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"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase ( A_="ro" , A_="en" , A_="wmt16" , A_=None )-> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("run pip install datasets" )
a : List[Any] = F'''{src_lang}-{tgt_lang}'''
print(F'''Converting {dataset}-{pair}''' )
a : Tuple = datasets.load_dataset(A_ , A_ )
if save_dir is None:
a : Dict = F'''{dataset}-{pair}'''
a : str = Path(A_ )
save_dir.mkdir(exist_ok=A_ )
for split in ds.keys():
print(F'''Splitting {split} with {ds[split].num_rows} records''' )
# to save to val.source, val.target like summary datasets
a : Any = "val" if split == "validation" else split
a : Tuple = save_dir.joinpath(F'''{fn}.source''' )
a : Any = save_dir.joinpath(F'''{fn}.target''' )
a : Tuple = src_path.open("w+" )
a : List[Any] = tgt_path.open("w+" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
a : Any = x["translation"]
src_fp.write(ex[src_lang] + "\n" )
tgt_fp.write(ex[tgt_lang] + "\n" )
print(F'''Saved {dataset} dataset to {save_dir}''' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 40
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
elif "subsample" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ ,snake_case_ = emb.weight.shape
snake_case_ = nn.Linear(__UpperCAmelCase, __UpperCAmelCase, bias=__UpperCAmelCase )
snake_case_ = emb.weight.data
return lin_layer
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = torch.load(__UpperCAmelCase, map_location='''cpu''' )
snake_case_ = mam_aaa['''args''']
snake_case_ = mam_aaa['''model''']
snake_case_ = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__UpperCAmelCase )
rename_keys(__UpperCAmelCase )
snake_case_ = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case_ = args.share_decoder_input_output_embed
snake_case_ = [int(__UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case_ = SpeechaTextConfig(
vocab_size=__UpperCAmelCase, max_source_positions=args.max_source_positions, max_target_positions=args.max_target_positions, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', num_conv_layers=len(__UpperCAmelCase ), conv_channels=args.conv_channels, conv_kernel_sizes=__UpperCAmelCase, input_feat_per_channel=args.input_feat_per_channel, input_channels=args.input_channels, tie_word_embeddings=__UpperCAmelCase, num_beams=5, max_length=200, use_cache=__UpperCAmelCase, decoder_start_token_id=2, early_stopping=__UpperCAmelCase, )
snake_case_ = SpeechaTextForConditionalGeneration(__UpperCAmelCase )
snake_case_ ,snake_case_ = model.model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0 and not set(__UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F" but all the following weights are missing {missing}" )
if tie_embeds:
snake_case_ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ = lm_head_weights
model.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
a : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 56
| 0
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
_A : List[Any] =logging.get_logger(__name__)
_A : List[str] ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_A : Optional[Any] ={
'''vocab_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
),
},
'''merges_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
'''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
),
},
'''tokenizer_file''': {
'''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
'''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
'''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
'''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
'''roberta-base-openai-detector''': (
'''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
),
'''roberta-large-openai-detector''': (
'''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
),
},
}
_A : Optional[Any] ={
'''roberta-base''': 512,
'''roberta-large''': 512,
'''roberta-large-mnli''': 512,
'''distilroberta-base''': 512,
'''roberta-base-openai-detector''': 512,
'''roberta-large-openai-detector''': 512,
}
class _lowercase ( _lowercase ):
a = VOCAB_FILES_NAMES
a = PRETRAINED_VOCAB_FILES_MAP
a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a = ["""input_ids""", """attention_mask"""]
a = RobertaTokenizer
def __init__( self: List[str] , UpperCamelCase__: Union[str, Any]=None , UpperCamelCase__: Dict=None , UpperCamelCase__: int=None , UpperCamelCase__: List[Any]="replace" , UpperCamelCase__: List[Any]="<s>" , UpperCamelCase__: Optional[Any]="</s>" , UpperCamelCase__: str="</s>" , UpperCamelCase__: List[Any]="<s>" , UpperCamelCase__: Union[str, Any]="<unk>" , UpperCamelCase__: Dict="<pad>" , UpperCamelCase__: Any="<mask>" , UpperCamelCase__: str=False , UpperCamelCase__: List[Any]=True , **UpperCamelCase__: int , ):
super().__init__(
UpperCamelCase__ , UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , errors=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , trim_offsets=UpperCamelCase__ , **UpperCamelCase__ , )
lowerCamelCase__ : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , UpperCamelCase__ ) != add_prefix_space:
lowerCamelCase__ : int = getattr(UpperCamelCase__ , pre_tok_state.pop("""type""" ) )
lowerCamelCase__ : Dict = add_prefix_space
lowerCamelCase__ : Union[str, Any] = pre_tok_class(**UpperCamelCase__ )
lowerCamelCase__ : Any = add_prefix_space
lowerCamelCase__ : List[Any] = """post_processor"""
lowerCamelCase__ : Optional[Any] = getattr(self.backend_tokenizer , UpperCamelCase__ , UpperCamelCase__ )
if tokenizer_component_instance:
lowerCamelCase__ : int = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCamelCase__ : Optional[Any] = tuple(state["""sep"""] )
if "cls" in state:
lowerCamelCase__ : List[Any] = tuple(state["""cls"""] )
lowerCamelCase__ : int = False
if state.get("""add_prefix_space""" , UpperCamelCase__ ) != add_prefix_space:
lowerCamelCase__ : Optional[Any] = add_prefix_space
lowerCamelCase__ : Any = True
if state.get("""trim_offsets""" , UpperCamelCase__ ) != trim_offsets:
lowerCamelCase__ : Optional[Any] = trim_offsets
lowerCamelCase__ : Tuple = True
if changes_to_apply:
lowerCamelCase__ : Optional[int] = getattr(UpperCamelCase__ , state.pop("""type""" ) )
lowerCamelCase__ : Any = component_class(**UpperCamelCase__ )
setattr(self.backend_tokenizer , UpperCamelCase__ , UpperCamelCase__ )
@property
def lowerCamelCase_ ( self: Union[str, Any] ):
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase_ ( self: str , UpperCamelCase__: int ):
lowerCamelCase__ : int = AddedToken(UpperCamelCase__ , lstrip=UpperCamelCase__ , rstrip=UpperCamelCase__ ) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else value
lowerCamelCase__ : str = value
def lowerCamelCase_ ( self: Any , *UpperCamelCase__: Optional[int] , **UpperCamelCase__: Optional[int] ):
lowerCamelCase__ : List[str] = kwargs.get("""is_split_into_words""" , UpperCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self: Any , *UpperCamelCase__: Any , **UpperCamelCase__: Optional[Any] ):
lowerCamelCase__ : int = kwargs.get("""is_split_into_words""" , UpperCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs."
)
return super()._encode_plus(*UpperCamelCase__ , **UpperCamelCase__ )
def lowerCamelCase_ ( self: str , UpperCamelCase__: str , UpperCamelCase__: Optional[str] = None ):
lowerCamelCase__ : Any = self._tokenizer.model.save(UpperCamelCase__ , name=UpperCamelCase__ )
return tuple(UpperCamelCase__ )
def lowerCamelCase_ ( self: Dict , UpperCamelCase__: Any , UpperCamelCase__: int=None ):
lowerCamelCase__ : Tuple = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase_ ( self: Optional[Any] , UpperCamelCase__: List[int] , UpperCamelCase__: Optional[List[int]] = None ):
lowerCamelCase__ : Optional[int] = [self.sep_token_id]
lowerCamelCase__ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 41
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : str ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 56
| 0
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def SCREAMING_SNAKE_CASE__ ( __A ) -> Optional[int]:
_snake_case = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(__A , __A )
def SCREAMING_SNAKE_CASE__ ( __A ) -> Dict:
_snake_case = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
_snake_case = s_dict.pop(__A )
elif "subsample" in key:
_snake_case = s_dict.pop(__A )
def SCREAMING_SNAKE_CASE__ ( __A ) -> Dict:
_snake_case , _snake_case = emb.weight.shape
_snake_case = nn.Linear(__A , __A , bias=__A )
_snake_case = emb.weight.data
return lin_layer
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> Union[str, Any]:
_snake_case = torch.load(__A , map_location='cpu' )
_snake_case = mam_aaa['args']
_snake_case = mam_aaa['model']
_snake_case = state_dict['decoder.output_projection.weight']
remove_ignore_keys_(__A )
rename_keys(__A )
_snake_case = state_dict['decoder.embed_tokens.weight'].shape[0]
_snake_case = args.share_decoder_input_output_embed
_snake_case = [int(__A ) for i in args.conv_kernel_sizes.split(',' )]
_snake_case = SpeechaTextConfig(
vocab_size=__A , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , num_conv_layers=len(__A ) , conv_channels=args.conv_channels , conv_kernel_sizes=__A , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=__A , num_beams=5 , max_length=200 , use_cache=__A , decoder_start_token_id=2 , early_stopping=__A , )
_snake_case = SpeechaTextForConditionalGeneration(__A )
_snake_case , _snake_case = model.model.load_state_dict(__A , strict=__A )
if len(__A ) > 0 and not set(__A ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'
F' but all the following weights are missing {missing}' )
if tie_embeds:
_snake_case = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
_snake_case = lm_head_weights
model.save_pretrained(__A )
if __name__ == "__main__":
lowercase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
lowercase : Any = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 42
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a : int = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
config.addinivalue_line(
'''markers''', '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''', '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''', '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''', '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''', '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''', '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
snake_case_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCAmelCase, id=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if exitstatus == 5:
snake_case_ = 0
# Doctest custom flag to ignore output.
a : Union[str, Any] = doctest.register_optionflag('IGNORE_RESULT')
a : Optional[int] = doctest.OutputChecker
class a ( _lowerCamelCase ):
def A_ ( self : List[Any] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Optional[int] ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , lowercase_ , lowercase_ , lowercase_ )
a : List[Any] = CustomOutputChecker
a : Optional[int] = HfDoctestModule
a : Tuple = HfDocTestParser
| 56
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def lowerCamelCase ( SCREAMING_SNAKE_CASE = 100 ):
'''simple docstring'''
__UpperCamelCase :Any = 0
__UpperCamelCase :int = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F'{solution() = }')
| 43
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
a : Dict = logging.get_logger(__name__)
a : List[str] = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class a ( _lowerCamelCase ):
snake_case_ = "marian"
snake_case_ = ["past_key_values"]
snake_case_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , lowercase_ : Optional[Any]=5_8101 , lowercase_ : Dict=None , lowercase_ : List[str]=1024 , lowercase_ : Optional[Any]=12 , lowercase_ : int=4096 , lowercase_ : Any=16 , lowercase_ : Optional[int]=12 , lowercase_ : str=4096 , lowercase_ : Union[str, Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Union[str, Any]=True , lowercase_ : int="gelu" , lowercase_ : Dict=1024 , lowercase_ : int=0.1 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.02 , lowercase_ : int=5_8100 , lowercase_ : Optional[Any]=False , lowercase_ : Any=5_8100 , lowercase_ : Optional[int]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=True , **lowercase_ : Any , ):
snake_case_ = vocab_size
snake_case_ = decoder_vocab_size or vocab_size
snake_case_ = max_position_embeddings
snake_case_ = d_model
snake_case_ = encoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class a ( _lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A_ ( self : Union[str, Any] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ = {0: '''batch'''}
snake_case_ = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A_ ( self : Dict ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super().outputs
else:
snake_case_ = super(lowercase_ , self ).outputs
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A_ ( self : Dict , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
snake_case_ = seq_length if not self.use_past else 1
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case_ = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
snake_case_ = common_inputs['''decoder_input_ids'''].shape[1]
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = decoder_seq_length + 3
snake_case_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
snake_case_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ ,snake_case_ = self.num_layers
snake_case_ = min(lowercase_ , lowercase_ )
snake_case_ = max(lowercase_ , lowercase_ ) - min_num_layers
snake_case_ = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
snake_case_ = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def A_ ( self : Union[str, Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case_ = seqlen + 2
snake_case_ ,snake_case_ = self.num_layers
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = common_inputs['''attention_mask'''].dtype
snake_case_ = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
snake_case_ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def A_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ = tokenizer.num_special_tokens_to_add(lowercase_ )
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def A_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
snake_case_ = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def A_ ( self : Dict , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : List[str] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
snake_case_ = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
@property
def A_ ( self : List[str] ):
return 1e-4
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"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> typing.Counter[int]:
_lowerCAmelCase : typing.Counter[int] = Counter()
for base in range(1 ,max_perimeter + 1 ):
for perpendicular in range(_lowerCamelCase ,max_perimeter + 1 ):
_lowerCAmelCase : Optional[int] = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000 ) -> int:
_lowerCAmelCase : int = pythagorean_triple(_lowerCamelCase )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(F"""Perimeter {solution()} has maximum solutions""")
| 44
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = CycleDiffusionPipeline
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
snake_case_ = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A_ ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ = 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 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
snake_case_ = 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 , )
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case_ = CLIPTextModel(lowercase_ )
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 A_ ( self : Any , lowercase_ : int , lowercase_ : Optional[Any]=0 ):
snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ = image / 2 + 0.5
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(lowercase_ )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def A_ ( self : Union[str, Any] ):
snake_case_ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , '''half''' ):
snake_case_ = module.half()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Optional[int] ):
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def A_ ( self : List[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_save_load_optional_components()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Union[str, Any] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def A_ ( self : List[str] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
assert np.abs(image - expected_image ).max() < 2e-2
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"""simple docstring"""
from math import factorial, pi
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 30 ) -> float:
if not isinstance(lowerCAmelCase__ , (int, float) ):
raise ValueError('''maclaurin_sin() requires either an int or float for theta''' )
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or accuracy <= 0:
raise ValueError('''maclaurin_sin() requires a positive int for accuracy''' )
