UpperCamelCase__ , device_map="""auto""" , bnb_abit_quant_type="""nf4""" , )
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
with self.assertRaises(UpperCamelCase__):
# Tries with `str`
self.model_abit.to("""cpu""")
with self.assertRaises(UpperCamelCase__):
# Tries with a `dtype``
self.model_abit.to(torch.floataa)
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.to(torch.device("""cuda:0"""))
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.float()
with self.assertRaises(UpperCamelCase__):
# Tries with a `device`
self.model_abit.half()
# Test if we did not break anything
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
snake_case__ = self.model_fpaa.to(torch.floataa)
snake_case__ = self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
# Check this does not throw an error
snake_case__ = self.model_fpaa.to("""cpu""")
# Check this does not throw an error
snake_case__ = self.model_fpaa.half()
# Check this does not throw an error
snake_case__ = self.model_fpaa.float()
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = AutoModelForSeqaSeqLM.from_pretrained("""t5-small""" , load_in_abit=UpperCamelCase__ , device_map="""auto""")
self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def __magic_name__ ( cls : Optional[Any]):
'''simple docstring'''
snake_case__ = """t5-small"""
snake_case__ = """google/flan-t5-small""" # flan-t5 uses dense-act instead of dense-relu-dense
snake_case__ = AutoTokenizer.from_pretrained(cls.model_name)
snake_case__ = """Translate in German: Hello, my dog is cute"""
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Any):
'''simple docstring'''
from transformers import TaForConditionalGeneration
snake_case__ = TaForConditionalGeneration._keep_in_fpaa_modules
snake_case__ = None
# test with `t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
# test with `flan-t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
snake_case__ = modules
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
import bitsandbytes as bnb
from transformers import TaForConditionalGeneration
# test with `t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# there was a bug with decoders - this test checks that it is fixed
self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q , bnb.nn.Linearabit))
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
# test with `flan-t5-small`
snake_case__ = TaForConditionalGeneration.from_pretrained(
self.dense_act_model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""").to(0)
snake_case__ = model.generate(**UpperCamelCase__)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : int):
'''simple docstring'''
super().setUp()
# model_name
snake_case__ = """bigscience/bloom-560m"""
snake_case__ = """t5-small"""
# Different types of model
snake_case__ = AutoModel.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# Sequence classification model
snake_case__ = AutoModelForSequenceClassification.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# CausalLM model
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
# Seq2seq model
snake_case__ = AutoModelForSeqaSeqLM.from_pretrained(
self.seq_to_seq_name , load_in_abit=UpperCamelCase__ , device_map="""auto""")
def __magic_name__ ( self : List[str]):
'''simple docstring'''
del self.base_model
del self.sequence_model
del self.model_abit
del self.seq_to_seq_model
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
from bitsandbytes.nn import Paramsabit
self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)
# Other heads should be nn.Parameter
self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Tuple):
'''simple docstring'''
super().setUp()
def __magic_name__ ( self : int):
'''simple docstring'''
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : Tuple):
'''simple docstring'''
snake_case__ = pipeline(
"""text-generation""" , model=self.model_name , model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa} , max_new_tokens=self.MAX_NEW_TOKENS , )
# Real second forward pass
snake_case__ = self.pipe(self.input_text)
self.assertIn(pipeline_output[0]["""generated_text"""] , self.EXPECTED_OUTPUTS)
@require_torch_multi_gpu
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
super().setUp()
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = AutoModelForCausalLM.from_pretrained(
self.model_name , load_in_abit=UpperCamelCase__ , device_map="""balanced""")
# Check correct device map
self.assertEqual(set(model_parallel.hf_device_map.values()) , {0, 1})
# Check that inference pass works on the model
snake_case__ = self.tokenizer(self.input_text , return_tensors="""pt""")
# Second real batch
snake_case__ = model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0) , max_new_tokens=1_0)
self.assertIn(self.tokenizer.decode(output_parallel[0] , skip_special_tokens=UpperCamelCase__) , self.EXPECTED_OUTPUTS)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = """facebook/opt-350m"""
super().setUp()
def __magic_name__ ( self : Any):
'''simple docstring'''
if version.parse(importlib.metadata.version("""bitsandbytes""")) < version.parse("""0.37.0"""):
return
# Step 1: freeze all parameters
snake_case__ = AutoModelForCausalLM.from_pretrained(self.model_name , load_in_abit=UpperCamelCase__)
self.assertEqual(set(model.hf_device_map.values()) , {torch.cuda.current_device()})
for param in model.parameters():
snake_case__ = False # freeze the model - train adapters later
if param.ndim == 1:
# cast the small parameters (e.g. layernorm) to fp32 for stability
snake_case__ = param.data.to(torch.floataa)
# Step 2: add adapters
for _, module in model.named_modules():
if "OPTAttention" in repr(type(UpperCamelCase__)):
snake_case__ = LoRALayer(module.q_proj , rank=1_6)
snake_case__ = LoRALayer(module.k_proj , rank=1_6)
snake_case__ = LoRALayer(module.v_proj , rank=1_6)
# Step 3: dummy batch
snake_case__ = self.tokenizer("""Test batch """ , return_tensors="""pt""").to(0)
# Step 4: Check if the gradient is not None
with torch.cuda.amp.autocast():
snake_case__ = model.forward(**UpperCamelCase__)
out.logits.norm().backward()
for module in model.modules():
if isinstance(UpperCamelCase__ , UpperCamelCase__):
self.assertTrue(module.adapter[1].weight.grad is not None)
self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
elif isinstance(UpperCamelCase__ , nn.Embedding):
self.assertTrue(module.weight.grad is None)
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : List[Any] = '''gpt2-xl'''
_lowercase : Any = 3.3191_8548_5415_2187
| 654 | 1 |
from torch import nn
def _UpperCAmelCase ( a : Any ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F'''Unsupported activation function: {act_fn}''' )
| 654 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a__ = """"""
a__ = """"""
a__ = """"""
a__ = 1 # (0 is vertical, 1 is horizontal)
def _UpperCAmelCase ( ):
snake_case__ , snake_case__ = get_dataset(a , a )
print("""Processing...""" )
snake_case__ , snake_case__ , snake_case__ = update_image_and_anno(a , a , a )
for index, image in enumerate(a ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case__ = random_chars(32 )
snake_case__ = paths[index].split(os.sep )[-1].rsplit(""".""" , 1 )[0]
snake_case__ = F'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
cva.imwrite(F'''/{file_root}.jpg''' , a , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'''Success {index+1}/{len(a )} with {file_name}''' )
snake_case__ = []
for anno in new_annos[index]:
snake_case__ = F'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
annos_list.append(a )
with open(F'''/{file_root}.txt''' , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def _UpperCAmelCase ( a : str , a : str ):
snake_case__ = []
snake_case__ = []
for label_file in glob.glob(os.path.join(a , """*.txt""" ) ):
snake_case__ = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(a ) as in_file:
snake_case__ = in_file.readlines()
snake_case__ = os.path.join(a , F'''{label_name}.jpg''' )
snake_case__ = []
for obj_list in obj_lists:
snake_case__ = obj_list.rstrip("""\n""" ).split(""" """ )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(a )
labels.append(a )
return img_paths, labels
def _UpperCAmelCase ( a : list , a : list , a : int = 1 ):
snake_case__ = []
snake_case__ = []
snake_case__ = []
for idx in range(len(a ) ):
snake_case__ = []
snake_case__ = img_list[idx]
path_list.append(a )
snake_case__ = anno_list[idx]
snake_case__ = cva.imread(a )
if flip_type == 1:
snake_case__ = cva.flip(a , a )
for bbox in img_annos:
snake_case__ = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
snake_case__ = cva.flip(a , a )
for bbox in img_annos:
snake_case__ = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(a )
new_imgs_list.append(a )
return new_imgs_list, new_annos_lists, path_list
def _UpperCAmelCase ( a : int = 32 ):
assert number_char > 1, "The number of character should greater than 1"
snake_case__ = ascii_lowercase + digits
return "".join(random.choice(a ) for _ in range(a ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 654 | 1 |
def _UpperCAmelCase ( a : int = 50 ):
snake_case__ = [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() = }''')
| 654 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a__ = 5_0_0_0_0_0
a__ , a__ = os.path.split(__file__)
a__ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def _UpperCAmelCase ( a : datasets.Dataset , **a : Tuple ):
snake_case__ = dataset.map(**a )
@get_duration
def _UpperCAmelCase ( a : datasets.Dataset , **a : Optional[Any] ):
snake_case__ = dataset.filter(**a )
def _UpperCAmelCase ( ):
snake_case__ = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
snake_case__ = generate_example_dataset(
os.path.join(a , """dataset.arrow""" ) , a , num_examples=a )
snake_case__ = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=a )
def tokenize(a : Union[str, Any] ):
return tokenizer(examples["""text"""] )
snake_case__ = map(a )
snake_case__ = map(a , batched=a )
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""numpy""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""pandas""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
snake_case__ = map(a , function=lambda a : None , batched=a )
snake_case__ = map(a , function=a , batched=a )
snake_case__ = filter(a )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(a , """wb""" ) as f:
f.write(json.dumps(a ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 654 | 1 |
import math
def _UpperCAmelCase ( ):
snake_case__ = input("""Enter message: """ )
snake_case__ = int(input(F'''Enter key [2-{len(a ) - 1}]: ''' ) )
snake_case__ = input("""Encryption/Decryption [e/d]: """ )
if mode.lower().startswith("""e""" ):
snake_case__ = encrypt_message(a , a )
elif mode.lower().startswith("""d""" ):
snake_case__ = decrypt_message(a , a )
# Append pipe symbol (vertical bar) to identify spaces at the end.
print(F'''Output:\n{text + '|'}''' )
def _UpperCAmelCase ( a : int , a : str ):
snake_case__ = [""""""] * key
for col in range(a ):
snake_case__ = col
while pointer < len(a ):
cipher_text[col] += message[pointer]
pointer += key
return "".join(a )
def _UpperCAmelCase ( a : int , a : str ):
snake_case__ = math.ceil(len(a ) / key )
snake_case__ = key
snake_case__ = (num_cols * num_rows) - len(a )
snake_case__ = [""""""] * num_cols
snake_case__ = 0
snake_case__ = 0
for symbol in message:
plain_text[col] += symbol
col += 1
if (
(col == num_cols)
or (col == num_cols - 1)
and (row >= num_rows - num_shaded_boxes)
):
snake_case__ = 0
row += 1
return "".join(a )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 654 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def _UpperCAmelCase ( a : List[str] , a : Any=False ):
snake_case__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """deit.embeddings.cls_token"""),
("""dist_token""", """deit.embeddings.distillation_token"""),
("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """deit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
snake_case__ = [(pair[0], pair[1][4:]) if pair[1].startswith("""deit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("""norm.weight""", """deit.layernorm.weight"""),
("""norm.bias""", """deit.layernorm.bias"""),
("""head.weight""", """cls_classifier.weight"""),
("""head.bias""", """cls_classifier.bias"""),
("""head_dist.weight""", """distillation_classifier.weight"""),
("""head_dist.bias""", """distillation_classifier.bias"""),
] )
return rename_keys
def _UpperCAmelCase ( a : int , a : List[Any] , a : Union[str, Any]=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case__ = """"""
else:
snake_case__ = """deit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
snake_case__ = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ = in_proj_weight[
: config.hidden_size, :
]
snake_case__ = in_proj_bias[: config.hidden_size]
snake_case__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ = in_proj_bias[-config.hidden_size :]
def _UpperCAmelCase ( a : Dict , a : Union[str, Any] , a : int ):
snake_case__ = dct.pop(a )
snake_case__ = val
def _UpperCAmelCase ( ):
snake_case__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ = Image.open(requests.get(a , stream=a ).raw )
return im
@torch.no_grad()
def _UpperCAmelCase ( a : List[str] , a : Tuple ):
snake_case__ = DeiTConfig()
# all deit models have fine-tuned heads
snake_case__ = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
snake_case__ = 1000
snake_case__ = """huggingface/label-files"""
snake_case__ = """imagenet-1k-id2label.json"""
snake_case__ = json.load(open(hf_hub_download(a , a , repo_type="""dataset""" ) , """r""" ) )
snake_case__ = {int(a ): v for k, v in idalabel.items()}
snake_case__ = idalabel
snake_case__ = {v: k for k, v in idalabel.items()}
snake_case__ = int(deit_name[-6:-4] )
snake_case__ = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("""tiny""" ):
snake_case__ = 192
snake_case__ = 768
snake_case__ = 12
snake_case__ = 3
elif deit_name[9:].startswith("""small""" ):
snake_case__ = 384
snake_case__ = 1536
snake_case__ = 12
snake_case__ = 6
if deit_name[9:].startswith("""base""" ):
pass
elif deit_name[4:].startswith("""large""" ):
snake_case__ = 1024
snake_case__ = 4096
snake_case__ = 24
snake_case__ = 16
# load original model from timm
snake_case__ = timm.create_model(a , pretrained=a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case__ = timm_model.state_dict()
snake_case__ = create_rename_keys(a , a )
for src, dest in rename_keys:
rename_key(a , a , a )
read_in_q_k_v(a , a , a )
# load HuggingFace model
snake_case__ = DeiTForImageClassificationWithTeacher(a ).eval()
model.load_state_dict(a )
# Check outputs on an image, prepared by DeiTImageProcessor
snake_case__ = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
snake_case__ = DeiTImageProcessor(size=a , crop_size=config.image_size )
snake_case__ = image_processor(images=prepare_img() , return_tensors="""pt""" )
snake_case__ = encoding["""pixel_values"""]
snake_case__ = model(a )
snake_case__ = timm_model(a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(a , outputs.logits , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(a )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--deit_name""",
default="""vit_deit_base_distilled_patch16_224""",
type=str,
help="""Name of the DeiT timm model 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_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 654 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
a__ = """base_with_context"""
def _UpperCAmelCase ( a : Any , a : Any ):
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""] ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=a )
for lyr_num, lyr in enumerate(model.encoders ):
snake_case__ = weights[F'''layers_{lyr_num}''']
snake_case__ = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
snake_case__ = ly_weight["""attention"""]
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def _UpperCAmelCase ( a : str , a : List[Any] ):
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=a )
for lyr_num, lyr in enumerate(model.encoders ):
snake_case__ = weights[F'''layers_{lyr_num}''']
snake_case__ = ly_weight["""attention"""]
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def _UpperCAmelCase ( a : Dict , a : int ):
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=a )
snake_case__ = nn.Parameter(
torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
snake_case__ = weights[F'''layers_{lyr_num}''']
snake_case__ = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
snake_case__ = ly_weight["""self_attention"""]
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
snake_case__ = ly_weight["""MultiHeadDotProductAttention_0"""]
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""] ) )
snake_case__ = nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T ) )
return model
def _UpperCAmelCase ( a : Union[str, Any] ):
snake_case__ = checkpoints.load_tax_checkpoint(args.checkpoint_path )
snake_case__ = jnp.tree_util.tree_map(onp.array , a )
snake_case__ = [
"""from __gin__ import dynamic_registration""",
"""from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
"""diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
"""diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
]
snake_case__ = os.path.join(args.checkpoint_path , """..""" , """config.gin""" )
snake_case__ = inference.parse_training_gin_file(a , a )
snake_case__ = inference.InferenceModel(args.checkpoint_path , a )
snake_case__ = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" , variance_type="""fixed_large""" )
snake_case__ = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["""inputs"""] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
snake_case__ = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["""targets_context"""] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
snake_case__ = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["""targets_context"""] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
snake_case__ = load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""] , a )
snake_case__ = load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , a )
snake_case__ = load_decoder(ta_checkpoint["""target"""]["""decoder"""] , a )
snake_case__ = OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )
snake_case__ = SpectrogramDiffusionPipeline(
notes_encoder=a , continuous_encoder=a , decoder=a , scheduler=a , melgan=a , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument("""--output_path""", default=None, type=str, required=True, help="""Path to the converted model.""")
parser.add_argument(
"""--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
)
parser.add_argument(
"""--checkpoint_path""",
default=F'''{MODEL}/checkpoint_500000''',
type=str,
required=False,
help="""Path to the original jax model checkpoint.""",
)
a__ = parser.parse_args()
main(args)
| 654 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : torch.FloatTensor
class _lowerCAmelCase ( lowercase_ , lowercase_ ):
"""simple docstring"""
@register_to_config
def __init__( self : Tuple , UpperCamelCase__ : int = 3_2 , UpperCamelCase__ : int = 6_4 , UpperCamelCase__ : int = 2_0 , UpperCamelCase__ : int = 7_6_8 , UpperCamelCase__ : Optional[Any]=7_7 , UpperCamelCase__ : str=4 , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : str = "silu" , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[str] = "linear" , UpperCamelCase__ : Optional[str] = "prd" , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[int] = None , ):
'''simple docstring'''
super().__init__()
snake_case__ = num_attention_heads
snake_case__ = attention_head_dim
snake_case__ = num_attention_heads * attention_head_dim
snake_case__ = additional_embeddings
snake_case__ = time_embed_dim or inner_dim
snake_case__ = embedding_proj_dim or embedding_dim
snake_case__ = clip_embed_dim or embedding_dim
snake_case__ = Timesteps(UpperCamelCase__ , UpperCamelCase__ , 0)
snake_case__ = TimestepEmbedding(UpperCamelCase__ , UpperCamelCase__ , out_dim=UpperCamelCase__ , act_fn=UpperCamelCase__)
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
if embedding_proj_norm_type is None:
snake_case__ = None
elif embedding_proj_norm_type == "layer":
snake_case__ = nn.LayerNorm(UpperCamelCase__)
else:
raise ValueError(F'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''')
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
if encoder_hid_proj_type is None:
snake_case__ = None
elif encoder_hid_proj_type == "linear":
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
else:
raise ValueError(F'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''')
snake_case__ = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , UpperCamelCase__))
if added_emb_type == "prd":
snake_case__ = nn.Parameter(torch.zeros(1 , 1 , UpperCamelCase__))
elif added_emb_type is None:
snake_case__ = None
else:
raise ValueError(
F'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''')
snake_case__ = nn.ModuleList(
[
BasicTransformerBlock(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , dropout=UpperCamelCase__ , activation_fn="""gelu""" , attention_bias=UpperCamelCase__ , )
for d in range(UpperCamelCase__)
])
if norm_in_type == "layer":
snake_case__ = nn.LayerNorm(UpperCamelCase__)
elif norm_in_type is None:
snake_case__ = None
else:
raise ValueError(F'''Unsupported norm_in_type: {norm_in_type}.''')
snake_case__ = nn.LayerNorm(UpperCamelCase__)
snake_case__ = nn.Linear(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = torch.full(
[num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_00_00.0)
causal_attention_mask.triu_(1)
snake_case__ = causal_attention_mask[None, ...]