__a = float(lowerCAmelCase__ )
__a = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(lowerCAmelCase__ ) )
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : int = 30 ) -> float:
if not isinstance(lowerCAmelCase__ , (int, float) ):
raise ValueError('''maclaurin_cos() requires either an int or float for theta''' )
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) or accuracy <= 0:
raise ValueError('''maclaurin_cos() requires a positive int for accuracy''' )
__a = float(lowerCAmelCase__ )
__a = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(lowerCAmelCase__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(1_0))
print(maclaurin_sin(-1_0))
print(maclaurin_sin(1_0, 1_5))
print(maclaurin_sin(-1_0, 1_5))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(1_0, 1_5))
print(maclaurin_cos(-1_0, 1_5))
| 45
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class a ( _lowerCamelCase ):
snake_case_ = "big_bird"
def __init__( self : Union[str, Any] , lowercase_ : List[Any]=5_0358 , lowercase_ : Tuple=768 , lowercase_ : Dict=12 , lowercase_ : str=12 , lowercase_ : Tuple=3072 , lowercase_ : Any="gelu_new" , lowercase_ : Optional[Any]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=4096 , lowercase_ : List[Any]=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[int]=1e-12 , lowercase_ : Tuple=True , lowercase_ : Tuple=0 , lowercase_ : str=1 , lowercase_ : Union[str, Any]=2 , lowercase_ : Optional[Any]=66 , lowercase_ : Optional[int]="block_sparse" , lowercase_ : Any=True , lowercase_ : List[str]=False , lowercase_ : Any=64 , lowercase_ : Tuple=3 , lowercase_ : Tuple=None , **lowercase_ : Tuple , ):
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , sep_token_id=lowercase_ , **lowercase_ , )
snake_case_ = vocab_size
snake_case_ = max_position_embeddings
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_ = type_vocab_size
snake_case_ = layer_norm_eps
snake_case_ = use_cache
snake_case_ = rescale_embeddings
snake_case_ = attention_type
snake_case_ = use_bias
snake_case_ = block_size
snake_case_ = num_random_blocks
snake_case_ = classifier_dropout
class a ( _lowerCamelCase ):
@property
def A_ ( self : str ):
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 56
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"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class lowercase :
def __init__( self , lowercase , lowercase=99 , lowercase=13 , lowercase=7 , lowercase=9 , lowercase=True , lowercase=True , lowercase=False , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase=8 , lowercase=0.1 , lowercase=0.002 , lowercase=1 , lowercase=0 , lowercase=0 , lowercase=None , lowercase=None , ) -> Optional[Any]:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = encoder_seq_length
lowerCAmelCase = decoder_seq_length
# For common tests
lowerCAmelCase = self.decoder_seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_attention_mask
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = d_ff
lowerCAmelCase = relative_attention_num_buckets
lowerCAmelCase = dropout_rate
lowerCAmelCase = initializer_factor
lowerCAmelCase = eos_token_id
lowerCAmelCase = pad_token_id
lowerCAmelCase = decoder_start_token_id
lowerCAmelCase = None
lowerCAmelCase = decoder_layers
def _snake_case ( self ) -> str:
return TaConfig.from_pretrained("""google/umt5-base""" )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None , ) -> Optional[Any]:
if attention_mask is None:
lowerCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=lowercase )
if decoder_head_mask is None:
lowerCAmelCase = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=lowercase )
if cross_attn_head_mask is None:
lowerCAmelCase = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=lowercase )
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,
}
def _snake_case ( self ) -> int:
lowerCAmelCase = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
lowerCAmelCase = self.get_config()
lowerCAmelCase = config.num_attention_heads
lowerCAmelCase = self.prepare_inputs_dict(lowercase , lowercase , lowercase )
return config, input_dict
def _snake_case ( self ) -> int:
lowerCAmelCase , lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def _snake_case ( self ) -> List[str]:
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _snake_case ( self ) -> Optional[Any]:
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> str:
lowerCAmelCase = UMTaModel(config=lowercase )
model.to(lowercase )
model.eval()
lowerCAmelCase = model(
input_ids=lowercase , decoder_input_ids=lowercase , attention_mask=lowercase , decoder_attention_mask=lowercase , )
lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase )
lowerCAmelCase = result.last_hidden_state
lowerCAmelCase = result.past_key_values
lowerCAmelCase = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(lowercase ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase , ) -> Optional[Any]:
lowerCAmelCase = UMTaModel(config=lowercase ).get_decoder().to(lowercase ).eval()
# first forward pass
lowerCAmelCase = model(lowercase , use_cache=lowercase )
lowerCAmelCase = model(lowercase )
lowerCAmelCase = model(lowercase , use_cache=lowercase )
self.parent.assertTrue(len(lowercase ) == len(lowercase ) )
self.parent.assertTrue(len(lowercase ) == len(lowercase ) + 1 )
lowerCAmelCase , lowerCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCAmelCase = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
lowerCAmelCase = model(lowercase )["""last_hidden_state"""]
lowerCAmelCase = model(lowercase , past_key_values=lowercase )["""last_hidden_state"""]
# select random slice
lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowerCAmelCase = output_from_no_past[:, -1, random_slice_idx].detach()
lowerCAmelCase = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowercase , lowercase , atol=1e-3 ) )
def _snake_case ( self , lowercase , lowercase , ) -> str:
lowerCAmelCase = UMTaModel(config=lowercase ).to(lowercase ).half().eval()
lowerCAmelCase = model(**lowercase )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(lowercase ).any().item() )
@require_torch
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE = (
{
'conversational': UMTaForConditionalGeneration,
'feature-extraction': UMTaModel,
'summarization': UMTaForConditionalGeneration,
'text2text-generation': UMTaForConditionalGeneration,
'translation': UMTaForConditionalGeneration,
'question-answering': UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE = [0.8, 0.9]
def _snake_case ( self ) -> str:
lowerCAmelCase = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _snake_case ( self ) -> Dict:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase = UMTaModel(config_and_inputs[0] ).to(lowercase )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
lowercase , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'{tmpdirname}/t5_test.onnx' , export_params=lowercase , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*lowercase )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase = config_and_inputs[0]
lowerCAmelCase = UMTaForConditionalGeneration(lowercase ).eval()
model.to(lowercase )
lowerCAmelCase = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=lowercase ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=lowercase ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=lowercase ),
}
for attn_name, (name, mask) in zip(lowercase , head_masking.items() ):
lowerCAmelCase = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
lowerCAmelCase = torch.ones(
config.num_decoder_layers , config.num_heads , device=lowercase )
lowerCAmelCase = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=lowercase , return_dict_in_generate=lowercase , **lowercase , )
# We check the state of decoder_attentions and cross_attentions just from the last step
lowerCAmelCase = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _snake_case ( self ) -> Union[str, Any]:
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class lowercase ( unittest.TestCase ):
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _snake_case ( self ) -> Optional[Any]:
lowerCAmelCase = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=lowercase ).to(lowercase )
lowerCAmelCase = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=lowercase , legacy=lowercase )
lowerCAmelCase = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
lowerCAmelCase = tokenizer(lowercase , return_tensors="""pt""" , padding=lowercase ).input_ids
# fmt: off
lowerCAmelCase = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(lowercase , lowercase )
lowerCAmelCase = model.generate(input_ids.to(lowercase ) )
lowerCAmelCase = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
lowerCAmelCase = tokenizer.batch_decode(lowercase )
self.assertEqual(lowercase , lowercase )
| 46
|
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''simple docstring'''
assert isinstance(__UpperCAmelCase, __UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''', [False, True] )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = SqlDatasetReader(
'''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase, keep_in_memory=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
'''features''', [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
], )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, features=__UpperCAmelCase, cache_dir=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(__UpperCAmelCase ) ) as con:
snake_case_ = con.cursor()
cur.execute('''SELECT * FROM dataset''' )
for row in cur:
yield row
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=1 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=2 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
with pytest.raises(__UpperCAmelCase ):
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=0 ).write()
| 56
| 0
|
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
"huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}
class A__ ( A__ ):
A__ = 'autoformer'
A__ = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self : Dict , _a : Optional[int] = None , _a : Optional[int] = None , _a : str = "student_t" , _a : str = "nll" , _a : int = 1 , _a : List[int] = [1, 2, 3, 4, 5, 6, 7] , _a : bool = True , _a : int = 0 , _a : int = 0 , _a : int = 0 , _a : int = 0 , _a : Optional[List[int]] = None , _a : Optional[List[int]] = None , _a : int = 64 , _a : int = 2 , _a : int = 2 , _a : int = 2 , _a : int = 2 , _a : int = 32 , _a : int = 32 , _a : str = "gelu" , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : float = 0.1 , _a : int = 100 , _a : float = 0.02 , _a : bool = True , _a : Dict=True , _a : int = 10 , _a : int = 25 , _a : int = 3 , **_a : str , ) -> str:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =prediction_length
_SCREAMING_SNAKE_CASE =context_length if context_length is not None else prediction_length
_SCREAMING_SNAKE_CASE =distribution_output
_SCREAMING_SNAKE_CASE =loss
_SCREAMING_SNAKE_CASE =input_size
_SCREAMING_SNAKE_CASE =num_time_features
_SCREAMING_SNAKE_CASE =lags_sequence
_SCREAMING_SNAKE_CASE =scaling
_SCREAMING_SNAKE_CASE =num_dynamic_real_features
_SCREAMING_SNAKE_CASE =num_static_real_features
_SCREAMING_SNAKE_CASE =num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
'The cardinality should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE =cardinality
else:
_SCREAMING_SNAKE_CASE =[0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(_a ) != num_static_categorical_features:
raise ValueError(
'The embedding dimension should be a list of the same length as `num_static_categorical_features`' )
_SCREAMING_SNAKE_CASE =embedding_dimension
else:
_SCREAMING_SNAKE_CASE =[min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_SCREAMING_SNAKE_CASE =num_parallel_samples
# Transformer architecture configuration
_SCREAMING_SNAKE_CASE =input_size * len(self.lags_sequence ) + self._number_of_features
_SCREAMING_SNAKE_CASE =d_model
_SCREAMING_SNAKE_CASE =encoder_attention_heads
_SCREAMING_SNAKE_CASE =decoder_attention_heads
_SCREAMING_SNAKE_CASE =encoder_ffn_dim
_SCREAMING_SNAKE_CASE =decoder_ffn_dim
_SCREAMING_SNAKE_CASE =encoder_layers
_SCREAMING_SNAKE_CASE =decoder_layers
_SCREAMING_SNAKE_CASE =dropout
_SCREAMING_SNAKE_CASE =attention_dropout
_SCREAMING_SNAKE_CASE =activation_dropout
_SCREAMING_SNAKE_CASE =encoder_layerdrop
_SCREAMING_SNAKE_CASE =decoder_layerdrop
_SCREAMING_SNAKE_CASE =activation_function
_SCREAMING_SNAKE_CASE =init_std
_SCREAMING_SNAKE_CASE =use_cache
# Autoformer
_SCREAMING_SNAKE_CASE =label_length
_SCREAMING_SNAKE_CASE =moving_average
_SCREAMING_SNAKE_CASE =autocorrelation_factor
super().__init__(is_encoder_decoder=_a , **_a )
@property
def A ( self : Any ) -> int:
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 47
|
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCase )
if ret % 2 == 0:
cuts.append(__UpperCAmelCase )
return ret
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
a ,a : Dict = 10, 9
a : Dict = defaultdict(list)
a : dict[int, bool] = {}
a : list[int] = []
a : Tuple = 0
a : str = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 56
| 0
|
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Dict = ["""image_processor""", """tokenizer"""]
lowerCamelCase_ : Optional[Any] = """FlavaImageProcessor"""
lowerCamelCase_ : Optional[Any] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self , UpperCamelCase__=None , UpperCamelCase__=None , **UpperCamelCase__ ) -> List[Any]:
lowerCamelCase : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , UpperCamelCase__ , )
lowerCamelCase : Dict = kwargs.pop("feature_extractor" )
lowerCamelCase : List[Any] = 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__(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : List[Any] = self.image_processor
def __call__( self , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = True , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = 0 , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = True , UpperCamelCase__ = None , **UpperCamelCase__ , ) -> Optional[int]:
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:
lowerCamelCase : Union[str, Any] = self.tokenizer(
text=UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , stride=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_overflowing_tokens=UpperCamelCase__ , return_special_tokens_mask=UpperCamelCase__ , return_offsets_mapping=UpperCamelCase__ , return_length=UpperCamelCase__ , verbose=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
if images is not None:
lowerCamelCase : str = self.image_processor(
UpperCamelCase__ , return_image_mask=UpperCamelCase__ , return_codebook_pixels=UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
if text is not None and images is not None:
encoding.update(UpperCamelCase__ )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**UpperCamelCase__ ) , tensor_type=UpperCamelCase__ )
def _lowercase ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ )
def _lowercase ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[int]:
return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ )
@property
def _lowercase ( self ) -> str:
lowerCamelCase : Tuple = self.tokenizer.model_input_names
lowerCamelCase : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _lowercase ( self ) -> Tuple:
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , UpperCamelCase__ , )
return self.image_processor_class
@property
def _lowercase ( self ) -> Any:
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , UpperCamelCase__ , )
return self.image_processor
| 48
|
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56
| 0
|
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
__snake_case :Any = False
__snake_case :List[str] = logging.get_logger(__name__)
__snake_case :int = '''ybelkada/fonts'''
def __snake_case ( ):
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
f'You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '
'''Pix2StructImageProcessor. Please upgrade torch.''' )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
requires_backends(_UpperCAmelCase , ['''torch'''] )
_check_torch_version()
__a = image_tensor.unsqueeze(0 )
__a = torch.nn.functional.unfold(_UpperCAmelCase , (patch_height, patch_width) , stride=(patch_height, patch_width) )
__a = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , _UpperCAmelCase , _UpperCAmelCase , -1 )
__a = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase = 36 , _UpperCAmelCase = "black" , _UpperCAmelCase = "white" , _UpperCAmelCase = 5 , _UpperCAmelCase = 5 , _UpperCAmelCase = 5 , _UpperCAmelCase = 5 , _UpperCAmelCase = None , _UpperCAmelCase = None , ):
requires_backends(_UpperCAmelCase , '''vision''' )
# Add new lines so that each line is no more than 80 characters.
__a = textwrap.TextWrapper(width=80 )
__a = wrapper.wrap(text=_UpperCAmelCase )
__a = '''\n'''.join(_UpperCAmelCase )
if font_bytes is not None and font_path is None:
__a = io.BytesIO(_UpperCAmelCase )
elif font_path is not None:
__a = font_path
else:
__a = hf_hub_download(_UpperCAmelCase , '''Arial.TTF''' )
__a = ImageFont.truetype(_UpperCAmelCase , encoding='''UTF-8''' , size=_UpperCAmelCase )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