self.register_buffer("""causal_attention_mask""" , UpperCamelCase__ , persistent=UpperCamelCase__)
snake_case__ = nn.Parameter(torch.zeros(1 , UpperCamelCase__))
snake_case__ = nn.Parameter(torch.zeros(1 , UpperCamelCase__))
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = {}
def fn_recursive_add_processors(UpperCamelCase__ : str , UpperCamelCase__ : torch.nn.Module , UpperCamelCase__ : Dict[str, AttentionProcessor]):
if hasattr(UpperCamelCase__ , """set_processor"""):
snake_case__ = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F'''{name}.{sub_name}''' , UpperCamelCase__ , UpperCamelCase__)
return processors
for name, module in self.named_children():
fn_recursive_add_processors(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
return processors
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
'''simple docstring'''
snake_case__ = len(self.attn_processors.keys())
if isinstance(UpperCamelCase__ , UpperCamelCase__) and len(UpperCamelCase__) != count:
raise ValueError(
F'''A dict of processors was passed, but the number of processors {len(UpperCamelCase__)} does not match the'''
F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''')
def fn_recursive_attn_processor(UpperCamelCase__ : str , UpperCamelCase__ : torch.nn.Module , UpperCamelCase__ : Optional[int]):
if hasattr(UpperCamelCase__ , """set_processor"""):
if not isinstance(UpperCamelCase__ , UpperCamelCase__):
module.set_processor(UpperCamelCase__)
else:
module.set_processor(processor.pop(F'''{name}.processor'''))
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F'''{name}.{sub_name}''' , UpperCamelCase__ , UpperCamelCase__)
for name, module in self.named_children():
fn_recursive_attn_processor(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
def __magic_name__ ( self : Dict):
'''simple docstring'''
self.set_attn_processor(AttnProcessor())
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Union[torch.Tensor, float, int] , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[torch.FloatTensor] = None , UpperCamelCase__ : Optional[torch.BoolTensor] = None , UpperCamelCase__ : bool = True , ):
'''simple docstring'''
snake_case__ = hidden_states.shape[0]
snake_case__ = timestep
if not torch.is_tensor(UpperCamelCase__):
snake_case__ = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device)
elif torch.is_tensor(UpperCamelCase__) and len(timesteps.shape) == 0:
snake_case__ = timesteps[None].to(hidden_states.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
snake_case__ = timesteps * torch.ones(UpperCamelCase__ , dtype=timesteps.dtype , device=timesteps.device)
snake_case__ = self.time_proj(UpperCamelCase__)
# timesteps does not contain any weights and will always return f32 tensors
# but time_embedding might be fp16, so we need to cast here.
snake_case__ = timesteps_projected.to(dtype=self.dtype)
snake_case__ = self.time_embedding(UpperCamelCase__)
if self.embedding_proj_norm is not None:
snake_case__ = self.embedding_proj_norm(UpperCamelCase__)
snake_case__ = self.embedding_proj(UpperCamelCase__)
if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
snake_case__ = self.encoder_hidden_states_proj(UpperCamelCase__)
elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set""")
snake_case__ = self.proj_in(UpperCamelCase__)
snake_case__ = self.positional_embedding.to(hidden_states.dtype)
snake_case__ = []
snake_case__ = 0
if encoder_hidden_states is not None:
additional_embeds.append(UpperCamelCase__)
additional_embeddings_len += encoder_hidden_states.shape[1]
if len(proj_embeddings.shape) == 2:
snake_case__ = proj_embeddings[:, None, :]
if len(hidden_states.shape) == 2:
snake_case__ = hidden_states[:, None, :]
snake_case__ = additional_embeds + [
proj_embeddings,
time_embeddings[:, None, :],
hidden_states,
]
if self.prd_embedding is not None:
snake_case__ = self.prd_embedding.to(hidden_states.dtype).expand(UpperCamelCase__ , -1 , -1)
additional_embeds.append(UpperCamelCase__)
snake_case__ = torch.cat(
UpperCamelCase__ , dim=1 , )
# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
snake_case__ = additional_embeddings_len + proj_embeddings.shape[1] + 1
if positional_embeddings.shape[1] < hidden_states.shape[1]:
snake_case__ = F.pad(
UpperCamelCase__ , (
0,
0,
additional_embeddings_len,
self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
) , value=0.0 , )
snake_case__ = hidden_states + positional_embeddings
if attention_mask is not None:
snake_case__ = (1 - attention_mask.to(hidden_states.dtype)) * -1_00_00.0
snake_case__ = F.pad(UpperCamelCase__ , (0, self.additional_embeddings) , value=0.0)
snake_case__ = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
snake_case__ = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0)
if self.norm_in is not None:
snake_case__ = self.norm_in(UpperCamelCase__)
for block in self.transformer_blocks:
snake_case__ = block(UpperCamelCase__ , attention_mask=UpperCamelCase__)
snake_case__ = self.norm_out(UpperCamelCase__)
if self.prd_embedding is not None:
snake_case__ = hidden_states[:, -1]
else:
snake_case__ = hidden_states[:, additional_embeddings_len:]
snake_case__ = self.proj_to_clip_embeddings(UpperCamelCase__)
if not return_dict:
return (predicted_image_embedding,)
return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase__)
def __magic_name__ ( self : Any , UpperCamelCase__ : Any):
'''simple docstring'''
snake_case__ = (prior_latents * self.clip_std) + self.clip_mean
return prior_latents
| 654 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
a__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ASTConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ASTForAudioClassification""",
"""ASTModel""",
"""ASTPreTrainedModel""",
]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ["""ASTFeatureExtractor"""]
if TYPE_CHECKING:
from .configuration_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
ASTConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
ASTForAudioClassification,
ASTModel,
ASTPreTrainedModel,
)
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor
else:
import sys
a__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 654 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
a__ = ["""gpt2"""]
a__ = """gpt2"""
if is_tf_available():
class _lowerCAmelCase ( tf.Module ):
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : int):
'''simple docstring'''
super().__init__()
snake_case__ = tokenizer
snake_case__ = AutoConfig.from_pretrained(UpperCamelCase__)
snake_case__ = TFGPTaLMHeadModel.from_config(UpperCamelCase__)
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name="""text"""),))
def __magic_name__ ( self : Tuple , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = self.tokenizer(UpperCamelCase__)
snake_case__ = tokenized["""input_ids"""].to_tensor()
snake_case__ = tf.cast(input_ids_dense > 0 , tf.intaa)
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
snake_case__ = self.model(input_ids=UpperCamelCase__ , attention_mask=UpperCamelCase__)["""logits"""]
return outputs
@require_tf
@require_keras_nlp
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
super().setUp()
snake_case__ = [GPTaTokenizer.from_pretrained(UpperCamelCase__) for checkpoint in (TOKENIZER_CHECKPOINTS)]
snake_case__ = [TFGPTaTokenizer.from_pretrained(UpperCamelCase__) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
snake_case__ = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
snake_case__ = list(zip(self.test_sentences , self.test_sentences[::-1]))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers):
for test_inputs in self.test_sentences:
snake_case__ = tokenizer([test_inputs] , return_tensors="""tf""")
snake_case__ = tf_tokenizer([test_inputs])
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
snake_case__ = python_outputs[key].numpy()
snake_case__ = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__ , tf.intaa) == tf_outputs_values))
@slow
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = tf.function(UpperCamelCase__)
for test_inputs in self.test_sentences:
snake_case__ = tf.constant(UpperCamelCase__)
snake_case__ = compiled_tokenizer(UpperCamelCase__)
snake_case__ = tf_tokenizer(UpperCamelCase__)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = ModelToSave(tokenizer=UpperCamelCase__)
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = model.serving(UpperCamelCase__) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
snake_case__ = Path(UpperCamelCase__) / """saved.model"""
tf.saved_model.save(UpperCamelCase__ , UpperCamelCase__ , signatures={"""serving_default""": model.serving})
snake_case__ = tf.saved_model.load(UpperCamelCase__)
snake_case__ = loaded_model.signatures["""serving_default"""](UpperCamelCase__)["""output_0"""]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output))
@slow
def __magic_name__ ( self : Tuple):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = tf_tokenizer(UpperCamelCase__) # Build model with some sample inputs
snake_case__ = tf_tokenizer.get_config()
snake_case__ = TFGPTaTokenizer.from_config(UpperCamelCase__)
snake_case__ = model_from_config(UpperCamelCase__)
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))
@slow
def __magic_name__ ( self : Dict):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
snake_case__ = 1_2_3_1_2_3
for max_length in [3, 5, 1_0_2_4]:
snake_case__ = tf.convert_to_tensor([self.test_sentences[0]])
snake_case__ = tf_tokenizer(UpperCamelCase__ , max_length=UpperCamelCase__)