__a = ImageDraw.Draw(Image.new('''RGB''' , (1, 1) , _UpperCAmelCase ) )
__a , __a , __a , __a = temp_draw.textbbox((0, 0) , _UpperCAmelCase , _UpperCAmelCase )
# Create the actual image with a bit of padding around the text.
__a = text_width + left_padding + right_padding
__a = text_height + top_padding + bottom_padding
__a = Image.new('''RGB''' , (image_width, image_height) , _UpperCAmelCase )
__a = ImageDraw.Draw(_UpperCAmelCase )
draw.text(xy=(left_padding, top_padding) , text=_UpperCAmelCase , fill=_UpperCAmelCase , font=_UpperCAmelCase )
return image
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , **_UpperCAmelCase ):
requires_backends(_UpperCAmelCase , '''vision''' )
# Convert to PIL image if necessary
__a = to_pil_image(_UpperCAmelCase )
__a = render_text(_UpperCAmelCase , **_UpperCAmelCase )
__a = max(header_image.width , image.width )
__a = int(image.height * (new_width / image.width) )
__a = int(header_image.height * (new_width / header_image.width) )
__a = Image.new('''RGB''' , (new_width, new_height + new_header_height) , '''white''' )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
__a = to_numpy_array(_UpperCAmelCase )
if infer_channel_dimension_format(_UpperCAmelCase ) == ChannelDimension.LAST:
__a = to_channel_dimension_format(_UpperCAmelCase , ChannelDimension.LAST )
return new_image
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : List[str] = ['''flattened_patches''']
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : bool = True , __SCREAMING_SNAKE_CASE : Dict[str, int] = None , __SCREAMING_SNAKE_CASE : int = 2_048 , __SCREAMING_SNAKE_CASE : bool = False , **__SCREAMING_SNAKE_CASE : Dict , ):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
__a = patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
__a = do_normalize
__a = do_convert_rgb
__a = max_patches
__a = is_vqa
def _lowerCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : dict , **__SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
requires_backends(self.extract_flattened_patches , '''torch''')
_check_torch_version()
# convert to torch
__a = to_channel_dimension_format(__SCREAMING_SNAKE_CASE , ChannelDimension.FIRST)
__a = torch.from_numpy(__SCREAMING_SNAKE_CASE)
__a , __a = patch_size['''height'''], patch_size['''width''']
__a , __a = get_image_size(__SCREAMING_SNAKE_CASE)
# maximize scale s.t.
__a = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width))
__a = max(min(math.floor(scale * image_height / patch_height) , __SCREAMING_SNAKE_CASE) , 1)
__a = max(min(math.floor(scale * image_width / patch_width) , __SCREAMING_SNAKE_CASE) , 1)
__a = max(num_feasible_rows * patch_height , 1)
__a = max(num_feasible_cols * patch_width , 1)
__a = torch.nn.functional.interpolate(
image.unsqueeze(0) , size=(resized_height, resized_width) , mode='''bilinear''' , align_corners=__SCREAMING_SNAKE_CASE , antialias=__SCREAMING_SNAKE_CASE , ).squeeze(0)
# [1, rows, columns, patch_height * patch_width * image_channels]
__a = torch_extract_patches(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE)
__a = patches.shape
__a = patches_shape[1]
__a = patches_shape[2]
__a = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
__a = patches.reshape([rows * columns, depth])
# [rows * columns, 1]
__a = torch.arange(__SCREAMING_SNAKE_CASE).reshape([rows, 1]).repeat(1 , __SCREAMING_SNAKE_CASE).reshape([rows * columns, 1])
__a = torch.arange(__SCREAMING_SNAKE_CASE).reshape([1, columns]).repeat(__SCREAMING_SNAKE_CASE , 1).reshape([rows * columns, 1])
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
__a = row_ids.to(torch.floataa)
__a = col_ids.to(torch.floataa)
# [rows * columns, 2 + patch_height * patch_width * image_channels]
__a = torch.cat([row_ids, col_ids, patches] , -1)
# [max_patches, 2 + patch_height * patch_width * image_channels]
__a = torch.nn.functional.pad(__SCREAMING_SNAKE_CASE , [0, 0, 0, max_patches - (rows * columns)]).float()
__a = to_numpy_array(__SCREAMING_SNAKE_CASE)
return result
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : np.ndarray , __SCREAMING_SNAKE_CASE : Optional[Union[str, ChannelDimension]] = None , **__SCREAMING_SNAKE_CASE : Any):
'''simple docstring'''
if image.dtype == np.uinta:
__a = image.astype(np.floataa)
# take mean across the whole `image`
__a = np.mean(__SCREAMING_SNAKE_CASE)
__a = np.std(__SCREAMING_SNAKE_CASE)
__a = max(__SCREAMING_SNAKE_CASE , 1.0 / math.sqrt(np.prod(image.shape)))
return normalize(__SCREAMING_SNAKE_CASE , mean=__SCREAMING_SNAKE_CASE , std=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : ImageInput , __SCREAMING_SNAKE_CASE : Optional[str] = None , __SCREAMING_SNAKE_CASE : bool = None , __SCREAMING_SNAKE_CASE : Optional[bool] = None , __SCREAMING_SNAKE_CASE : Optional[int] = None , __SCREAMING_SNAKE_CASE : Optional[Dict[str, int]] = None , __SCREAMING_SNAKE_CASE : Optional[Union[str, TensorType]] = None , __SCREAMING_SNAKE_CASE : ChannelDimension = ChannelDimension.FIRST , **__SCREAMING_SNAKE_CASE : Optional[int] , ):
'''simple docstring'''
__a = do_normalize if do_normalize is not None else self.do_normalize
__a = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__a = patch_size if patch_size is not None else self.patch_size
__a = max_patches if max_patches is not None else self.max_patches
__a = self.is_vqa
if kwargs.get('''data_format''' , __SCREAMING_SNAKE_CASE) is not None:
raise ValueError('''data_format is not an accepted input as the outputs are ''')
__a = make_list_of_images(__SCREAMING_SNAKE_CASE)
if not valid_images(__SCREAMING_SNAKE_CASE):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''')
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__a = [convert_to_rgb(__SCREAMING_SNAKE_CASE) for image in images]
# All transformations expect numpy arrays.
__a = [to_numpy_array(__SCREAMING_SNAKE_CASE) for image in images]
if is_vqa:
if header_text is None:
raise ValueError('''A header text must be provided for VQA models.''')
__a = kwargs.pop('''font_bytes''' , __SCREAMING_SNAKE_CASE)
__a = kwargs.pop('''font_path''' , __SCREAMING_SNAKE_CASE)
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE):
__a = [header_text] * len(__SCREAMING_SNAKE_CASE)
__a = [
render_header(__SCREAMING_SNAKE_CASE , header_text[i] , font_bytes=__SCREAMING_SNAKE_CASE , font_path=__SCREAMING_SNAKE_CASE)
for i, image in enumerate(__SCREAMING_SNAKE_CASE)
]
if do_normalize:
__a = [self.normalize(image=__SCREAMING_SNAKE_CASE) for image in images]
# convert to torch tensor and permute
__a = [
self.extract_flattened_patches(image=__SCREAMING_SNAKE_CASE , max_patches=__SCREAMING_SNAKE_CASE , patch_size=__SCREAMING_SNAKE_CASE)
for image in images
]
# create attention mask in numpy
__a = [(image.sum(axis=-1) != 0).astype(np.floataa) for image in images]
__a = BatchFeature(
data={'''flattened_patches''': images, '''attention_mask''': attention_masks} , tensor_type=__SCREAMING_SNAKE_CASE)
return encoded_outputs
| 49
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Any = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = DPTConfig()
if "large" in checkpoint_url:
snake_case_ = 1024
snake_case_ = 4096
snake_case_ = 24
snake_case_ = 16
snake_case_ = [5, 11, 17, 23]
snake_case_ = [256, 512, 1024, 1024]
snake_case_ = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case_ = True
snake_case_ = 150
snake_case_ = '''huggingface/label-files'''
snake_case_ = '''ade20k-id2label.json'''
snake_case_ = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset''' ) ), '''r''' ) )
snake_case_ = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = [1, 150, 480, 480]
return config, expected_shape
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case_ = name.replace('''pretrained.model''', '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case_ = name.replace('''pretrained.model''', '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case_ = name.replace('''patch_embed''', '''patch_embeddings''' )
if "pos_embed" in name:
snake_case_ = name.replace('''pos_embed''', '''position_embeddings''' )
if "attn.proj" in name:
snake_case_ = name.replace('''attn.proj''', '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case_ = name.replace('''proj''', '''projection''' )
if "blocks" in name:
snake_case_ = name.replace('''blocks''', '''layer''' )
if "mlp.fc1" in name:
snake_case_ = name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case_ = name.replace('''mlp.fc2''', '''output.dense''' )
if "norm1" in name:
snake_case_ = name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
snake_case_ = name.replace('''norm2''', '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case_ = name.replace('''scratch.output_conv''', '''head''' )
if "scratch" in name:
snake_case_ = name.replace('''scratch''', '''neck''' )
if "layer1_rn" in name:
snake_case_ = name.replace('''layer1_rn''', '''convs.0''' )
if "layer2_rn" in name:
snake_case_ = name.replace('''layer2_rn''', '''convs.1''' )
if "layer3_rn" in name:
snake_case_ = name.replace('''layer3_rn''', '''convs.2''' )
if "layer4_rn" in name:
snake_case_ = name.replace('''layer4_rn''', '''convs.3''' )
if "refinenet" in name:
snake_case_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case_ = name.replace(F"refinenet{layer_idx}", F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
snake_case_ = name.replace('''out_conv''', '''projection''' )
if "resConfUnit1" in name:
snake_case_ = name.replace('''resConfUnit1''', '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case_ = name.replace('''resConfUnit2''', '''residual_layer2''' )
if "conv1" in name:
snake_case_ = name.replace('''conv1''', '''convolution1''' )
if "conv2" in name:
snake_case_ = name.replace('''conv2''', '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.0.project.0''', '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.0.project.0''', '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.0.project.0''', '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.0.project.0''', '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.3''', '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.4''', '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.3''', '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.4''', '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.3''', '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.3''', '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.4''', '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case_ = name.replace('''pretrained''', '''dpt''' )
if "bn" in name:
snake_case_ = name.replace('''bn''', '''batch_norm''' )
if "head" in name:
snake_case_ = name.replace('''head''', '''head.head''' )
if "encoder.norm" in name:
snake_case_ = name.replace('''encoder.norm''', '''layernorm''' )
if "auxlayer" in name:
snake_case_ = name.replace('''auxlayer''', '''auxiliary_head.head''' )
return name
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{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 __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ = Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dpt_config(__UpperCAmelCase )
# load original state_dict from URL
snake_case_ = torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
snake_case_ = state_dict.pop(__UpperCAmelCase )
snake_case_ = val
# read in qkv matrices
read_in_q_k_v(__UpperCAmelCase, __UpperCAmelCase )
# load HuggingFace model
snake_case_ = DPTForSemanticSegmentation(__UpperCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
# Check outputs on an image
snake_case_ = 480 if '''ade''' in checkpoint_url else 384
snake_case_ = DPTImageProcessor(size=__UpperCAmelCase )
snake_case_ = prepare_img()
snake_case_ = image_processor(__UpperCAmelCase, return_tensors='''pt''' )
# forward pass
snake_case_ = model(**__UpperCAmelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase ).predicted_depth
# Assert logits
snake_case_ = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
snake_case_ = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__UpperCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3], __UpperCAmelCase, atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3], __UpperCAmelCase )
)
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add model''', use_temp_dir=__UpperCAmelCase, )
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add image processor''', use_temp_dir=__UpperCAmelCase, )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
a : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 56
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|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
@slow
def A_ ( self : int ) -> Any:
lowerCamelCase__ : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=UpperCAmelCase ).to(UpperCAmelCase )
lowerCamelCase__ : str = AutoTokenizer.from_pretrained('google/mt5-small' )
lowerCamelCase__ : Any = tokenizer('Hello there' , return_tensors='pt' ).input_ids
lowerCamelCase__ : Tuple = tokenizer('Hi I am' , return_tensors='pt' ).input_ids
lowerCamelCase__ : Optional[int] = model(input_ids.to(UpperCAmelCase ) , labels=labels.to(UpperCAmelCase ) ).loss
lowerCamelCase__ : Dict = -(labels.shape[-1] * loss.item())
lowerCamelCase__ : Optional[int] = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 50
|
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
a : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 56
| 0
|
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL
| 51
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
re.sub('''<n>''', '''''', __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 56
| 0
|
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__lowerCamelCase : Tuple = (720, 1280) # Height, Width