snake_case__ = out["""input_ids"""].numpy().shape[1]
assert out_length == max_length
| 654 | 1 |
import argparse
from collections import defaultdict
import yaml
a__ = """docs/source/en/_toctree.yml"""
def _UpperCAmelCase ( a : str ):
snake_case__ = defaultdict(a )
for doc in model_doc:
counts[doc["local"]] += 1
snake_case__ = [key for key, value in counts.items() if value > 1]
snake_case__ = []
for duplicate_key in duplicates:
snake_case__ = list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key} )
if len(a ) > 1:
raise ValueError(
F'''{duplicate_key} is present several times in the documentation table of content at '''
"""`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
"""others.""" )
# Only add this once
new_doc.append({"""local""": duplicate_key, """title""": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1] )
# Sort
return sorted(a , key=lambda a : s["title"].lower() )
def _UpperCAmelCase ( a : Optional[int]=False ):
with open(a , encoding="""utf-8""" ) as f:
snake_case__ = yaml.safe_load(f.read() )
# Get to the API doc
snake_case__ = 0
while content[api_idx]["title"] != "API":
api_idx += 1
snake_case__ = content[api_idx]["""sections"""]
# Then to the model doc
snake_case__ = 0
while api_doc[model_idx]["title"] != "Models":
model_idx += 1
snake_case__ = api_doc[model_idx]["""sections"""]
snake_case__ = [(idx, section) for idx, section in enumerate(a ) if """sections""" in section]
snake_case__ = False
for idx, modality_doc in modalities_docs:
snake_case__ = modality_doc["""sections"""]
snake_case__ = clean_model_doc_toc(a )
if old_modality_doc != new_modality_doc:
snake_case__ = True
if overwrite:
snake_case__ = new_modality_doc
if diff:
if overwrite:
snake_case__ = model_doc
snake_case__ = api_doc
with open(a , """w""" , encoding="""utf-8""" ) as f:
f.write(yaml.dump(a , allow_unicode=a ) )
else:
raise ValueError(
"""The model doc part of the table of content is not properly sorted, run `make style` to fix this.""" )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
a__ = parser.parse_args()
check_model_doc(args.fix_and_overwrite)
| 654 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : int = (IPNDMScheduler,)
_lowercase : int = (('''num_inference_steps''', 50),)
def __magic_name__ ( self : Any , **UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = {"""num_train_timesteps""": 1_0_0_0}
config.update(**UpperCamelCase__)
return config
def __magic_name__ ( self : int , UpperCamelCase__ : Dict=0 , **UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = dict(self.forward_default_kwargs)
snake_case__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase__)
snake_case__ = self.dummy_sample
snake_case__ = 0.1 * sample
snake_case__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case__ = self.get_scheduler_config(**UpperCamelCase__)
snake_case__ = scheduler_class(**UpperCamelCase__)
scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residuals
snake_case__ = dummy_past_residuals[:]
if time_step is None:
snake_case__ = scheduler.timesteps[len(scheduler.timesteps) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase__)
snake_case__ = scheduler_class.from_pretrained(UpperCamelCase__)
new_scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residuals
snake_case__ = dummy_past_residuals[:]
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
pass
def __magic_name__ ( self : Tuple , UpperCamelCase__ : Union[str, Any]=0 , **UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = dict(self.forward_default_kwargs)
snake_case__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase__)
snake_case__ = self.dummy_sample
snake_case__ = 0.1 * sample
snake_case__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
snake_case__ = self.get_scheduler_config()
snake_case__ = scheduler_class(**UpperCamelCase__)
scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residuals (must be after setting timesteps)
snake_case__ = dummy_past_residuals[:]
if time_step is None:
snake_case__ = scheduler.timesteps[len(scheduler.timesteps) // 2]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(UpperCamelCase__)
snake_case__ = scheduler_class.from_pretrained(UpperCamelCase__)
# copy over dummy past residuals
new_scheduler.set_timesteps(UpperCamelCase__)
# copy over dummy past residual (must be after setting timesteps)
snake_case__ = dummy_past_residuals[:]
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = new_scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def __magic_name__ ( self : Union[str, Any] , **UpperCamelCase__ : Dict):
'''simple docstring'''
snake_case__ = self.scheduler_classes[0]
snake_case__ = self.get_scheduler_config(**UpperCamelCase__)
snake_case__ = scheduler_class(**UpperCamelCase__)
snake_case__ = 1_0
snake_case__ = self.dummy_model()
snake_case__ = self.dummy_sample_deter
scheduler.set_timesteps(UpperCamelCase__)
for i, t in enumerate(scheduler.timesteps):
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__).prev_sample
for i, t in enumerate(scheduler.timesteps):
snake_case__ = model(UpperCamelCase__ , UpperCamelCase__)
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__).prev_sample
return sample
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = dict(self.forward_default_kwargs)
snake_case__ = kwargs.pop("""num_inference_steps""" , UpperCamelCase__)
for scheduler_class in self.scheduler_classes:
snake_case__ = self.get_scheduler_config()
snake_case__ = scheduler_class(**UpperCamelCase__)
snake_case__ = self.dummy_sample
snake_case__ = 0.1 * sample
if num_inference_steps is not None and hasattr(UpperCamelCase__ , """set_timesteps"""):
scheduler.set_timesteps(UpperCamelCase__)
elif num_inference_steps is not None and not hasattr(UpperCamelCase__ , """set_timesteps"""):
snake_case__ = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
snake_case__ = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
snake_case__ = dummy_past_residuals[:]
snake_case__ = scheduler.timesteps[5]
snake_case__ = scheduler.timesteps[6]
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
snake_case__ = scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__).prev_sample
self.assertEqual(output_a.shape , sample.shape)
self.assertEqual(output_a.shape , output_a.shape)
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
for timesteps in [1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=UpperCamelCase__ , time_step=UpperCamelCase__)
def __magic_name__ ( self : Dict):
'''simple docstring'''
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=UpperCamelCase__ , time_step=UpperCamelCase__)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = self.full_loop()
snake_case__ = torch.mean(torch.abs(UpperCamelCase__))
assert abs(result_mean.item() - 2_5_4_0_5_2_9) < 1_0
| 654 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
a__ = logging.getLogger(__name__)
def _UpperCAmelCase ( a : Tuple , a : Optional[Any] ):
snake_case__ = np.argmax(a , axis=1 )
return np.sum(outputs == labels )
def _UpperCAmelCase ( a : List[str] ):
with open(a , encoding="""utf_8""" ) as f:
snake_case__ = csv.reader(a )
snake_case__ = []
next(a ) # skip the first line
for line in tqdm(a ):
output.append((""" """.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def _UpperCAmelCase ( a : List[str] , a : List[Any] , a : Tuple , a : List[str] , a : List[str] , a : Dict ):
snake_case__ = []
for dataset in encoded_datasets:
snake_case__ = len(a )
snake_case__ = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
snake_case__ = np.zeros((n_batch, 2) , dtype=np.intaa )
snake_case__ = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
snake_case__ = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(a ):
snake_case__ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
snake_case__ = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
snake_case__ = with_conta
snake_case__ = with_conta
snake_case__ = len(a ) - 1
snake_case__ = len(a ) - 1
snake_case__ = with_conta
snake_case__ = with_conta
snake_case__ = mc_label
snake_case__ = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(a ) for t in all_inputs ) )
return tensor_datasets
def _UpperCAmelCase ( ):
snake_case__ = argparse.ArgumentParser()
parser.add_argument("""--model_name""" , type=a , default="""openai-gpt""" , help="""pretrained model name""" )
parser.add_argument("""--do_train""" , action="""store_true""" , help="""Whether to run training.""" )
parser.add_argument("""--do_eval""" , action="""store_true""" , help="""Whether to run eval on the dev set.""" )
parser.add_argument(
"""--output_dir""" , default=a , type=a , required=a , help="""The output directory where the model predictions and checkpoints will be written.""" , )
parser.add_argument("""--train_dataset""" , type=a , default="""""" )
parser.add_argument("""--eval_dataset""" , type=a , default="""""" )
parser.add_argument("""--seed""" , type=a , default=42 )
parser.add_argument("""--num_train_epochs""" , type=a , default=3 )
parser.add_argument("""--train_batch_size""" , type=a , default=8 )
parser.add_argument("""--eval_batch_size""" , type=a , default=16 )
parser.add_argument("""--adam_epsilon""" , default=1e-8 , type=a , help="""Epsilon for Adam optimizer.""" )
parser.add_argument("""--max_grad_norm""" , type=a , default=1 )
parser.add_argument(
"""--max_steps""" , default=-1 , type=a , help=(
"""If > 0: set total number of training steps to perform. Override num_train_epochs."""