__lowerCamelCase : int = (0.4, 0.6) # if height or width lower than this scale, drop it.
__lowerCamelCase : int = 1 / 100
__lowerCamelCase : Any = """"""
__lowerCamelCase : List[str] = """"""
__lowerCamelCase : List[Any] = """"""
__lowerCamelCase : Tuple = 250
def A_ ( ) -> None:
UpperCamelCase , UpperCamelCase : Tuple = get_dataset(_lowerCAmelCase , _lowerCAmelCase )
for index in range(_lowerCAmelCase ):
UpperCamelCase : Union[str, Any] = random.sample(range(len(_lowerCAmelCase ) ) , 4 )
UpperCamelCase , UpperCamelCase , UpperCamelCase : str = update_image_and_anno(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , filter_scale=_lowerCAmelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
UpperCamelCase : Union[str, Any] = random_chars(32 )
UpperCamelCase : Optional[Any] = path.split(os.sep )[-1].rsplit("." , 1 )[0]
UpperCamelCase : Optional[Any] = F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(F"""{file_root}.jpg""" , _lowerCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
UpperCamelCase : int = []
for anno in new_annos:
UpperCamelCase : Dict = anno[3] - anno[1]
UpperCamelCase : Union[str, Any] = anno[4] - anno[2]
UpperCamelCase : Optional[int] = anno[1] + width / 2
UpperCamelCase : Tuple = anno[2] + height / 2
UpperCamelCase : Dict = F"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(_lowerCAmelCase )
with open(F"""{file_root}.txt""" , "w" ) as outfile:
outfile.write("\n".join(line for line in annos_list ) )
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[list, list]:
UpperCamelCase : int = []
UpperCamelCase : Tuple = []
for label_file in glob.glob(os.path.join(_lowerCAmelCase , "*.txt" ) ):
UpperCamelCase : Union[str, Any] = label_file.split(os.sep )[-1].rsplit("." , 1 )[0]
with open(_lowerCAmelCase ) as in_file:
UpperCamelCase : int = in_file.readlines()
UpperCamelCase : Optional[Any] = os.path.join(_lowerCAmelCase , F"""{label_name}.jpg""" )
UpperCamelCase : str = []
for obj_list in obj_lists:
UpperCamelCase : Optional[int] = obj_list.rstrip("\n" ).split(" " )
UpperCamelCase : Dict = float(obj[1] ) - float(obj[3] ) / 2
UpperCamelCase : List[str] = float(obj[2] ) - float(obj[4] ) / 2
UpperCamelCase : Dict = float(obj[1] ) + float(obj[3] ) / 2
UpperCamelCase : int = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(_lowerCAmelCase )
labels.append(_lowerCAmelCase )
return img_paths, labels
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0.0 , ) -> tuple[list, list, str]:
UpperCamelCase : str = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
UpperCamelCase : List[Any] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
UpperCamelCase : Dict = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
UpperCamelCase : Dict = int(scale_x * output_size[1] )
UpperCamelCase : Tuple = int(scale_y * output_size[0] )
UpperCamelCase : Union[str, Any] = []
UpperCamelCase : int = []
for i, index in enumerate(_lowerCAmelCase ):
UpperCamelCase : str = all_img_list[index]
path_list.append(_lowerCAmelCase )
UpperCamelCase : Tuple = all_annos[index]
UpperCamelCase : Union[str, Any] = cva.imread(_lowerCAmelCase )
if i == 0: # top-left
UpperCamelCase : int = cva.resize(_lowerCAmelCase , (divid_point_x, divid_point_y) )
UpperCamelCase : int = img
for bbox in img_annos:
UpperCamelCase : Any = bbox[1] * scale_x
UpperCamelCase : Optional[Any] = bbox[2] * scale_y
UpperCamelCase : str = bbox[3] * scale_x
UpperCamelCase : Any = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
UpperCamelCase : Any = cva.resize(_lowerCAmelCase , (output_size[1] - divid_point_x, divid_point_y) )
UpperCamelCase : str = img
for bbox in img_annos:
UpperCamelCase : str = scale_x + bbox[1] * (1 - scale_x)
UpperCamelCase : Tuple = bbox[2] * scale_y
UpperCamelCase : int = scale_x + bbox[3] * (1 - scale_x)
UpperCamelCase : str = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
UpperCamelCase : Dict = cva.resize(_lowerCAmelCase , (divid_point_x, output_size[0] - divid_point_y) )
UpperCamelCase : Dict = img
for bbox in img_annos:
UpperCamelCase : str = bbox[1] * scale_x
UpperCamelCase : List[str] = scale_y + bbox[2] * (1 - scale_y)
UpperCamelCase : Dict = bbox[3] * scale_x
UpperCamelCase : Optional[int] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
UpperCamelCase : str = cva.resize(
_lowerCAmelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
UpperCamelCase : Optional[int] = img
for bbox in img_annos:
UpperCamelCase : Dict = scale_x + bbox[1] * (1 - scale_x)
UpperCamelCase : Any = scale_y + bbox[2] * (1 - scale_y)
UpperCamelCase : int = scale_x + bbox[3] * (1 - scale_x)
UpperCamelCase : Union[str, Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
UpperCamelCase : List[str] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def A_ ( _lowerCAmelCase ) -> str:
assert number_char > 1, "The number of character should greater than 1"
UpperCamelCase : Optional[Any] = ascii_lowercase + digits
return "".join(random.choice(_lowerCAmelCase ) for _ in range(_lowerCAmelCase ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 52
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
a : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 56
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : Any ={'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : List[str] =[
'''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VanForImageClassification''',
'''VanModel''',
'''VanPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_van import (
VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
VanForImageClassification,
VanModel,
VanPreTrainedModel,
)
else:
import sys
a__ : Dict =_LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 53
|
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Optional[Any] , lowercase_ : float , lowercase_ : Callable , lowercase_ : int , lowercase_ : float = 1.0 , lowercase_ : str = None , ):
super().__init__()
snake_case_ = initial_learning_rate
snake_case_ = warmup_steps
snake_case_ = power
snake_case_ = decay_schedule_fn
snake_case_ = name
def __call__( self : Tuple , lowercase_ : str ):
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
snake_case_ = tf.cast(lowercase_ , tf.floataa )
snake_case_ = tf.cast(self.warmup_steps , tf.floataa )
snake_case_ = global_step_float / warmup_steps_float
snake_case_ = self.initial_learning_rate * tf.math.pow(lowercase_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=lowercase_ , )
def A_ ( self : Any ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 0.9, __UpperCAmelCase = 0.9_9_9, __UpperCAmelCase = 1e-8, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0, __UpperCAmelCase = None, ) -> List[str]:
'''simple docstring'''
snake_case_ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=__UpperCAmelCase, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=init_lr * min_lr_ratio, power=__UpperCAmelCase, )
if num_warmup_steps:
snake_case_ = WarmUp(
initial_learning_rate=__UpperCAmelCase, decay_schedule_fn=__UpperCAmelCase, warmup_steps=__UpperCAmelCase, )
if weight_decay_rate > 0.0:
snake_case_ = AdamWeightDecay(
learning_rate=__UpperCAmelCase, weight_decay_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''], include_in_weight_decay=__UpperCAmelCase, )
else:
snake_case_ = tf.keras.optimizers.Adam(
learning_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class a ( _lowerCamelCase ):
def __init__( self : Dict , lowercase_ : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , lowercase_ : float = 0.9 , lowercase_ : float = 0.999 , lowercase_ : float = 1e-7 , lowercase_ : bool = False , lowercase_ : float = 0.0 , lowercase_ : Optional[List[str]] = None , lowercase_ : Optional[List[str]] = None , lowercase_ : str = "AdamWeightDecay" , **lowercase_ : Optional[int] , ):
super().__init__(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
snake_case_ = weight_decay_rate
snake_case_ = include_in_weight_decay
snake_case_ = exclude_from_weight_decay
@classmethod
def A_ ( cls : Dict , lowercase_ : Union[str, Any] ):
snake_case_ = {'''WarmUp''': WarmUp}
return super(lowercase_ , cls ).from_config(lowercase_ , custom_objects=lowercase_ )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Optional[int] ):
super(lowercase_ , self )._prepare_local(lowercase_ , lowercase_ , lowercase_ )
snake_case_ = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def A_ ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any ):
snake_case_ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : str=None , **lowercase_ : List[str] ):
snake_case_ ,snake_case_ = list(zip(*lowercase_ ) )
return super(lowercase_ , self ).apply_gradients(zip(lowercase_ , lowercase_ ) , name=lowercase_ , **lowercase_ )
def A_ ( self : List[Any] , lowercase_ : str , lowercase_ : str , lowercase_ : Any ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
snake_case_ = apply_state or {}
snake_case_ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
snake_case_ = self._fallback_apply_state(lowercase_ , lowercase_ )
snake_case_ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : Optional[int]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_dense(lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : str , lowercase_ : List[Any]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_sparse(lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def A_ ( self : Optional[int] , lowercase_ : int ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return False
return True
class a ( _lowerCamelCase ):
def __init__( self : List[Any] ):
snake_case_ = []
snake_case_ = None
@property
def A_ ( self : Union[str, Any] ):
if self._accum_steps is None:
snake_case_ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def A_ ( self : Dict ):
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Any , lowercase_ : int ):
if not self._gradients:
snake_case_ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(lowercase_ ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(lowercase_ ) != len(self._gradients ):
raise ValueError(F"Expected {len(self._gradients )} gradients, but got {len(lowercase_ )}" )
for accum_gradient, gradient in zip(self._gradients , lowercase_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(lowercase_ )
self._accum_steps.assign_add(1 )
def A_ ( self : Optional[int] ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(lowercase_ ) )
| 56
| 0
|
"""simple docstring"""
# Copyright 2021 The HuggingFace 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 pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
a__ : Tuple = '''Create a default config file for Accelerate with only a few flags set.'''
def UpperCAmelCase__ (lowerCAmelCase_="no" , lowerCAmelCase_ = default_json_config_file , lowerCAmelCase_ = False ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = Path(lowerCAmelCase_ )
path.parent.mkdir(parents=lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
if path.exists():
print(
f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.""" )
return False
__SCREAMING_SNAKE_CASE = mixed_precision.lower()
if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
raise ValueError(
f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}""" )
__SCREAMING_SNAKE_CASE = {
"compute_environment": "LOCAL_MACHINE",
"mixed_precision": mixed_precision,
}
if torch.cuda.is_available():
__SCREAMING_SNAKE_CASE = torch.cuda.device_count()
__SCREAMING_SNAKE_CASE = num_gpus
__SCREAMING_SNAKE_CASE = False
if num_gpus > 1:
__SCREAMING_SNAKE_CASE = "MULTI_GPU"
else:
__SCREAMING_SNAKE_CASE = "NO"
elif is_xpu_available() and use_xpu:
__SCREAMING_SNAKE_CASE = torch.xpu.device_count()
__SCREAMING_SNAKE_CASE = num_xpus
__SCREAMING_SNAKE_CASE = False
if num_xpus > 1:
__SCREAMING_SNAKE_CASE = "MULTI_XPU"
else:
__SCREAMING_SNAKE_CASE = "NO"
elif is_npu_available():
__SCREAMING_SNAKE_CASE = torch.npu.device_count()
__SCREAMING_SNAKE_CASE = num_npus
__SCREAMING_SNAKE_CASE = False
if num_npus > 1:
__SCREAMING_SNAKE_CASE = "MULTI_NPU"
else:
__SCREAMING_SNAKE_CASE = "NO"
else:
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = "NO"
__SCREAMING_SNAKE_CASE = ClusterConfig(**lowerCAmelCase_ )
config.to_json_file(lowerCAmelCase_ )
return path
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = parser.add_parser("default" , parents=lowerCAmelCase_ , help=lowerCAmelCase_ , formatter_class=lowerCAmelCase_ )
parser.add_argument(
"--config_file" , default=lowerCAmelCase_ , help=(
"The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
"location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
"such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
"with 'huggingface'."
) , dest="save_location" , )
parser.add_argument(
"--mixed_precision" , choices=["no", "fp16", "bf16"] , type=lowerCAmelCase_ , help="Whether or not to use mixed precision training. "
"Choose between FP16 and BF16 (bfloat16) training. "
"BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later." , default="no" , )
parser.set_defaults(func=lowerCAmelCase_ )
return parser
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = write_basic_config(args.mixed_precision , args.save_location )
if config_file:
print(f"""accelerate configuration saved at {config_file}""" )
| 54
|
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = "sample"
snake_case_ = 1e-2
@property
def A_ ( self : Dict ):
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
return {"sample": image}
@property
def A_ ( self : List[Any] ):
return (3, 32, 32)
@property
def A_ ( self : Dict ):
return (3, 32, 32)
def A_ ( self : Union[str, Any] ):
snake_case_ = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def A_ ( self : Any ):
pass
def A_ ( self : str ):
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def A_ ( self : Dict ):
# enable deterministic behavior for gradient checkpointing
snake_case_ ,snake_case_ = self.prepare_init_args_and_inputs_for_common()
snake_case_ = self.model_class(**lowercase_ )
model.to(lowercase_ )
assert not model.is_gradient_checkpointing and model.training
snake_case_ = model(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
snake_case_ = torch.randn_like(lowercase_ )
snake_case_ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
snake_case_ = self.model_class(**lowercase_ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase_ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
snake_case_ = model_a(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
snake_case_ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
snake_case_ = dict(model.named_parameters() )
snake_case_ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def A_ ( self : Tuple ):
snake_case_ ,snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(lowercase_ )
snake_case_ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def A_ ( self : Tuple ):
snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
snake_case_ = model.to(lowercase_ )
model.eval()
if torch_device == "mps":
snake_case_ = torch.manual_seed(0 )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(0 )
snake_case_ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case_ = image.to(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , sample_posterior=lowercase_ , generator=lowercase_ ).sample
snake_case_ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
snake_case_ = torch.tensor(
[
-4.0_078e-01,
-3.8_323e-04,
-1.2_681e-01,
-1.1_462e-01,
2.0_095e-01,
1.0_893e-01,
-8.8_247e-02,
-3.0_361e-01,
-9.8_644e-03,
] )
elif torch_device == "cpu":
snake_case_ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
snake_case_ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1e-2 ) )
@slow
class a ( unittest.TestCase ):
def A_ ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] ):
return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_ ) for s in shape] )}.npy"
def A_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Dict , lowercase_ : List[Any]=0 , lowercase_ : Union[str, Any]=(4, 3, 512, 512) , lowercase_ : Optional[Any]=False ):
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_ , lowercase_ ) ) ).to(lowercase_ ).to(lowercase_ )
return image
def A_ ( self : Any , lowercase_ : Dict="CompVis/stable-diffusion-v1-4" , lowercase_ : List[str]=False ):
snake_case_ = '''fp16''' if fpaa else None
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = AutoencoderKL.from_pretrained(
lowercase_ , subfolder='''vae''' , torch_dtype=lowercase_ , revision=lowercase_ , )
model.to(lowercase_ ).eval()
return model
def A_ ( self : Any , lowercase_ : int=0 ):
if torch_device == "mps":
return torch.manual_seed(lowercase_ )
return torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , fpaa=lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[Any] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : List[str] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model.encode(lowercase_ ).latent_dist
snake_case_ = dist.sample(generator=lowercase_ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
snake_case_ = sample[0, -1, -3:, -3:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
snake_case_ = 3e-3 if torch_device != '''mps''' else 1e-2
assert torch_all_close(lowercase_ , lowercase_ , atol=lowercase_ )
| 56
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
a_ : Dict = {
"""google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
"""google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = "mobilenet_v1"
def __init__( self , UpperCamelCase=3 , UpperCamelCase=224 , UpperCamelCase=1.0 , UpperCamelCase=8 , UpperCamelCase="relu6" , UpperCamelCase=True , UpperCamelCase=0.999 , UpperCamelCase=0.02 , UpperCamelCase=0.001 , **UpperCamelCase , ):
"""simple docstring"""
super().__init__(**UpperCamelCase )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
lowerCamelCase_ = num_channels
lowerCamelCase_ = image_size
lowerCamelCase_ = depth_multiplier
lowerCamelCase_ = min_depth
lowerCamelCase_ = hidden_act
lowerCamelCase_ = tf_padding
lowerCamelCase_ = classifier_dropout_prob
lowerCamelCase_ = initializer_range
lowerCamelCase_ = layer_norm_eps
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = version.parse("1.11" )
@property
def snake_case ( self ):
"""simple docstring"""
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def snake_case ( self ):
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def snake_case ( self ):
"""simple docstring"""
return 1e-4
| 55
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class a ( _lowerCamelCase ):
snake_case_ = 42
@flax_register_to_config
class a ( nn.Module , _lowerCamelCase , _lowerCamelCase ):
snake_case_ = 32
snake_case_ = 4
snake_case_ = 4
snake_case_ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
snake_case_ = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
snake_case_ = False
snake_case_ = (320, 640, 1_280, 1_280)
snake_case_ = 2
snake_case_ = 8
snake_case_ = None
snake_case_ = 1_280
snake_case_ = 0.0
snake_case_ = False
snake_case_ = jnp.floataa
snake_case_ = True
snake_case_ = 0
snake_case_ = False
def A_ ( self : Optional[int] , lowercase_ : jax.random.KeyArray ):
# init input tensors
snake_case_ = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case_ = jnp.zeros(lowercase_ , dtype=jnp.floataa )
snake_case_ = jnp.ones((1,) , dtype=jnp.intaa )
snake_case_ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case_ ,snake_case_ = jax.random.split(lowercase_ )
snake_case_ = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(lowercase_ , lowercase_ , lowercase_ , lowercase_ )["params"]
def A_ ( self : List[str] ):
snake_case_ = self.block_out_channels
snake_case_ = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
snake_case_ = self.num_attention_heads or self.attention_head_dim
# input
snake_case_ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case_ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case_ = FlaxTimestepEmbedding(lowercase_ , dtype=self.dtype )
snake_case_ = self.only_cross_attention
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case_ = []
snake_case_ = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
snake_case_ = output_channel
snake_case_ = block_out_channels[i]
snake_case_ = i == len(lowercase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case_ = FlaxCrossAttnDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowercase_ )
snake_case_ = down_blocks
# mid
snake_case_ = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
snake_case_ = []
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
snake_case_ = output_channel
snake_case_ = reversed_block_out_channels[i]
snake_case_ = reversed_block_out_channels[min(i + 1 , len(lowercase_ ) - 1 )]
snake_case_ = i == len(lowercase_ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
snake_case_ = FlaxCrossAttnUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(lowercase_ )
snake_case_ = output_channel
snake_case_ = up_blocks
# out
snake_case_ = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
snake_case_ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int , lowercase_ : Any , lowercase_ : int=None , lowercase_ : Any=None , lowercase_ : bool = True , lowercase_ : bool = False , ):
# 1. time
if not isinstance(lowercase_ , jnp.ndarray ):
snake_case_ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowercase_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case_ = timesteps.astype(dtype=jnp.floataa )
snake_case_ = jnp.expand_dims(lowercase_ , 0 )
snake_case_ = self.time_proj(lowercase_ )
snake_case_ = self.time_embedding(lowercase_ )
# 2. pre-process
snake_case_ = jnp.transpose(lowercase_ , (0, 2, 3, 1) )
snake_case_ = self.conv_in(lowercase_ )
# 3. down
snake_case_ = (sample,)
for down_block in self.down_blocks:
if isinstance(lowercase_ , lowercase_ ):
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
else:
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
snake_case_ = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowercase_ , lowercase_ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
snake_case_ = new_down_block_res_samples
# 4. mid
snake_case_ = self.mid_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
snake_case_ = down_block_res_samples[-(self.layers_per_block + 1) :]
snake_case_ = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = up_block(
lowercase_ , temb=lowercase_ , encoder_hidden_states=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train , )
else:
snake_case_ = up_block(lowercase_ , temb=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train )
# 6. post-process
snake_case_ = self.conv_norm_out(lowercase_ )
snake_case_ = nn.silu(lowercase_ )
snake_case_ = self.conv_out(lowercase_ )
snake_case_ = jnp.transpose(lowercase_ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowercase_ )
| 56
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|
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCamelCase :
'''simple docstring'''
__UpperCAmelCase : int
__UpperCAmelCase : Node | None =None
__UpperCAmelCase : Node | None =None
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = Node(1 )
__lowerCAmelCase = Node(2 )
__lowerCAmelCase = Node(3 )
__lowerCAmelCase = Node(4 )
__lowerCAmelCase = Node(5 )
return tree
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = []
if root is None:
return output
__lowerCAmelCase = deque([root] )
while process_queue:
__lowerCAmelCase = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = []
def populate_output(_UpperCamelCase , _UpperCamelCase ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(_UpperCamelCase , _UpperCamelCase )
return output
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = []
def populate_output(_UpperCamelCase , _UpperCamelCase ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(_UpperCamelCase , _UpperCamelCase )
return output
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
if root is None:
return []
__lowerCAmelCase = []
__lowerCAmelCase = 0
__lowerCAmelCase = height(_UpperCamelCase )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(_UpperCamelCase , _UpperCamelCase ) )
__lowerCAmelCase = 1
else:
output.append(get_nodes_from_right_to_left(_UpperCamelCase , _UpperCamelCase ) )
__lowerCAmelCase = 0
return output
def _lowerCamelCase ( ): # Main function for testing.
'''simple docstring'''
__lowerCAmelCase = make_tree()
print(f"In-order Traversal: {inorder(_UpperCamelCase )}" )
print(f"Pre-order Traversal: {preorder(_UpperCamelCase )}" )
print(f"Post-order Traversal: {postorder(_UpperCamelCase )}" , "\n" )
print(f"Height of Tree: {height(_UpperCamelCase )}" , "\n" )
print("Complete Level Order Traversal: " )
print(level_order(_UpperCamelCase ) , "\n" )
print("Level-wise order Traversal: " )
for level in range(1 , height(_UpperCamelCase ) + 1 ):
print(f"Level {level}:" , get_nodes_from_left_to_right(_UpperCamelCase , level=_UpperCamelCase ) )
print("\nZigZag order Traversal: " )
print(zigzag(_UpperCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57
|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a : Dict = (720, 1280) # Height, Width
a : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a : Dict = 1 / 100
a : str = ''
a : Any = ''
a : Optional[int] = ''
a : List[str] = 250
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dataset(__UpperCAmelCase, __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
snake_case_ = random.sample(range(len(__UpperCAmelCase ) ), 4 )
snake_case_ ,snake_case_ ,snake_case_ = update_image_and_anno(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, filter_scale=__UpperCAmelCase, )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ = random_chars(32 )
snake_case_ = path.split(os.sep )[-1].rsplit('''.''', 1 )[0]
snake_case_ = F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(F"{file_root}.jpg", __UpperCAmelCase, [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
snake_case_ = []
for anno in new_annos:
snake_case_ = anno[3] - anno[1]
snake_case_ = anno[4] - anno[2]
snake_case_ = anno[1] + width / 2
snake_case_ = anno[2] + height / 2
snake_case_ = F"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(F"{file_root}.txt", '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> tuple[list, list]:
'''simple docstring'''
snake_case_ = []
snake_case_ = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase, '''*.txt''' ) ):
snake_case_ = label_file.split(os.sep )[-1].rsplit('''.''', 1 )[0]
with open(__UpperCAmelCase ) as in_file:
snake_case_ = in_file.readlines()
snake_case_ = os.path.join(__UpperCAmelCase, F"{label_name}.jpg" )
snake_case_ = []
for obj_list in obj_lists:
snake_case_ = obj_list.rstrip('''\n''' ).split(''' ''' )
snake_case_ = float(obj[1] ) - float(obj[3] ) / 2
snake_case_ = float(obj[2] ) - float(obj[4] ) / 2
snake_case_ = float(obj[1] ) + float(obj[3] ) / 2
snake_case_ = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, ) -> tuple[list, list, str]:
'''simple docstring'''
snake_case_ = np.zeros([output_size[0], output_size[1], 3], dtype=np.uinta )
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = int(scale_x * output_size[1] )
snake_case_ = int(scale_y * output_size[0] )
snake_case_ = []
snake_case_ = []
for i, index in enumerate(__UpperCAmelCase ):
snake_case_ = all_img_list[index]
path_list.append(__UpperCAmelCase )
snake_case_ = all_annos[index]
snake_case_ = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = bbox[2] * scale_y
snake_case_ = bbox[3] * scale_x
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case_ = cva.resize(__UpperCAmelCase, (output_size[1] - divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = bbox[2] * scale_y
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = bbox[3] * scale_x
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case_ = cva.resize(
__UpperCAmelCase, (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case_ = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case_ = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 56
| 0
|
'''simple docstring'''
from __future__ import annotations
lowercase_ = 10
def lowerCamelCase ( __lowerCamelCase : list[int] ) ->list[int]:
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = max(__lowerCamelCase )
while placement <= max_digit:
# declare and initialize empty buckets
_SCREAMING_SNAKE_CASE = [[] for _ in range(__lowerCamelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
_SCREAMING_SNAKE_CASE = int((i / placement) % RADIX )
buckets[tmp].append(__lowerCamelCase )
# put each buckets' contents into list_of_ints
_SCREAMING_SNAKE_CASE = 0
for b in range(__lowerCamelCase ):
for i in buckets[b]:
_SCREAMING_SNAKE_CASE = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class a :
@staticmethod
def A_ ( *lowercase_ : int , **lowercase_ : str ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class a ( unittest.TestCase ):
snake_case_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def A_ ( self : Any , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str] ):
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , image_processor=lowercase_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def A_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : int ):
snake_case_ = object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' , threshold=0.0 )
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = [
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
]
snake_case_ = object_detector(lowercase_ , threshold=0.0 )
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for outputs in batch_outputs:
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
@require_tf
@unittest.skip('''Object detection not implemented in TF''' )
def A_ ( self : int ):
pass
@require_torch
def A_ ( self : Tuple ):
snake_case_ = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
] , )
@require_torch
@slow
def A_ ( self : Optional[int] ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : Tuple ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : str ):
snake_case_ = 0.9985
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=lowercase_ )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def A_ ( self : Dict ):
snake_case_ = '''Narsil/layoutlmv3-finetuned-funsd'''
snake_case_ = 0.9993
snake_case_ = pipeline('''object-detection''' , model=lowercase_ , threshold=lowercase_ )
snake_case_ = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
] , )
| 56
| 0
|
import math
def UpperCamelCase ( __lowerCamelCase : float , __lowerCamelCase : float ):
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError("In Malus Law, the angle is in the range 0-360 degrees" )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(__lowerCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="""malus_law""")
| 59
|
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class a :
def __init__( self : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any=13 , lowercase_ : Optional[Any]=7 , lowercase_ : Optional[Any]=True , lowercase_ : Dict=True , lowercase_ : Tuple=False , lowercase_ : Optional[Any]=True , lowercase_ : Any=99 , lowercase_ : Union[str, Any]=64 , lowercase_ : str=5 , lowercase_ : int=4 , lowercase_ : List[Any]=64 , lowercase_ : Dict="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : List[Any]=16 , lowercase_ : str=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
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_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def A_ ( self : List[str] ):
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def A_ ( self : str ):
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_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Tuple ):
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def A_ ( self : Any , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int] ):
snake_case_ = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 : Tuple , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.num_labels
snake_case_ = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Any , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.num_choices
snake_case_ = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : int , lowercase_ : List[str] ):
snake_case_ = self.num_labels
snake_case_ = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Union[str, Any] ):
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_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = True
def A_ ( self : Tuple ):
snake_case_ = MPNetModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def A_ ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ):
snake_case_ = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
snake_case_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
snake_case_ = model(lowercase_ )[0]
snake_case_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowercase_ )
snake_case_ = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1e-4 ) )
| 56
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case_( a__ , a__ , a__ , unittest.TestCase ):
__UpperCamelCase = StableDiffusionInpaintPipeline
__UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
__UpperCamelCase = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__UpperCamelCase = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__UpperCamelCase = frozenset([] )
def lowerCamelCase__ ( self : Optional[Any] ):
torch.manual_seed(0 )
lowerCAmelCase : Dict = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=9 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=UpperCamelCase_ , )
lowerCAmelCase : List[Any] = PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
torch.manual_seed(0 )
lowerCAmelCase : Union[str, Any] = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
lowerCAmelCase : Union[str, Any] = 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 , hidden_act='''gelu''' , projection_dim=5_1_2 , )
lowerCAmelCase : Any = CLIPTextModel(UpperCamelCase_ )
lowerCAmelCase : str = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
lowerCAmelCase : int = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowerCamelCase__ ( self : List[str] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Dict=0 ):
# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
lowerCAmelCase : Any = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
lowerCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCAmelCase : Union[str, Any] = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((6_4, 6_4) )
lowerCAmelCase : Any = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((6_4, 6_4) )
if str(UpperCamelCase_ ).startswith('''mps''' ):
lowerCAmelCase : Optional[Any] = torch.manual_seed(UpperCamelCase_ )
else:
lowerCAmelCase : Dict = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
lowerCAmelCase : Optional[int] = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': init_image,
'''mask_image''': mask_image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowerCamelCase__ ( self : Dict ):
lowerCAmelCase : Union[str, Any] = '''cpu''' # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase : Dict = self.get_dummy_components()
lowerCAmelCase : Any = StableDiffusionInpaintPipeline(**UpperCamelCase_ )
lowerCAmelCase : Optional[Any] = sd_pipe.to(UpperCamelCase_ )
sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )
lowerCAmelCase : Optional[int] = self.get_dummy_inputs(UpperCamelCase_ )
lowerCAmelCase : Tuple = sd_pipe(**UpperCamelCase_ ).images
lowerCAmelCase : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
lowerCAmelCase : Optional[Any] = np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase__ ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class snake_case_( unittest.TestCase ):
def lowerCamelCase__ ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self : str ):
lowerCAmelCase : Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowerCAmelCase : Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowerCAmelCase : List[str] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench.npy''' )
lowerCAmelCase : Union[str, Any] = '''stabilityai/stable-diffusion-2-inpainting'''
lowerCAmelCase : Tuple = StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_ , safety_checker=UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
lowerCAmelCase : int = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowerCAmelCase : List[str] = torch.manual_seed(0 )
lowerCAmelCase : int = pipe(
prompt=UpperCamelCase_ , image=UpperCamelCase_ , mask_image=UpperCamelCase_ , generator=UpperCamelCase_ , output_type='''np''' , )
lowerCAmelCase : Optional[int] = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 9E-3
def lowerCamelCase__ ( self : Tuple ):
lowerCAmelCase : Optional[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowerCAmelCase : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowerCAmelCase : int = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
'''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )
lowerCAmelCase : int = '''stabilityai/stable-diffusion-2-inpainting'''
lowerCAmelCase : Any = StableDiffusionInpaintPipeline.from_pretrained(
UpperCamelCase_ , torch_dtype=torch.floataa , safety_checker=UpperCamelCase_ , )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing()
lowerCAmelCase : str = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowerCAmelCase : Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase : Tuple = pipe(
prompt=UpperCamelCase_ , image=UpperCamelCase_ , mask_image=UpperCamelCase_ , generator=UpperCamelCase_ , output_type='''np''' , )
lowerCAmelCase : Tuple = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def lowerCamelCase__ ( self : Any ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCAmelCase : Tuple = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
lowerCAmelCase : Any = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
lowerCAmelCase : Union[str, Any] = '''stabilityai/stable-diffusion-2-inpainting'''
lowerCAmelCase : List[str] = PNDMScheduler.from_pretrained(UpperCamelCase_ , subfolder='''scheduler''' )
lowerCAmelCase : Optional[int] = StableDiffusionInpaintPipeline.from_pretrained(
UpperCamelCase_ , safety_checker=UpperCamelCase_ , scheduler=UpperCamelCase_ , torch_dtype=torch.floataa , )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCAmelCase : int = '''Face of a yellow cat, high resolution, sitting on a park bench'''
lowerCAmelCase : Tuple = torch.manual_seed(0 )
lowerCAmelCase : Dict = pipe(
prompt=UpperCamelCase_ , image=UpperCamelCase_ , mask_image=UpperCamelCase_ , generator=UpperCamelCase_ , num_inference_steps=2 , output_type='''np''' , )
lowerCAmelCase : List[str] = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 1_0**9
| 60
|
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class a ( _lowerCamelCase ):
def A_ ( self : str ):
snake_case_ = tempfile.mkdtemp()
snake_case_ = 8
# DPR tok
snake_case_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
snake_case_ = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
snake_case_ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
snake_case_ = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
snake_case_ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case_ = {'''unk_token''': '''<unk>'''}
snake_case_ = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def A_ ( self : Union[str, Any] ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : Union[str, Any] ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : int ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def A_ ( self : str ):
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def A_ ( self : str ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def A_ ( self : str , lowercase_ : bool ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
snake_case_ = os.path.join(self.tmpdirname , '''dataset''' )
snake_case_ = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , lowercase_ ) , )
return retriever
def A_ ( self : Tuple ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
snake_case_ = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
snake_case_ = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
snake_case_ = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(lowercase_ , open(lowercase_ , '''wb''' ) )
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def A_ ( self : Optional[Any] ):
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : str ):
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = self.get_dummy_dataset()
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : int ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : str ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : Any ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : Any ):
snake_case_ = 1
snake_case_ = self.get_dummy_legacy_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : List[str] ):
import torch
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
snake_case_ ,snake_case_ ,snake_case_ = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , np.ndarray )
snake_case_ = retriever(
lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ , return_tensors='''pt''' , )
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : Tuple ):
snake_case_ = self.get_dpr_ctx_encoder_tokenizer()
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
retriever.set_ctx_encoder_tokenizer(lowercase_ )
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
self.assertEqual(
len(lowercase_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , lowercase_ ) # check for doc token related keys in dictionary.
| 56
| 0
|
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class A_ (lowercase__ ,unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = MobileBertTokenizer
SCREAMING_SNAKE_CASE__ : int = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE__ : str = True
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
SCREAMING_SNAKE_CASE__ : Optional[int] = filter_non_english
SCREAMING_SNAKE_CASE__ : List[str] = """google/mobilebert-uncased"""
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().setUp()
UpperCAmelCase_ : Union[str, Any] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
UpperCAmelCase_ : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
UpperCAmelCase_ : Any = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase__ ( self , lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = "UNwant\u00E9d,running"
UpperCAmelCase_ : Dict = "unwanted, running"
return input_text, output_text
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = self.tokenizer_class(self.vocab_file )
UpperCAmelCase_ : List[str] = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(lowercase_ , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCAmelCase_ : List[str] = self.get_tokenizer()
UpperCAmelCase_ : Any = self.get_rust_tokenizer()
UpperCAmelCase_ : Dict = "UNwant\u00E9d,running"
UpperCAmelCase_ : Optional[int] = tokenizer.tokenize(lowercase_ )
UpperCAmelCase_ : List[str] = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase_ : List[Any] = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase_ : Dict = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[int] = self.get_rust_tokenizer()
UpperCAmelCase_ : str = tokenizer.encode(lowercase_ )
UpperCAmelCase_ : Optional[Any] = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# With lower casing
UpperCAmelCase_ : Union[str, Any] = self.get_tokenizer(do_lower_case=lowercase_ )
UpperCAmelCase_ : Tuple = self.get_rust_tokenizer(do_lower_case=lowercase_ )
UpperCAmelCase_ : int = "UNwant\u00E9d,running"
UpperCAmelCase_ : Dict = tokenizer.tokenize(lowercase_ )
UpperCAmelCase_ : Optional[int] = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase_ : str = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase_ : List[Any] = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase_ : str = self.get_rust_tokenizer()
UpperCAmelCase_ : Dict = tokenizer.encode(lowercase_ )
UpperCAmelCase_ : Optional[Any] = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Any = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : int = BasicTokenizer(do_lower_case=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = BasicTokenizer(do_lower_case=lowercase_ , strip_accents=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = BasicTokenizer(do_lower_case=lowercase_ , strip_accents=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = BasicTokenizer(do_lower_case=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = BasicTokenizer(do_lower_case=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = BasicTokenizer(do_lower_case=lowercase_ , strip_accents=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = BasicTokenizer(do_lower_case=lowercase_ , strip_accents=lowercase_ )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = BasicTokenizer(do_lower_case=lowercase_ , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
UpperCAmelCase_ : str = {}
for i, token in enumerate(lowercase_ ):
UpperCAmelCase_ : str = i
UpperCAmelCase_ : str = WordpieceTokenizer(vocab=lowercase_ , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.get_tokenizer()
UpperCAmelCase_ : List[str] = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(lowercase_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(lowercase_ ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
UpperCAmelCase_ : str = tokenizer.encode("sequence builders" , add_special_tokens=lowercase_ )
UpperCAmelCase_ : Dict = tokenizer.encode("multi-sequence build" , add_special_tokens=lowercase_ )
UpperCAmelCase_ : List[str] = tokenizer.build_inputs_with_special_tokens(lowercase_ )
UpperCAmelCase_ : List[Any] = tokenizer.build_inputs_with_special_tokens(lowercase_ , lowercase_ )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def UpperCamelCase__ ( self ):
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase_ : List[str] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase_ : Optional[int] = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
UpperCAmelCase_ : Optional[int] = tokenizer_r.encode_plus(
lowercase_ , return_attention_mask=lowercase_ , return_token_type_ids=lowercase_ , return_offsets_mapping=lowercase_ , add_special_tokens=lowercase_ , )
UpperCAmelCase_ : Tuple = tokenizer_r.do_lower_case if hasattr(lowercase_ , "do_lower_case" ) else False
UpperCAmelCase_ : Any = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Dict = ["的", "人", "有"]
UpperCAmelCase_ : List[str] = "".join(lowercase_ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
UpperCAmelCase_ : Any = True
UpperCAmelCase_ : List[Any] = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase_ : Optional[Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase_ : Optional[Any] = tokenizer_p.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase_ : List[str] = tokenizer_r.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase_ : Optional[int] = tokenizer_r.convert_ids_to_tokens(lowercase_ )
UpperCAmelCase_ : List[str] = tokenizer_p.convert_ids_to_tokens(lowercase_ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[int] = False
UpperCAmelCase_ : List[Any] = self.rust_tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase_ : str = self.tokenizer_class.from_pretrained(lowercase_ , **lowercase_ )
UpperCAmelCase_ : int = tokenizer_r.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase_ : Optional[int] = tokenizer_p.encode(lowercase_ , add_special_tokens=lowercase_ )
UpperCAmelCase_ : str = tokenizer_r.convert_ids_to_tokens(lowercase_ )
UpperCAmelCase_ : str = tokenizer_p.convert_ids_to_tokens(lowercase_ )
# it is expected that only the first Chinese character is not preceded by "##".
UpperCAmelCase_ : List[str] = [
F"""##{token}""" if idx != 0 else token for idx, token in enumerate(lowercase_ )
]
self.assertListEqual(lowercase_ , lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
| 61
|
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
a : Dict = None
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
a : str = {
'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',
},
'tokenizer_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
},
}
# TODO(PVP) - this should be removed in Transformers v5
a : List[Any] = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
class a ( _lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = TaTokenizer
snake_case_ = []
def __init__( self : List[Any] , lowercase_ : int=None , lowercase_ : Dict=None , lowercase_ : Dict="</s>" , lowercase_ : List[Any]="<unk>" , lowercase_ : int="<pad>" , lowercase_ : int=100 , lowercase_ : List[Any]=None , **lowercase_ : List[str] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
snake_case_ = [F"<extra_id_{i}>" for i in range(lowercase_ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
snake_case_ = len(set(filter(lambda lowercase_ : bool('''extra_id_''' in str(lowercase_ ) ) , lowercase_ ) ) )
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''' )
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , extra_ids=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ = vocab_file
snake_case_ = False if not self.vocab_file else True
snake_case_ = extra_ids
@staticmethod
def A_ ( lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : int ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
snake_case_ = TaTokenizerFast.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.''' , lowercase_ , )
return max_model_length
def A_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
logger.info(F"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def A_ ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
snake_case_ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def A_ ( self : int , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = [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 A_ ( self : Dict ):
return list(
set(filter(lambda lowercase_ : bool(re.search(R'''<extra_id_\d+>''' , lowercase_ ) ) is not None , self.additional_special_tokens ) ) )
def A_ ( self : Any ):
return [self.convert_tokens_to_ids(lowercase_ ) for token in self.get_sentinel_tokens()]
| 56
| 0
|
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> List[Any]:
__UpperCamelCase =tempfile.mkdtemp()
__UpperCamelCase =SamImageProcessor()
__UpperCamelCase =SamProcessor(A_ )
processor.save_pretrained(self.tmpdirname )
def _a ( self , **A_ ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **A_ ).image_processor
def _a ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Tuple:
__UpperCamelCase =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__UpperCamelCase =[Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
__UpperCamelCase =SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__UpperCamelCase =self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
__UpperCamelCase =SamProcessor.from_pretrained(self.tmpdirname , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def _a ( self ) -> str:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =SamProcessor(image_processor=A_ )
__UpperCamelCase =self.prepare_image_inputs()
__UpperCamelCase =image_processor(A_ , return_tensors='np' )
__UpperCamelCase =processor(images=A_ , return_tensors='np' )
input_feat_extract.pop('original_sizes' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes' ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
@require_torch
def _a ( self ) -> Optional[int]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =SamProcessor(image_processor=A_ )
__UpperCamelCase =[torch.ones((1, 3, 5, 5) )]
__UpperCamelCase =[[1764, 2646]]
__UpperCamelCase =[[683, 1024]]
__UpperCamelCase =processor.post_process_masks(A_ , A_ , A_ )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
__UpperCamelCase =processor.post_process_masks(
A_ , torch.tensor(A_ ) , torch.tensor(A_ ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
__UpperCamelCase =[np.ones((1, 3, 5, 5) )]
__UpperCamelCase =processor.post_process_masks(A_ , np.array(A_ ) , np.array(A_ ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
__UpperCamelCase =[[1, 0], [0, 1]]
with self.assertRaises(A_ ):
__UpperCamelCase =processor.post_process_masks(A_ , np.array(A_ ) , np.array(A_ ) )
@require_vision
@require_tf
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> Any:
__UpperCamelCase =tempfile.mkdtemp()
__UpperCamelCase =SamImageProcessor()
__UpperCamelCase =SamProcessor(A_ )
processor.save_pretrained(self.tmpdirname )
def _a ( self , **A_ ) -> Any:
return AutoProcessor.from_pretrained(self.tmpdirname , **A_ ).image_processor
def _a ( self ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Any:
__UpperCamelCase =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__UpperCamelCase =[Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> Optional[int]:
__UpperCamelCase =SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__UpperCamelCase =self.get_image_processor(do_normalize=A_ , padding_value=1.0 )
__UpperCamelCase =SamProcessor.from_pretrained(self.tmpdirname , do_normalize=A_ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A_ )
def _a ( self ) -> Optional[Any]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =SamProcessor(image_processor=A_ )
__UpperCamelCase =self.prepare_image_inputs()
__UpperCamelCase =image_processor(A_ , return_tensors='np' )
__UpperCamelCase =processor(images=A_ , return_tensors='np' )
input_feat_extract.pop('original_sizes' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('reshaped_input_sizes' ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
@require_tf
def _a ( self ) -> str:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =SamProcessor(image_processor=A_ )
__UpperCamelCase =[tf.ones((1, 3, 5, 5) )]
__UpperCamelCase =[[1764, 2646]]
__UpperCamelCase =[[683, 1024]]
__UpperCamelCase =processor.post_process_masks(A_ , A_ , A_ , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
__UpperCamelCase =processor.post_process_masks(
A_ , tf.convert_to_tensor(A_ ) , tf.convert_to_tensor(A_ ) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
__UpperCamelCase =[np.ones((1, 3, 5, 5) )]
__UpperCamelCase =processor.post_process_masks(
A_ , np.array(A_ ) , np.array(A_ ) , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
__UpperCamelCase =[[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
__UpperCamelCase =processor.post_process_masks(
A_ , np.array(A_ ) , np.array(A_ ) , return_tensors='tf' )
@require_vision
@require_torchvision
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _a ( self ) -> Any:
__UpperCamelCase =tempfile.mkdtemp()
__UpperCamelCase =SamImageProcessor()
__UpperCamelCase =SamProcessor(A_ )
processor.save_pretrained(self.tmpdirname )
def _a ( self , **A_ ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname , **A_ ).image_processor
def _a ( self ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Dict:
__UpperCamelCase =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__UpperCamelCase =[Image.fromarray(np.moveaxis(A_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def _a ( self ) -> Dict:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =SamProcessor(image_processor=A_ )
__UpperCamelCase =np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
__UpperCamelCase =[tf.convert_to_tensor(A_ )]
__UpperCamelCase =[torch.tensor(A_ )]
__UpperCamelCase =[[1764, 2646]]
__UpperCamelCase =[[683, 1024]]
__UpperCamelCase =processor.post_process_masks(
A_ , A_ , A_ , return_tensors='tf' )
__UpperCamelCase =processor.post_process_masks(
A_ , A_ , A_ , return_tensors='pt' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def _a ( self ) -> Union[str, Any]:
__UpperCamelCase =self.get_image_processor()
__UpperCamelCase =SamProcessor(image_processor=A_ )
__UpperCamelCase =self.prepare_image_inputs()
__UpperCamelCase =image_processor(A_ , return_tensors='pt' )['pixel_values'].numpy()
__UpperCamelCase =processor(images=A_ , return_tensors='pt' )['pixel_values'].numpy()
__UpperCamelCase =image_processor(A_ , return_tensors='tf' )['pixel_values'].numpy()
__UpperCamelCase =processor(images=A_ , return_tensors='tf' )['pixel_values'].numpy()
self.assertTrue(np.allclose(A_ , A_ ) )
self.assertTrue(np.allclose(A_ , A_ ) )
self.assertTrue(np.allclose(A_ , A_ ) )
| 62
|
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(__UpperCAmelCase ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
return min(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ = [90, 23, 6, 33, 21, 65, 123, 3_4423]
snake_case_ = math.log(len(__UpperCAmelCase ), 2 )
print('''Optimal value : ''', end='''''' )
print(minimax(0, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 56
| 0
|
'''simple docstring'''
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 _lowerCamelCase ( lowercase : Any ) -> int:
_a = fname.split(os.path.sep )[-1]
return re.search(r"^(.*)_\d+\.jpg$" , lowercase ).groups()[0]
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Optional[Any] , __a : List[Any] , __a : List[str]=None , __a : List[Any]=None ):
_a = file_names
_a = image_transform
_a = label_to_id
def __len__( self : Any ):
return len(self.file_names )
def __getitem__( self : Optional[Any] , __a : str ):
_a = self.file_names[idx]
_a = PIL.Image.open(__a )
_a = raw_image.convert("RGB" )
if self.image_transform is not None:
_a = self.image_transform(__a )
_a = extract_label(__a )
if self.label_to_id is not None:
_a = self.label_to_id[label]
return {"image": image, "label": label}
def _lowerCamelCase ( lowercase : List[Any] , lowercase : int ) -> str:
# Initialize accelerator
if args.with_tracking:
_a = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir )
else:
_a = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_a = config["lr"]
_a = int(config["num_epochs"] )
_a = int(config["seed"] )
_a = int(config["batch_size"] )
_a = config["image_size"]
if not isinstance(lowercase , (list, tuple) ):
_a = (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":
_a = args.checkpointing_steps
elif args.checkpointing_steps.isdigit():
_a = int(args.checkpointing_steps )
else:
raise ValueError(
F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.' )
else:
_a = None
# We need to initialize the trackers we use, and also store our configuration
if args.with_tracking:
_a = os.path.split(lowercase )[-1].split("." )[0]
accelerator.init_trackers(lowercase , lowercase )
# Grab all the image filenames
_a = [os.path.join(args.data_dir , lowercase ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]
# Build the label correspondences
_a = [extract_label(lowercase ) for fname in file_names]
_a = list(set(lowercase ) )
id_to_label.sort()