) , )
parser.add_argument(
"""--gradient_accumulation_steps""" , type=a , default=1 , help="""Number of updates steps to accumulate before performing a backward/update pass.""" , )
parser.add_argument("""--learning_rate""" , type=a , default=6.25e-5 )
parser.add_argument("""--warmup_steps""" , default=0 , type=a , help="""Linear warmup over warmup_steps.""" )
parser.add_argument("""--lr_schedule""" , type=a , default="""warmup_linear""" )
parser.add_argument("""--weight_decay""" , type=a , default=0.01 )
parser.add_argument("""--lm_coef""" , type=a , default=0.9 )
parser.add_argument("""--n_valid""" , type=a , default=374 )
parser.add_argument("""--server_ip""" , type=a , default="""""" , help="""Can be used for distant debugging.""" )
parser.add_argument("""--server_port""" , type=a , default="""""" , help="""Can be used for distant debugging.""" )
snake_case__ = parser.parse_args()
print(a )
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("""Waiting for debugger attach""" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=a )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
snake_case__ = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
snake_case__ = torch.cuda.device_count()
logger.info("""device: {}, n_gpu {}""".format(a , a ) )
if not args.do_train and not args.do_eval:
raise ValueError("""At least one of `do_train` or `do_eval` must be True.""" )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
snake_case__ = ["""_start_""", """_delimiter_""", """_classify_"""]
snake_case__ = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(a )
snake_case__ = tokenizer.convert_tokens_to_ids(a )
snake_case__ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(a ) )
model.to(a )
# Load and encode the datasets
def tokenize_and_encode(a : Optional[Any] ):
if isinstance(a , a ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(a ) )
elif isinstance(a , a ):
return obj
return [tokenize_and_encode(a ) for o in obj]
logger.info("""Encoding dataset...""" )
snake_case__ = load_rocstories_dataset(args.train_dataset )
snake_case__ = load_rocstories_dataset(args.eval_dataset )
snake_case__ = (train_dataset, eval_dataset)
snake_case__ = tokenize_and_encode(a )
# Compute the max input length for the Transformer
snake_case__ = model.config.n_positions // 2 - 2
snake_case__ = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
snake_case__ = min(a , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
snake_case__ = pre_process_datasets(a , a , a , *a )
snake_case__ , snake_case__ = tensor_datasets[0], tensor_datasets[1]
snake_case__ = TensorDataset(*a )
snake_case__ = RandomSampler(a )
snake_case__ = DataLoader(a , sampler=a , batch_size=args.train_batch_size )
snake_case__ = TensorDataset(*a )
snake_case__ = SequentialSampler(a )
snake_case__ = DataLoader(a , sampler=a , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
snake_case__ = args.max_steps
snake_case__ = args.max_steps // (len(a ) // args.gradient_accumulation_steps) + 1
else:
snake_case__ = len(a ) // args.gradient_accumulation_steps * args.num_train_epochs
snake_case__ = list(model.named_parameters() )
snake_case__ = ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
snake_case__ = [
{
"""params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
"""weight_decay""": args.weight_decay,
},
{"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], """weight_decay""": 0.0},
]
snake_case__ = AdamW(a , lr=args.learning_rate , eps=args.adam_epsilon )
snake_case__ = get_linear_schedule_with_warmup(
a , num_warmup_steps=args.warmup_steps , num_training_steps=a )
if args.do_train:
snake_case__ , snake_case__ , snake_case__ = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc="""Epoch""" ):
snake_case__ = 0
snake_case__ = 0
snake_case__ = tqdm(a , desc="""Training""" )
for step, batch in enumerate(a ):
snake_case__ = tuple(t.to(a ) for t in batch )
snake_case__ , snake_case__ , snake_case__ , snake_case__ = batch
snake_case__ = model(a , mc_token_ids=a , lm_labels=a , mc_labels=a )
snake_case__ = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
snake_case__ = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
snake_case__ = """Training loss: {:.2e} lr: {:.2e}""".format(a , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
snake_case__ = model.module if hasattr(a , """module""" ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
snake_case__ = os.path.join(args.output_dir , a )
snake_case__ = os.path.join(args.output_dir , a )
torch.save(model_to_save.state_dict() , a )
model_to_save.config.to_json_file(a )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
snake_case__ = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
snake_case__ = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(a )
if args.do_eval:
model.eval()
snake_case__ , snake_case__ = 0, 0
snake_case__ , snake_case__ = 0, 0
for batch in tqdm(a , desc="""Evaluating""" ):
snake_case__ = tuple(t.to(a ) for t in batch )
snake_case__ , snake_case__ , snake_case__ , snake_case__ = batch
with torch.no_grad():
snake_case__ , snake_case__ , snake_case__ , snake_case__ = model(
a , mc_token_ids=a , lm_labels=a , mc_labels=a )
snake_case__ = mc_logits.detach().cpu().numpy()
snake_case__ = mc_labels.to("""cpu""" ).numpy()
snake_case__ = accuracy(a , a )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
snake_case__ = eval_loss / nb_eval_steps
snake_case__ = eval_accuracy / nb_eval_examples
snake_case__ = tr_loss / nb_tr_steps if args.do_train else None
snake_case__ = {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}
snake_case__ = os.path.join(args.output_dir , """eval_results.txt""" )
with open(a , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key in sorted(result.keys() ):
logger.info(""" %s = %s""" , a , str(result[key] ) )
writer.write("""%s = %s\n""" % (key, str(result[key] )) )
if __name__ == "__main__":
main()
| 654 |
# 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 _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[Any] = (
'''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.'''
)
_lowercase : Dict = '''CIDAS/clipseg-rd64-refined'''
_lowercase : List[Any] = '''image_segmenter'''
_lowercase : Tuple = CLIPSegForImageSegmentation
_lowercase : str = ['''image''', '''text''']
_lowercase : Dict = ['''image''']
def __init__( self : Optional[int] , *UpperCamelCase__ : Optional[Any] , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
requires_backends(self , ["""vision"""])
super().__init__(*UpperCamelCase__ , **UpperCamelCase__)
def __magic_name__ ( self : str , UpperCamelCase__ : "Image" , UpperCamelCase__ : str):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=UpperCamelCase__ , return_tensors="""pt""")
def __magic_name__ ( self : Any , UpperCamelCase__ : Optional[Any]):
'''simple docstring'''
with torch.no_grad():
snake_case__ = self.model(**UpperCamelCase__).logits
return logits
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = outputs.cpu().detach().numpy()
snake_case__ = 0
snake_case__ = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta))
| 654 | 1 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Any , UpperCamelCase__ : Optional[Any] , ):
'''simple docstring'''
snake_case__ = parent
snake_case__ = 1_3
snake_case__ = 7
snake_case__ = True
snake_case__ = True
snake_case__ = False
snake_case__ = True
snake_case__ = 9_9
snake_case__ = 3_2
snake_case__ = 2
snake_case__ = 4
snake_case__ = 3_7
snake_case__ = """gelu"""
snake_case__ = 0.1
snake_case__ = 0.1
snake_case__ = 5_1_2
snake_case__ = 1_6
snake_case__ = 2
snake_case__ = 0.02
snake_case__ = 3
snake_case__ = 4
snake_case__ = None
def __magic_name__ ( self : Tuple):
'''simple docstring'''
snake_case__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
snake_case__ = None
if self.use_input_mask:
snake_case__ = random_attention_mask([self.batch_size, self.seq_length])
snake_case__ = None
snake_case__ = None
snake_case__ = None
if self.use_labels:
snake_case__ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
snake_case__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
snake_case__ = ids_tensor([self.batch_size] , self.num_choices)
snake_case__ = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__ ( self : Optional[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
snake_case__ = TFDistilBertModel(config=UpperCamelCase__)
snake_case__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
snake_case__ = model(UpperCamelCase__)
snake_case__ = [input_ids, input_mask]
snake_case__ = model(UpperCamelCase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def __magic_name__ ( self : List[Any] , UpperCamelCase__ : int , UpperCamelCase__ : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[Any]):
'''simple docstring'''
snake_case__ = TFDistilBertForMaskedLM(config=UpperCamelCase__)
snake_case__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
snake_case__ = model(UpperCamelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def __magic_name__ ( self : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : str , UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = TFDistilBertForQuestionAnswering(config=UpperCamelCase__)
snake_case__ = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
}
snake_case__ = model(UpperCamelCase__)
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 __magic_name__ ( self : str , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict):
'''simple docstring'''
snake_case__ = self.num_labels
snake_case__ = TFDistilBertForSequenceClassification(UpperCamelCase__)
snake_case__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
snake_case__ = model(UpperCamelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def __magic_name__ ( self : Dict , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Dict , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple):
'''simple docstring'''
snake_case__ = self.num_choices
snake_case__ = TFDistilBertForMultipleChoice(UpperCamelCase__)
snake_case__ = tf.tile(tf.expand_dims(UpperCamelCase__ , 1) , (1, self.num_choices, 1))
snake_case__ = tf.tile(tf.expand_dims(UpperCamelCase__ , 1) , (1, self.num_choices, 1))
snake_case__ = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
}
snake_case__ = model(UpperCamelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def __magic_name__ ( self : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any):
'''simple docstring'''
snake_case__ = self.num_labels
snake_case__ = TFDistilBertForTokenClassification(UpperCamelCase__)
snake_case__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
snake_case__ = model(UpperCamelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = self.prepare_config_and_inputs()
((snake_case__) , (snake_case__) , (snake_case__) , (snake_case__) , (snake_case__) , (snake_case__)) = config_and_inputs
snake_case__ = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : Tuple = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
_lowercase : Tuple = (
{
'''feature-extraction''': TFDistilBertModel,
'''fill-mask''': TFDistilBertForMaskedLM,
'''question-answering''': TFDistilBertForQuestionAnswering,
'''text-classification''': TFDistilBertForSequenceClassification,
'''token-classification''': TFDistilBertForTokenClassification,
'''zero-shot''': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowercase : Optional[Any] = False
_lowercase : Tuple = False
def __magic_name__ ( self : Union[str, Any]):
'''simple docstring'''
snake_case__ = TFDistilBertModelTester(self)
snake_case__ = ConfigTester(self , config_class=UpperCamelCase__ , dim=3_7)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__)
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__)
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__)
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__)
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__)
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__)
@slow
def __magic_name__ ( self : Tuple):
'''simple docstring'''
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]):
snake_case__ = TFDistilBertModel.from_pretrained(UpperCamelCase__)
self.assertIsNotNone(UpperCamelCase__)
@require_tf
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@slow
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
snake_case__ = TFDistilBertModel.from_pretrained("""distilbert-base-uncased""")
snake_case__ = tf.constant([[0, 1, 2, 3, 4, 5]])
snake_case__ = model(UpperCamelCase__)[0]
snake_case__ = [1, 6, 7_6_8]
self.assertEqual(output.shape , UpperCamelCase__)
snake_case__ = tf.constant(
[
[
[0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
[0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
[0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
]
])
tf.debugging.assert_near(output[:, :3, :3] , UpperCamelCase__ , atol=1E-4)
| 654 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : List[Any]=3 , UpperCamelCase__ : Dict=1_8 , UpperCamelCase__ : Any=3_0 , UpperCamelCase__ : List[Any]=4_0_0 , UpperCamelCase__ : Optional[int]=True , UpperCamelCase__ : Any=None , UpperCamelCase__ : Optional[int]=True , ):
'''simple docstring'''
snake_case__ = size if size is not None else {"""height""": 1_8, """width""": 1_8}
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = num_channels
snake_case__ = image_size
snake_case__ = min_resolution
snake_case__ = max_resolution
snake_case__ = do_resize
snake_case__ = size
snake_case__ = apply_ocr
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class _lowerCAmelCase ( lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : str = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = LayoutLMvaImageProcessingTester(self)
@property
def __magic_name__ ( self : Tuple):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(UpperCamelCase__ , """do_resize"""))
self.assertTrue(hasattr(UpperCamelCase__ , """size"""))
self.assertTrue(hasattr(UpperCamelCase__ , """apply_ocr"""))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {"""height""": 1_8, """width""": 1_8})
snake_case__ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2)
self.assertEqual(image_processor.size , {"""height""": 4_2, """width""": 4_2})
def __magic_name__ ( self : List[str]):
'''simple docstring'''
pass
def __magic_name__ ( self : List[str]):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , Image.Image)
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors="""pt""")
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
self.assertIsInstance(encoding.words , UpperCamelCase__)
self.assertIsInstance(encoding.boxes , UpperCamelCase__)
# Test batched
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , numpify=UpperCamelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , np.ndarray)
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __magic_name__ ( self : Dict):
'''simple docstring'''
snake_case__ = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
snake_case__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase__ , torchify=UpperCamelCase__)
for image in image_inputs:
self.assertIsInstance(UpperCamelCase__ , torch.Tensor)
# Test not batched input
snake_case__ = image_processing(image_inputs[0] , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""").pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = LayoutLMvaImageProcessor()
from datasets import load_dataset
snake_case__ = load_dataset("""hf-internal-testing/fixtures_docvqa""" , split="""test""")
snake_case__ = Image.open(ds[0]["""file"""]).convert("""RGB""")
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""")
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4))
self.assertEqual(len(encoding.words) , len(encoding.boxes))
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
snake_case__ = [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]] # noqa: E231
snake_case__ = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , UpperCamelCase__)
self.assertListEqual(encoding.boxes , UpperCamelCase__)
# with apply_OCR = False
snake_case__ = LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__)
snake_case__ = image_processing(UpperCamelCase__ , return_tensors="""pt""")
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4))
| 654 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a__ = logging.get_logger(__name__)
def _UpperCAmelCase ( a : List[str] , a : Any=False ):
snake_case__ = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''') )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """deit.embeddings.cls_token"""),
("""dist_token""", """deit.embeddings.distillation_token"""),
("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """deit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "deit" from all keys that start with "deit"
snake_case__ = [(pair[0], pair[1][4:]) if pair[1].startswith("""deit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification heads
rename_keys.extend(
[
("""norm.weight""", """deit.layernorm.weight"""),
("""norm.bias""", """deit.layernorm.bias"""),
("""head.weight""", """cls_classifier.weight"""),
("""head.bias""", """cls_classifier.bias"""),
("""head_dist.weight""", """distillation_classifier.weight"""),
("""head_dist.bias""", """distillation_classifier.bias"""),
] )
return rename_keys
def _UpperCAmelCase ( a : int , a : List[Any] , a : Union[str, Any]=False ):
for i in range(config.num_hidden_layers ):
if base_model:
snake_case__ = """"""
else:
snake_case__ = """deit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
snake_case__ = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ = in_proj_weight[
: config.hidden_size, :
]
snake_case__ = in_proj_bias[: config.hidden_size]
snake_case__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ = in_proj_bias[-config.hidden_size :]
def _UpperCAmelCase ( a : Dict , a : Union[str, Any] , a : int ):
snake_case__ = dct.pop(a )
snake_case__ = val
def _UpperCAmelCase ( ):
snake_case__ = """http://images.cocodataset.org/val2017/000000039769.jpg"""
snake_case__ = Image.open(requests.get(a , stream=a ).raw )
return im
@torch.no_grad()
def _UpperCAmelCase ( a : List[str] , a : Tuple ):
snake_case__ = DeiTConfig()
# all deit models have fine-tuned heads
snake_case__ = False
# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
snake_case__ = 1000
snake_case__ = """huggingface/label-files"""
snake_case__ = """imagenet-1k-id2label.json"""
snake_case__ = json.load(open(hf_hub_download(a , a , repo_type="""dataset""" ) , """r""" ) )
snake_case__ = {int(a ): v for k, v in idalabel.items()}
snake_case__ = idalabel
snake_case__ = {v: k for k, v in idalabel.items()}
snake_case__ = int(deit_name[-6:-4] )
snake_case__ = int(deit_name[-3:] )
# size of the architecture
if deit_name[9:].startswith("""tiny""" ):
snake_case__ = 192
snake_case__ = 768
snake_case__ = 12
snake_case__ = 3
elif deit_name[9:].startswith("""small""" ):
snake_case__ = 384
snake_case__ = 1536
snake_case__ = 12
snake_case__ = 6
if deit_name[9:].startswith("""base""" ):
pass
elif deit_name[4:].startswith("""large""" ):
snake_case__ = 1024
snake_case__ = 4096
snake_case__ = 24
snake_case__ = 16
# load original model from timm
snake_case__ = timm.create_model(a , pretrained=a )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
snake_case__ = timm_model.state_dict()
snake_case__ = create_rename_keys(a , a )
for src, dest in rename_keys:
rename_key(a , a , a )
read_in_q_k_v(a , a , a )
# load HuggingFace model
snake_case__ = DeiTForImageClassificationWithTeacher(a ).eval()
model.load_state_dict(a )
# Check outputs on an image, prepared by DeiTImageProcessor
snake_case__ = int(
(256 / 224) * config.image_size ) # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
snake_case__ = DeiTImageProcessor(size=a , crop_size=config.image_size )
snake_case__ = image_processor(images=prepare_img() , return_tensors="""pt""" )
snake_case__ = encoding["""pixel_values"""]
snake_case__ = model(a )
snake_case__ = timm_model(a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(a , outputs.logits , atol=1e-3 )
Path(a ).mkdir(exist_ok=a )
print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(a )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(a )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--deit_name""",
default="""vit_deit_base_distilled_patch16_224""",
type=str,
help="""Name of the DeiT timm model 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_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
| 654 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
def __init__( self : int , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = params
snake_case__ = np.array(UpperCamelCase__)
snake_case__ = np.array([len(UpperCamelCase__) for t in data])
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self : Dict , UpperCamelCase__ : Any):
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self : Union[str, Any]):
'''simple docstring'''
return len(self.lengths)
def __magic_name__ ( self : str):
'''simple docstring'''
assert len(self.token_ids) == len(self.lengths)
assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.params.max_model_input_size
snake_case__ = self.lengths > max_len
logger.info(F'''Splitting {sum(UpperCamelCase__)} too long sequences.''')
def divide_chunks(UpperCamelCase__ : str , UpperCamelCase__ : Tuple):
return [l[i : i + n] for i in range(0 , len(UpperCamelCase__) , UpperCamelCase__)]
snake_case__ = []
snake_case__ = []
if self.params.mlm:
snake_case__ , snake_case__ = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
snake_case__ , snake_case__ = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_)
new_lengths.append(len_)
else:
snake_case__ = []
for sub_s in divide_chunks(seq_ , max_len - 2):
if sub_s[0] != cls_id:
snake_case__ = np.insert(UpperCamelCase__ , 0 , UpperCamelCase__)
if sub_s[-1] != sep_id:
snake_case__ = np.insert(UpperCamelCase__ , len(UpperCamelCase__) , UpperCamelCase__)
assert len(UpperCamelCase__) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(UpperCamelCase__)
new_tok_ids.extend(UpperCamelCase__)
new_lengths.extend([len(UpperCamelCase__) for l in sub_seqs])
snake_case__ = np.array(UpperCamelCase__)
snake_case__ = np.array(UpperCamelCase__)
def __magic_name__ ( self : Any):
'''simple docstring'''
snake_case__ = len(self)
snake_case__ = self.lengths > 1_1
snake_case__ = self.token_ids[indices]
snake_case__ = self.lengths[indices]
snake_case__ = len(self)
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''')
def __magic_name__ ( self : List[str]):
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