_a = {lbl: i for i, lbl in enumerate(lowercase )}
# Set the seed before splitting the data.
np.random.seed(lowercase )
torch.manual_seed(lowercase )
torch.cuda.manual_seed_all(lowercase )
# Split our filenames between train and validation
_a = np.random.permutation(len(lowercase ) )
_a = int(0.8 * len(lowercase ) )
_a = random_perm[:cut]
_a = random_perm[cut:]
# For training we use a simple RandomResizedCrop
_a = Compose([RandomResizedCrop(lowercase , scale=(0.5, 1.0) ), ToTensor()] )
_a = PetsDataset(
[file_names[i] for i in train_split] , image_transform=lowercase , label_to_id=lowercase )
# For evaluation, we use a deterministic Resize
_a = Compose([Resize(lowercase ), ToTensor()] )
_a = PetsDataset([file_names[i] for i in eval_split] , image_transform=lowercase , label_to_id=lowercase )
# Instantiate dataloaders.
_a = DataLoader(lowercase , shuffle=lowercase , batch_size=lowercase , num_workers=4 )
_a = DataLoader(lowercase , shuffle=lowercase , batch_size=lowercase , num_workers=4 )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_a = create_model("resnet50d" , pretrained=lowercase , num_classes=len(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).
_a = model.to(accelerator.device )
# Freezing the base model
for param in model.parameters():
_a = False
for param in model.get_classifier().parameters():
_a = True
# We normalize the batches of images to be a bit faster.
_a = torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
_a = torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )
# Instantiate optimizer
_a = torch.optim.Adam(params=model.parameters() , lr=lr / 25 )
# Instantiate learning rate scheduler
_a = OneCycleLR(optimizer=lowercase , max_lr=lowercase , epochs=lowercase , steps_per_epoch=len(lowercase ) )
# 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.
_a , _a , _a , _a , _a = accelerator.prepare(
lowercase , lowercase , lowercase , lowercase , lowercase )
# We need to keep track of how many total steps we have iterated over
_a = 0
# We also need to keep track of the starting epoch so files are named properly
_a = 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 )
_a = os.path.basename(args.resume_from_checkpoint )
else:
# Get the most recent checkpoint
_a = [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
dirs.sort(key=os.path.getctime )
_a = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
# Extract `epoch_{i}` or `step_{i}`
_a = os.path.splitext(lowercase )[0]
if "epoch" in training_difference:
_a = int(training_difference.replace("epoch_" , "" ) ) + 1
_a = None
else:
_a = int(training_difference.replace("step_" , "" ) )
_a = resume_step // len(lowercase )
resume_step -= starting_epoch * len(lowercase )
# Now we train the model
for epoch in range(lowercase , lowercase ):
model.train()
if args.with_tracking:
_a = 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
_a = accelerator.skip_first_batches(lowercase , lowercase )
overall_step += resume_step
else:
# After the first iteration though, we need to go back to the original dataloader
_a = train_dataloader
for batch in active_dataloader:
# We could avoid this line since we set the accelerator with `device_placement=True`.
_a = {k: v.to(accelerator.device ) for k, v in batch.items()}
_a = (batch["image"] - mean) / std
_a = model(lowercase )
_a = torch.nn.functional.cross_entropy(lowercase , batch["label"] )
# We keep track of the loss at each epoch
if args.with_tracking:
total_loss += loss.detach().float()
accelerator.backward(lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
if isinstance(lowercase , lowercase ):
_a = F'step_{overall_step}'
if overall_step % checkpointing_steps == 0:
if args.output_dir is not None:
_a = os.path.join(args.output_dir , lowercase )
accelerator.save_state(lowercase )
model.eval()
_a = 0
_a = 0
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
_a = {k: v.to(accelerator.device ) for k, v in batch.items()}
_a = (batch["image"] - mean) / std
with torch.no_grad():
_a = model(lowercase )
_a = outputs.argmax(dim=-1 )
_a , _a = accelerator.gather_for_metrics((predictions, batch["label"]) )
_a = predictions == references
num_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
_a = 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(lowercase ),
"epoch": epoch,
} , step=lowercase , )
if checkpointing_steps == "epoch":
_a = F'epoch_{epoch}'
if args.output_dir is not None:
_a = os.path.join(args.output_dir , lowercase )
accelerator.save_state(lowercase )
if args.with_tracking:
accelerator.end_training()
def _lowerCamelCase ( ) -> List[str]:
_a = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument("--data_dir" , required=lowercase , 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=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." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
parser.add_argument(
"--checkpointing_steps" , type=lowercase , default=lowercase , 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=lowercase , 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=lowercase , default=lowercase , 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=lowercase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
_a = parser.parse_args()
_a = {"lr": 3E-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
training_function(lowercase , lowercase )
if __name__ == "__main__":
main()
| 63
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
elif "subsample" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ ,snake_case_ = emb.weight.shape
snake_case_ = nn.Linear(__UpperCAmelCase, __UpperCAmelCase, bias=__UpperCAmelCase )
snake_case_ = emb.weight.data
return lin_layer
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = torch.load(__UpperCAmelCase, map_location='''cpu''' )
snake_case_ = mam_aaa['''args''']
snake_case_ = mam_aaa['''model''']
snake_case_ = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__UpperCAmelCase )
rename_keys(__UpperCAmelCase )
snake_case_ = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case_ = args.share_decoder_input_output_embed
snake_case_ = [int(__UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case_ = SpeechaTextConfig(
vocab_size=__UpperCAmelCase, max_source_positions=args.max_source_positions, max_target_positions=args.max_target_positions, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', num_conv_layers=len(__UpperCAmelCase ), conv_channels=args.conv_channels, conv_kernel_sizes=__UpperCAmelCase, input_feat_per_channel=args.input_feat_per_channel, input_channels=args.input_channels, tie_word_embeddings=__UpperCAmelCase, num_beams=5, max_length=200, use_cache=__UpperCAmelCase, decoder_start_token_id=2, early_stopping=__UpperCAmelCase, )
snake_case_ = SpeechaTextForConditionalGeneration(__UpperCAmelCase )
snake_case_ ,snake_case_ = model.model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0 and not set(__UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F" but all the following weights are missing {missing}" )
if tie_embeds:
snake_case_ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ = lm_head_weights
model.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
a : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 56
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
# See all XGLM models at https://huggingface.co/models?filter=xglm
}
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "xglm"
lowercase__ = ["past_key_values"]
lowercase__ = {
"num_attention_heads": "attention_heads",
"hidden_size": "d_model",
"num_hidden_layers": "num_layers",
}
def __init__( self: Dict, a_: int=256_008, a_: List[str]=2_048, a_: Dict=1_024, a_: int=4_096, a_: List[Any]=24, a_: Any=16, a_: Dict="gelu", a_: Optional[Any]=0.1, a_: str=0.1, a_: Union[str, Any]=0.0, a_: List[str]=0.0, a_: List[Any]=0.02, a_: Dict=True, a_: int=True, a_: List[Any]=2, a_: str=1, a_: Optional[int]=0, a_: Tuple=2, **a_: Tuple, ):
'''simple docstring'''
_snake_case : Union[str, Any] = vocab_size
_snake_case : Optional[int] = max_position_embeddings
_snake_case : Union[str, Any] = d_model
_snake_case : Optional[int] = ffn_dim
_snake_case : List[Any] = num_layers
_snake_case : int = attention_heads
_snake_case : int = activation_function
_snake_case : List[str] = dropout
_snake_case : List[Any] = attention_dropout
_snake_case : Any = activation_dropout
_snake_case : Union[str, Any] = layerdrop
_snake_case : int = init_std
_snake_case : Optional[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
_snake_case : Union[str, Any] = use_cache
super().__init__(
pad_token_id=a_, bos_token_id=a_, eos_token_id=a_, decoder_start_token_id=a_, **a_, )
| 64
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : str ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 56
| 0
|
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class A :
def __init__(self : List[str] , __UpperCAmelCase : Optional[Any] , ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = parent
UpperCAmelCase__ = 1_3
UpperCAmelCase__ = 7
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = True
UpperCAmelCase__ = 9_9
UpperCAmelCase__ = 3_2
UpperCAmelCase__ = 2
UpperCAmelCase__ = 4
UpperCAmelCase__ = 3_7
UpperCAmelCase__ = "gelu"
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 0.1
UpperCAmelCase__ = 5_1_2
UpperCAmelCase__ = 1_6
UpperCAmelCase__ = 2
UpperCAmelCase__ = 0.02
UpperCAmelCase__ = 3
UpperCAmelCase__ = 4
UpperCAmelCase__ = None
def lowercase_ (self : Optional[Any] ) -> str:
"""simple docstring"""
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase__ = None
if self.use_input_mask:
UpperCAmelCase__ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase__ = None
UpperCAmelCase__ = None
UpperCAmelCase__ = None
if self.use_labels:
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase__ = ids_tensor([self.batch_size] , self.num_choices )
UpperCAmelCase__ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , 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 , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowercase_ (self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = self.prepare_config_and_inputs()
UpperCAmelCase__ = True
UpperCAmelCase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def lowercase_ (self : Any , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[str] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : int ) -> str:
"""simple docstring"""
UpperCAmelCase__ = TFEsmModel(config=__UpperCAmelCase )
UpperCAmelCase__ = {"input_ids": input_ids, "attention_mask": input_mask}
UpperCAmelCase__ = model(__UpperCAmelCase )
UpperCAmelCase__ = [input_ids, input_mask]
UpperCAmelCase__ = model(__UpperCAmelCase )
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ (self : Optional[Any] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple , ) -> List[str]:
"""simple docstring"""
UpperCAmelCase__ = True
UpperCAmelCase__ = TFEsmModel(config=__UpperCAmelCase )
UpperCAmelCase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
UpperCAmelCase__ = model(__UpperCAmelCase )
UpperCAmelCase__ = [input_ids, input_mask]
UpperCAmelCase__ = model(__UpperCAmelCase , encoder_hidden_states=__UpperCAmelCase )
# Also check the case where encoder outputs are not passed
UpperCAmelCase__ = model(__UpperCAmelCase , attention_mask=__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowercase_ (self : Optional[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : int , __UpperCAmelCase : List[str] , __UpperCAmelCase : Tuple , __UpperCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFEsmForMaskedLM(config=__UpperCAmelCase )
UpperCAmelCase__ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowercase_ (self : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.num_labels
UpperCAmelCase__ = TFEsmForTokenClassification(config=__UpperCAmelCase )
UpperCAmelCase__ = {"input_ids": input_ids, "attention_mask": input_mask}
UpperCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowercase_ (self : List[str] ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) = config_and_inputs
UpperCAmelCase__ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
__UpperCAmelCase : List[str] = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
__UpperCAmelCase : Optional[int] = (
{
'feature-extraction': TFEsmModel,
'fill-mask': TFEsmForMaskedLM,
'text-classification': TFEsmForSequenceClassification,
'token-classification': TFEsmForTokenClassification,
'zero-shot': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCAmelCase : str = False
__UpperCAmelCase : Union[str, Any] = False
def lowercase_ (self : Dict ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = TFEsmModelTester(self )
UpperCAmelCase__ = ConfigTester(self , config_class=__UpperCAmelCase , hidden_size=3_7 )
def lowercase_ (self : List[Any] ) -> int:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase_ (self : str ) -> str:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def lowercase_ (self : List[Any] ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__UpperCAmelCase )
def lowercase_ (self : Dict ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )
def lowercase_ (self : List[str] ) -> int:
"""simple docstring"""
UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )
@slow
def lowercase_ (self : List[Any] ) -> str:
"""simple docstring"""
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ = TFEsmModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@unittest.skip("Protein models do not support embedding resizing." )
def lowercase_ (self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip("Protein models do not support embedding resizing." )
def lowercase_ (self : Dict ) -> str:
"""simple docstring"""
pass
def lowercase_ (self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase__ , UpperCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ = model_class(__UpperCAmelCase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
UpperCAmelCase__ = model.get_bias()
assert isinstance(__UpperCAmelCase , __UpperCAmelCase )
for k, v in name.items():
assert isinstance(__UpperCAmelCase , tf.Variable )
else:
UpperCAmelCase__ = model.get_output_embeddings()
assert x is None
UpperCAmelCase__ = model.get_bias()
assert name is None
@require_tf
class A ( unittest.TestCase ):
@slow
def lowercase_ (self : Tuple ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
UpperCAmelCase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
UpperCAmelCase__ = model(__UpperCAmelCase )[0]
UpperCAmelCase__ = [1, 6, 3_3]
self.assertEqual(list(output.numpy().shape ) , __UpperCAmelCase )