snake_case__ = self.params.special_tok_ids["""unk_token"""]
snake_case__ = len(self)
snake_case__ = np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
snake_case__ = (unk_occs / self.lengths) < 0.5
snake_case__ = self.token_ids[indices]
snake_case__ = self.lengths[indices]
snake_case__ = len(self)
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''')
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
if not self.params.is_master:
return
logger.info(F'''{len(self)} sequences''')
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def __magic_name__ ( self : int , UpperCamelCase__ : Optional[int]):
'''simple docstring'''
snake_case__ = [t[0] for t in batch]
snake_case__ = [t[1] for t in batch]
assert len(UpperCamelCase__) == len(UpperCamelCase__)
# Max for paddings
snake_case__ = max(UpperCamelCase__)
# Pad token ids
if self.params.mlm:
snake_case__ = self.params.special_tok_ids["""pad_token"""]
else:
snake_case__ = self.params.special_tok_ids["""unk_token"""]
snake_case__ = [list(t.astype(UpperCamelCase__)) + [pad_idx] * (max_seq_len_ - len(UpperCamelCase__)) for t in token_ids]
assert len(tk_) == len(UpperCamelCase__)
assert all(len(UpperCamelCase__) == max_seq_len_ for t in tk_)
snake_case__ = torch.tensor(tk_) # (bs, max_seq_len_)
snake_case__ = torch.tensor(UpperCamelCase__) # (bs)
return tk_t, lg_t
| 654 | 1 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
a__ = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
a__ = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
a__ = re.compile(r"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a__ = re.compile(r"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
a__ = re.compile(r"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a__ = [
("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
(
"""zero-shot-object-detection""",
"""MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
"""AutoModelForZeroShotObjectDetection""",
),
("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
(
"""table-question-answering""",
"""MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForTableQuestionAnswering""",
),
("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
(
"""next-sentence-prediction""",
"""MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
"""AutoModelForNextSentencePrediction""",
),
(
"""audio-frame-classification""",
"""MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
"""AutoModelForAudioFrameClassification""",
),
("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
(
"""document-question-answering""",
"""MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForDocumentQuestionAnswering""",
),
(
"""visual-question-answering""",
"""MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForVisualQuestionAnswering""",
),
("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
(
"""zero-shot-image-classification""",
"""MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
"""AutoModelForZeroShotImageClassification""",
),
("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]
def _UpperCAmelCase ( a : Tuple ):
snake_case__ = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , a )
return [m.group(0 ) for m in matches]
def _UpperCAmelCase ( ):
snake_case__ = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
snake_case__ = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
snake_case__ = collections.defaultdict(a )
snake_case__ = collections.defaultdict(a )
snake_case__ = collections.defaultdict(a )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(a ):
snake_case__ = None
if _re_tf_models.match(a ) is not None:
snake_case__ = tf_models
snake_case__ = _re_tf_models.match(a ).groups()[0]
elif _re_flax_models.match(a ) is not None:
snake_case__ = flax_models
snake_case__ = _re_flax_models.match(a ).groups()[0]
elif _re_pt_models.match(a ) is not None:
snake_case__ = pt_models
snake_case__ = _re_pt_models.match(a ).groups()[0]
if lookup_dict is not None:
while len(a ) > 0:
if attr_name in model_prefix_to_model_type:
snake_case__ = True
break
# Try again after removing the last word in the name
snake_case__ = """""".join(camel_case_split(a )[:-1] )
snake_case__ = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
snake_case__ = list(a )
all_models.sort()
snake_case__ = {"""model_type""": all_models}
snake_case__ = [pt_models[t] for t in all_models]
snake_case__ = [tf_models[t] for t in all_models]
snake_case__ = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
snake_case__ = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
snake_case__ = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
snake_case__ = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
snake_case__ = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
snake_case__ = """AutoTokenizer"""
snake_case__ = [processors[t] for t in all_models]
return pd.DataFrame(a )
def _UpperCAmelCase ( a : List[str] ):
snake_case__ = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
snake_case__ = [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
snake_case__ = [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
# Loop through all three frameworks
for module, cls, mapping in zip(a , a , a ):
# The type of pipeline may not exist in this framework
if not hasattr(a , a ):
continue
# First extract all model_names
snake_case__ = []
for name in getattr(a , a ).values():
if isinstance(a , a ):
model_names.append(a )
else:
model_names.extend(list(a ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def _UpperCAmelCase ( a : Union[str, Any] , a : Optional[int] ):
snake_case__ = get_frameworks_table()
snake_case__ = Dataset.from_pandas(a )
snake_case__ = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=a )
snake_case__ = Dataset.from_json(a )
snake_case__ = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(a ) )
}
snake_case__ = update_pipeline_and_auto_class_table(a )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
snake_case__ = sorted(table.keys() )
snake_case__ = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
snake_case__ = Dataset.from_pandas(a )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(a , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(a , """pipeline_tags.json""" ) )
if commit_sha is not None:
snake_case__ = (
F'''Update with commit {commit_sha}\n\nSee: '''
F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
)
else:
snake_case__ = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=a , repo_type="""dataset""" , token=a , commit_message=a , )
def _UpperCAmelCase ( ):
snake_case__ = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
snake_case__ = transformers_module.pipelines.SUPPORTED_TASKS
snake_case__ = []
for key in pipeline_tasks:
if key not in in_table:
snake_case__ = pipeline_tasks[key]["""pt"""]
if isinstance(a , (list, tuple) ):
snake_case__ = model[0]
snake_case__ = model.__name__
if model not in in_table.values():
missing.append(a )
if len(a ) > 0:
snake_case__ = """, """.join(a )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F'''`utils/update_metadata.py`: {msg}. Please add them!''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
a__ = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 654 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _UpperCAmelCase ( a : str ):
if "model" in orig_key:
snake_case__ = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
snake_case__ = orig_key.replace("""norm1""" , """attention.output.LayerNorm""" )
if "norm2" in orig_key:
snake_case__ = orig_key.replace("""norm2""" , """output.LayerNorm""" )
if "norm" in orig_key:
snake_case__ = orig_key.replace("""norm""" , """LayerNorm""" )
if "transformer" in orig_key:
snake_case__ = orig_key.split(""".""" )[0].split("""_""" )[-1]
snake_case__ = orig_key.replace(F'''transformer_{layer_num}''' , F'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
snake_case__ = orig_key.replace("""mha.attn""" , """attention.self""" )
if "mha" in orig_key:
snake_case__ = orig_key.replace("""mha""" , """attention""" )
if "W_q" in orig_key:
snake_case__ = orig_key.replace("""W_q""" , """self.query""" )
if "W_k" in orig_key:
snake_case__ = orig_key.replace("""W_k""" , """self.key""" )
if "W_v" in orig_key:
snake_case__ = orig_key.replace("""W_v""" , """self.value""" )
if "ff1" in orig_key:
snake_case__ = orig_key.replace("""ff1""" , """intermediate.dense""" )
if "ff2" in orig_key:
snake_case__ = orig_key.replace("""ff2""" , """output.dense""" )
if "ff" in orig_key:
snake_case__ = orig_key.replace("""ff""" , """output.dense""" )
if "mlm_class" in orig_key:
snake_case__ = orig_key.replace("""mlm.mlm_class""" , """cls.predictions.decoder""" )
if "mlm" in orig_key:
snake_case__ = orig_key.replace("""mlm""" , """cls.predictions.transform""" )
if "cls" not in orig_key:
snake_case__ = """yoso.""" + orig_key
return orig_key
def _UpperCAmelCase ( a : Tuple , a : Dict ):
for key in orig_state_dict.copy().keys():
snake_case__ = orig_state_dict.pop(a )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
snake_case__ = val
snake_case__ = orig_state_dict["""cls.predictions.decoder.bias"""]
snake_case__ = torch.arange(a ).expand((1, -1) ) + 2
return orig_state_dict
def _UpperCAmelCase ( a : int , a : List[Any] , a : List[Any] ):
snake_case__ = torch.load(a , map_location="""cpu""" )["""model_state_dict"""]
snake_case__ = YosoConfig.from_json_file(a )
snake_case__ = YosoForMaskedLM(a )
snake_case__ = convert_checkpoint_helper(config.max_position_embeddings , a )
print(model.load_state_dict(a ) )
model.eval()
model.save_pretrained(a )
print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
a__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--pytorch_model_path""", default=None, type=str, required=True, help="""Path to YOSO pytorch checkpoint."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The json file for YOSO model config.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a__ = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 654 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Union[List[PIL.Image.Image], np.ndarray]
_lowercase : Optional[List[bool]]
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 654 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = ''''''
_lowercase : str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
_lowercase : str = None # compression type in fsspec. ex: "gzip"
_lowercase : str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : List[Any] , UpperCamelCase__ : str = "" , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[dict] = None , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
super().__init__(self , **UpperCamelCase__)
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
snake_case__ = fsspec.open(
UpperCamelCase__ , mode="""rb""" , protocol=UpperCamelCase__ , compression=self.compression , client_kwargs={
"""requote_redirect_url""": False, # see https://github.com/huggingface/datasets/pull/5459
"""trust_env""": True, # Enable reading proxy env variables.
**(target_options or {}).pop("""client_kwargs""" , {}), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
snake_case__ = os.path.basename(self.file.path.split("""::""")[0])
snake_case__ = (
self.compressed_name[: self.compressed_name.rindex(""".""")]
if """.""" in self.compressed_name
else self.compressed_name
)
snake_case__ = None
@classmethod
def __magic_name__ ( cls : Union[str, Any] , UpperCamelCase__ : List[Any]):
'''simple docstring'''
return super()._strip_protocol(UpperCamelCase__).lstrip("""/""")
def __magic_name__ ( self : Dict):
'''simple docstring'''
if self.dir_cache is None:
snake_case__ = {**self.file.fs.info(self.file.path), """name""": self.uncompressed_name}
snake_case__ = {f["""name"""]: f}
def __magic_name__ ( self : Optional[int] , UpperCamelCase__ : str):
'''simple docstring'''
return self.file.open().read()
def __magic_name__ ( self : int , UpperCamelCase__ : str , UpperCamelCase__ : str = "rb" , UpperCamelCase__ : Any=None , UpperCamelCase__ : Union[str, Any]=True , UpperCamelCase__ : Optional[int]=None , **UpperCamelCase__ : Optional[Any] , ):
'''simple docstring'''
snake_case__ = self._strip_protocol(UpperCamelCase__)
if mode != "rb":
raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''')
return self.file.open()
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = '''bz2'''
_lowercase : Dict = '''bz2'''
_lowercase : Optional[int] = '''.bz2'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Dict = '''gzip'''
_lowercase : List[str] = '''gzip'''
_lowercase : Any = '''.gz'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : str = '''lz4'''
_lowercase : List[Any] = '''lz4'''
_lowercase : Dict = '''.lz4'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = '''xz'''
_lowercase : Union[str, Any] = '''xz'''
_lowercase : Optional[int] = '''.xz'''
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Optional[int] = '''zstd'''
_lowercase : Tuple = '''zstd'''
_lowercase : Union[str, Any] = '''.zst'''
def __init__( self : str , UpperCamelCase__ : str , UpperCamelCase__ : str = "rb" , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : Optional[dict] = None , UpperCamelCase__ : int = DEFAULT_BLOCK_SIZE , **UpperCamelCase__ : int , ):
'''simple docstring'''
super().__init__(
fo=UpperCamelCase__ , mode=UpperCamelCase__ , target_protocol=UpperCamelCase__ , target_options=UpperCamelCase__ , block_size=UpperCamelCase__ , **UpperCamelCase__ , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
snake_case__ = self.file.__enter__
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Tuple , UpperCamelCase__ : str):
'''simple docstring'''
snake_case__ = file_
def __enter__( self : List[str]):
'''simple docstring'''
self._file.__enter__()
return self
def __exit__( self : Dict , *UpperCamelCase__ : str , **UpperCamelCase__ : Optional[Any]):
'''simple docstring'''
self._file.__exit__(*UpperCamelCase__ , **UpperCamelCase__)
def __iter__( self : Any):
'''simple docstring'''
return iter(self._file)
def __magic_name__ ( self : List[str]):
'''simple docstring'''
return next(self._file)
def __getattr__( self : Any , UpperCamelCase__ : int):
'''simple docstring'''
return getattr(self._file , UpperCamelCase__)
def fixed_enter(*UpperCamelCase__ : int , **UpperCamelCase__ : int):
return WrappedFile(_enter(*UpperCamelCase__ , **UpperCamelCase__))
snake_case__ = fixed_enter
| 654 | 1 |
def _UpperCAmelCase ( a : list[int] ):
snake_case__ = []
if len(a ) == 1:
return [nums.copy()]
for _ in range(len(a ) ):
snake_case__ = nums.pop(0 )
snake_case__ = permute(a )
for perm in permutations:
perm.append(a )
result.extend(a )
nums.append(a )
return result
def _UpperCAmelCase ( a : Union[str, Any] ):
def backtrack(a : List[Any] ):
if start == len(a ) - 1:
output.append(nums[:] )
else:
for i in range(a , len(a ) ):
snake_case__ , snake_case__ = nums[i], nums[start]
backtrack(start + 1 )
snake_case__ , snake_case__ = nums[i], nums[start] # backtrack
snake_case__ = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
a__ = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 654 |
def _UpperCAmelCase ( a : int ):
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 | 1 |
def _UpperCAmelCase ( a : Dict ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
snake_case__ = len(a )
snake_case__ = max(a )
snake_case__ = min(a )
# create the counting array
snake_case__ = coll_max + 1 - coll_min
snake_case__ = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , a ):
snake_case__ = counting_arr[i] + counting_arr[i - 1]
# create the output collection
snake_case__ = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , a ) ):
snake_case__ = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def _UpperCAmelCase ( a : Tuple ):
return "".join([chr(a ) for i in counting_sort([ord(a ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
a__ = input("""Enter numbers separated by a comma:\n""").strip()
a__ = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 654 |
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = size
snake_case__ = [0] * size
snake_case__ = [0] * size
@staticmethod
def __magic_name__ ( UpperCamelCase__ : int):
'''simple docstring'''
return index | (index + 1)
@staticmethod
def __magic_name__ ( UpperCamelCase__ : int):
'''simple docstring'''
return (index & (index + 1)) - 1
def __magic_name__ ( self : Any , UpperCamelCase__ : int , UpperCamelCase__ : int):
'''simple docstring'''
snake_case__ = value
while index < self.size:
snake_case__ = self.get_prev(UpperCamelCase__) + 1
if current_left_border == index:
snake_case__ = value
else:
snake_case__ = max(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
snake_case__ = self.get_next(UpperCamelCase__)
def __magic_name__ ( self : int , UpperCamelCase__ : int , UpperCamelCase__ : int):
'''simple docstring'''
right -= 1 # Because of right is exclusive
snake_case__ = 0
while left <= right:
snake_case__ = self.get_prev(UpperCamelCase__)
if left <= current_left:
snake_case__ = max(UpperCamelCase__ , self.tree[right])
snake_case__ = current_left
else:
snake_case__ = max(UpperCamelCase__ , self.arr[right])
right -= 1
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 654 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class _lowerCAmelCase ( lowercase_ ):
"""simple docstring"""
_lowercase : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 654 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class _lowerCAmelCase :
"""simple docstring"""
_lowercase : List[str] = PegasusConfig
_lowercase : Union[str, Any] = {}
_lowercase : Tuple = '''gelu'''
def __init__( self : List[str] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int]=1_3 , UpperCamelCase__ : Any=7 , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Optional[int]=False , UpperCamelCase__ : int=9_9 , UpperCamelCase__ : Dict=3_2 , UpperCamelCase__ : str=2 , UpperCamelCase__ : int=4 , UpperCamelCase__ : Tuple=3_7 , UpperCamelCase__ : Union[str, Any]=0.1 , UpperCamelCase__ : Any=0.1 , UpperCamelCase__ : str=4_0 , UpperCamelCase__ : Optional[int]=2 , UpperCamelCase__ : Optional[Any]=1 , UpperCamelCase__ : Dict=0 , ):
'''simple docstring'''
snake_case__ = parent
snake_case__ = batch_size
snake_case__ = seq_length
snake_case__ = is_training
snake_case__ = use_labels
snake_case__ = vocab_size
snake_case__ = hidden_size
snake_case__ = num_hidden_layers
snake_case__ = num_attention_heads
snake_case__ = intermediate_size
snake_case__ = hidden_dropout_prob
snake_case__ = attention_probs_dropout_prob
snake_case__ = max_position_embeddings
snake_case__ = eos_token_id
snake_case__ = pad_token_id
snake_case__ = bos_token_id
def __magic_name__ ( self : Optional[Any]):
'''simple docstring'''
snake_case__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size)
snake_case__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) , 1)
snake_case__ = tf.concat([input_ids, eos_tensor] , axis=1)
snake_case__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
snake_case__ = self.config_cls(
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_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
snake_case__ = prepare_pegasus_inputs_dict(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__)
return config, inputs_dict
def __magic_name__ ( self : Any , UpperCamelCase__ : Tuple , UpperCamelCase__ : Union[str, Any]):
'''simple docstring'''
snake_case__ = TFPegasusModel(config=UpperCamelCase__).get_decoder()
snake_case__ = inputs_dict["""input_ids"""]
snake_case__ = input_ids[:1, :]
snake_case__ = inputs_dict["""attention_mask"""][:1, :]
snake_case__ = inputs_dict["""head_mask"""]
snake_case__ = 1
# first forward pass
snake_case__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , head_mask=UpperCamelCase__ , use_cache=UpperCamelCase__)
snake_case__ , snake_case__ = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case__ = ids_tensor((self.batch_size, 3) , config.vocab_size)
snake_case__ = tf.cast(ids_tensor((self.batch_size, 3) , 2) , tf.inta)
# append to next input_ids and
snake_case__ = tf.concat([input_ids, next_tokens] , axis=-1)
snake_case__ = tf.concat([attention_mask, next_attn_mask] , axis=-1)
snake_case__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__)[0]
snake_case__ = model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__)[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1])
# select random slice
snake_case__ = int(ids_tensor((1,) , output_from_past.shape[-1]))
snake_case__ = output_from_no_past[:, -3:, random_slice_idx]
snake_case__ = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCamelCase__ , UpperCamelCase__ , rtol=1E-3)
def _UpperCAmelCase ( a : str , a : Union[str, Any] , a : List[str] , a : str=None , a : int=None , a : int=None , a : int=None , a : Optional[int]=None , ):
if attention_mask is None:
snake_case__ = tf.cast(tf.math.not_equal(a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case__ = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
snake_case__ = tf.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": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
"""simple docstring"""
_lowercase : int = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_lowercase : List[Any] = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_lowercase : List[Any] = (
{
'''conversational''': TFPegasusForConditionalGeneration,
'''feature-extraction''': TFPegasusModel,
'''summarization''': TFPegasusForConditionalGeneration,
'''text2text-generation''': TFPegasusForConditionalGeneration,
'''translation''': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowercase : Optional[int] = True
_lowercase : Dict = False
_lowercase : Any = False
def __magic_name__ ( self : str):
'''simple docstring'''
snake_case__ = TFPegasusModelTester(self)
snake_case__ = ConfigTester(self , config_class=UpperCamelCase__)
def __magic_name__ ( self : List[Any]):
'''simple docstring'''
self.config_tester.run_common_tests()
def __magic_name__ ( self : Optional[int]):
'''simple docstring'''
snake_case__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__)
@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_lowercase : List[str] = [
''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
]
_lowercase : str = [
'''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
''' reduce the risk of wildfires.''',
'''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_lowercase : int = '''google/pegasus-xsum'''
@cached_property
def __magic_name__ ( self : Dict):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.model_name)
@cached_property
def __magic_name__ ( self : int):
'''simple docstring'''
snake_case__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
return model
def __magic_name__ ( self : Dict , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
snake_case__ = self.translate_src_text(**UpperCamelCase__)
assert self.expected_text == generated_words
def __magic_name__ ( self : str , **UpperCamelCase__ : List[Any]):
'''simple docstring'''
snake_case__ = self.tokenizer(self.src_text , **UpperCamelCase__ , padding=UpperCamelCase__ , return_tensors="""tf""")
snake_case__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=UpperCamelCase__ , )
snake_case__ = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=UpperCamelCase__)
return generated_words
@slow
def __magic_name__ ( self : List[str]):
'''simple docstring'''
self._assert_generated_batch_equal_expected()
| 654 | 1 |
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