# compare the actual values for a slice.
UpperCAmelCase__ = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def lowercase_ (self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
UpperCAmelCase__ = tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
UpperCAmelCase__ = model(__UpperCAmelCase )[0]
# compare the actual values for a slice.
UpperCAmelCase__ = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 65
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a : int = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
config.addinivalue_line(
'''markers''', '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''', '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''', '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''', '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''', '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''', '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
snake_case_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCAmelCase, id=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if exitstatus == 5:
snake_case_ = 0
# Doctest custom flag to ignore output.
a : Union[str, Any] = doctest.register_optionflag('IGNORE_RESULT')
a : Optional[int] = doctest.OutputChecker
class a ( _lowerCamelCase ):
def A_ ( self : List[Any] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Optional[int] ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , lowercase_ , lowercase_ , lowercase_ )
a : List[Any] = CustomOutputChecker
a : Optional[int] = HfDoctestModule
a : Tuple = HfDocTestParser
| 56
| 0
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert 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 AlignProcessor, EfficientNetImageProcessor
@require_vision
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: str ) -> str:
snake_case_ :Tuple = tempfile.mkdtemp()
snake_case_ :Optional[Any] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
snake_case_ :int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
snake_case_ :List[str] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
"""image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
snake_case_ :List[str] = os.path.join(self.tmpdirname , snake_case )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(snake_case , snake_case )
def lowerCAmelCase_ ( self: Dict , **snake_case: Any ) -> List[Any]:
return BertTokenizer.from_pretrained(self.tmpdirname , **snake_case )
def lowerCAmelCase_ ( self: Optional[Any] , **snake_case: List[str] ) -> Union[str, Any]:
return BertTokenizerFast.from_pretrained(self.tmpdirname , **snake_case )
def lowerCAmelCase_ ( self: Optional[Any] , **snake_case: Dict ) -> Any:
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **snake_case )
def lowerCAmelCase_ ( self: List[Any] ) -> Optional[Any]:
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self: List[Any] ) -> int:
snake_case_ :Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
snake_case_ :Dict = [Image.fromarray(np.moveaxis(snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase_ ( self: List[Any] ) -> Any:
snake_case_ :Optional[Any] = self.get_tokenizer()
snake_case_ :Tuple = self.get_rust_tokenizer()
snake_case_ :Optional[int] = self.get_image_processor()
snake_case_ :List[Any] = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
processor_slow.save_pretrained(self.tmpdirname )
snake_case_ :Union[str, Any] = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=snake_case )
snake_case_ :int = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
processor_fast.save_pretrained(self.tmpdirname )
snake_case_ :int = AlignProcessor.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 , snake_case )
self.assertIsInstance(processor_fast.tokenizer , snake_case )
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 , snake_case )
self.assertIsInstance(processor_fast.image_processor , snake_case )
def lowerCAmelCase_ ( self: str ) -> int:
snake_case_ :List[str] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case_ :List[str] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
snake_case_ :Optional[Any] = self.get_image_processor(do_normalize=snake_case , padding_value=1.0 )
snake_case_ :int = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=snake_case , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , snake_case )
def lowerCAmelCase_ ( self: Optional[Any] ) -> str:
snake_case_ :Tuple = self.get_image_processor()
snake_case_ :List[str] = self.get_tokenizer()
snake_case_ :int = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
snake_case_ :Optional[Any] = self.prepare_image_inputs()
snake_case_ :Optional[int] = image_processor(snake_case , return_tensors="""np""" )
snake_case_ :Optional[Any] = processor(images=snake_case , 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: Optional[int] ) -> List[Any]:
snake_case_ :List[str] = self.get_image_processor()
snake_case_ :List[Any] = self.get_tokenizer()
snake_case_ :Dict = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
snake_case_ :Optional[Any] = """lower newer"""
snake_case_ :int = processor(text=snake_case )
snake_case_ :Optional[Any] = tokenizer(snake_case , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCAmelCase_ ( self: Dict ) -> Union[str, Any]:
snake_case_ :Any = self.get_image_processor()
snake_case_ :Optional[int] = self.get_tokenizer()
snake_case_ :Union[str, Any] = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
snake_case_ :Dict = """lower newer"""
snake_case_ :Dict = self.prepare_image_inputs()
snake_case_ :Dict = processor(text=snake_case , images=snake_case )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(snake_case ):
processor()
def lowerCAmelCase_ ( self: Union[str, Any] ) -> Optional[Any]:
snake_case_ :Optional[Any] = self.get_image_processor()
snake_case_ :Optional[Any] = self.get_tokenizer()
snake_case_ :str = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
snake_case_ :Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case_ :int = processor.batch_decode(snake_case )
snake_case_ :List[str] = tokenizer.batch_decode(snake_case )
self.assertListEqual(snake_case , snake_case )
def lowerCAmelCase_ ( self: List[Any] ) -> List[str]:
snake_case_ :List[Any] = self.get_image_processor()
snake_case_ :str = self.get_tokenizer()
snake_case_ :Optional[Any] = AlignProcessor(tokenizer=snake_case , image_processor=snake_case )
snake_case_ :List[Any] = """lower newer"""
snake_case_ :Dict = self.prepare_image_inputs()
snake_case_ :int = processor(text=snake_case , images=snake_case )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 66
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
a : Dict = logging.get_logger(__name__)
a : List[str] = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class a ( _lowerCamelCase ):
snake_case_ = "marian"
snake_case_ = ["past_key_values"]
snake_case_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , lowercase_ : Optional[Any]=5_8101 , lowercase_ : Dict=None , lowercase_ : List[str]=1024 , lowercase_ : Optional[Any]=12 , lowercase_ : int=4096 , lowercase_ : Any=16 , lowercase_ : Optional[int]=12 , lowercase_ : str=4096 , lowercase_ : Union[str, Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Union[str, Any]=True , lowercase_ : int="gelu" , lowercase_ : Dict=1024 , lowercase_ : int=0.1 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.02 , lowercase_ : int=5_8100 , lowercase_ : Optional[Any]=False , lowercase_ : Any=5_8100 , lowercase_ : Optional[int]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=True , **lowercase_ : Any , ):
snake_case_ = vocab_size
snake_case_ = decoder_vocab_size or vocab_size
snake_case_ = max_position_embeddings
snake_case_ = d_model
snake_case_ = encoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class a ( _lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A_ ( self : Union[str, Any] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ = {0: '''batch'''}
snake_case_ = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A_ ( self : Dict ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super().outputs
else:
snake_case_ = super(lowercase_ , self ).outputs
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A_ ( self : Dict , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
snake_case_ = seq_length if not self.use_past else 1
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case_ = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
snake_case_ = common_inputs['''decoder_input_ids'''].shape[1]
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = decoder_seq_length + 3
snake_case_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
snake_case_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ ,snake_case_ = self.num_layers
snake_case_ = min(lowercase_ , lowercase_ )
snake_case_ = max(lowercase_ , lowercase_ ) - min_num_layers
snake_case_ = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
snake_case_ = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def A_ ( self : Union[str, Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case_ = seqlen + 2
snake_case_ ,snake_case_ = self.num_layers
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = common_inputs['''attention_mask'''].dtype
snake_case_ = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
snake_case_ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def A_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ = tokenizer.num_special_tokens_to_add(lowercase_ )
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def A_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
snake_case_ = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def A_ ( self : Dict , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : List[str] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
snake_case_ = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
@property
def A_ ( self : List[str] ):
return 1e-4
| 56
| 0
|
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__lowerCamelCase = mf_knapsack(i - 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
else:
__lowerCamelCase = max(
mf_knapsack(i - 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) , mf_knapsack(i - 1 , UpperCamelCase__ , UpperCamelCase__ , j - wt[i - 1] ) + val[i - 1] , )
__lowerCamelCase = val
return f[i][j]
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
__lowerCamelCase = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
__lowerCamelCase = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
__lowerCamelCase = dp[i - 1][w_]
return dp[n][w_], dp
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
if not (isinstance(UpperCamelCase__ , (list, tuple) ) and isinstance(UpperCamelCase__ , (list, tuple) )):
raise ValueError(
'''Both the weights and values vectors must be either lists or tuples''' )
__lowerCamelCase = len(UpperCamelCase__ )
if num_items != len(UpperCamelCase__ ):
__lowerCamelCase = (
'''The number of weights must be the same as the number of values.\n'''
f"""But got {num_items} weights and {len(UpperCamelCase__ )} values"""
)
raise ValueError(UpperCamelCase__ )
for i in range(UpperCamelCase__ ):
if not isinstance(wt[i] , UpperCamelCase__ ):
__lowerCamelCase = (
'''All weights must be integers but got weight of '''
f"""type {type(wt[i] )} at index {i}"""
)
raise TypeError(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase = knapsack(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = set()
_construct_solution(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return optimal_val, example_optional_set
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(UpperCamelCase__ , UpperCamelCase__ , i - 1 , UpperCamelCase__ , UpperCamelCase__ )
else:
optimal_set.add(UpperCamelCase__ )
_construct_solution(UpperCamelCase__ , UpperCamelCase__ , i - 1 , j - wt[i - 1] , UpperCamelCase__ )
if __name__ == "__main__":
__UpperCAmelCase =[3, 2, 4, 4]
__UpperCAmelCase =[4, 3, 2, 3]
__UpperCAmelCase =4
__UpperCAmelCase =6
__UpperCAmelCase =[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
__UpperCAmelCase , __UpperCAmelCase =knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
__UpperCAmelCase , __UpperCAmelCase =knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("optimal_value = ", optimal_solution)
print("An optimal subset corresponding to the optimal value", optimal_subset)
| 67
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = CycleDiffusionPipeline
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
snake_case_ = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A_ ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ = 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 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
snake_case_ = 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 , )
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case_ = CLIPTextModel(lowercase_ )
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 A_ ( self : Any , lowercase_ : int , lowercase_ : Optional[Any]=0 ):
snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ = image / 2 + 0.5
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(lowercase_ )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def A_ ( self : Union[str, Any] ):
snake_case_ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , '''half''' ):
snake_case_ = module.half()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Optional[int] ):
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def A_ ( self : List[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_save_load_optional_components()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Union[str, Any] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def A_ ( self : List[str] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 56
| 0
|
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}
lowerCAmelCase__ = {
"""vocab_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
},
"""emoji_file""": {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
},
}
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": 2_0_4_8,
}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: Tuple ) -> Union[str, Any]:
'''simple docstring'''
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" ) as f:
A__ = json.loads(f.read() )
A__ = collections.OrderedDict()
A__ = collections.OrderedDict()
A__ = collections.OrderedDict()
with open(SCREAMING_SNAKE_CASE_ , "r" , encoding="utf-8" ) as f:
A__ = f.readlines()
A__ = [[t.rstrip("\n" )] if (t == "," or "," not in t) else t.rstrip("\n" ).split("," ) for t in token]
for idx, b in enumerate(SCREAMING_SNAKE_CASE_ ):
A__ = b
A__ = idx
for wd in b:
A__ = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = ['input_ids', 'attention_mask']
def __init__( self , lowercase , lowercase , lowercase="<|endoftext|>" , lowercase="<|endoftext|>" , lowercase="<|startoftext|>" , lowercase="<|endoftext|>" , lowercase=False , **lowercase , ) -> str:
'''simple docstring'''
super().__init__(
unk_token=lowercase , pad_token=lowercase , bos_token=lowercase , eos_token=lowercase , do_clean_text=lowercase , **lowercase , )
if not os.path.isfile(lowercase ):
raise ValueError(
F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
" model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
if not os.path.isfile(lowercase ):
raise ValueError(
F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
" pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" )
A__ = do_clean_text
A__ , A__ , A__ , A__ = load_vocab_and_emoji(lowercase , lowercase )
A__ = SubWordJapaneseTokenizer(
vocab=self.vocab , ids_to_tokens=self.ids_to_tokens , emoji=self.emoji )
@property
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
return len(self.raw_vocab )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
return dict(self.raw_vocab , **self.added_tokens_encoder )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
return self.subword_tokenizer.tokenize(lowercase , clean=self.do_clean_text )
def UpperCamelCase ( self , lowercase ) -> List[str]:
'''simple docstring'''
return self.vocab.get(lowercase , self.vocab.get(self.unk_token ) )
def UpperCamelCase ( self , lowercase ) -> int:
'''simple docstring'''
return self.subword_tokenizer.convert_id_to_token(lowercase )
def UpperCamelCase ( self , lowercase ) -> Optional[Any]:
'''simple docstring'''
A__ = "".join(lowercase ).strip()
return out_string
def UpperCamelCase ( self , lowercase ) -> List[int]:
'''simple docstring'''
A__ = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowercase , add_special_tokens=lowercase ) + [self.eos_token_id] )
if len(lowercase ) > self.model_max_length:
A__ = input_ids[-self.model_max_length :]
return input_ids
def UpperCamelCase ( self , lowercase , lowercase = None ) -> Tuple[str]:
'''simple docstring'''
A__ = 0
if os.path.isdir(lowercase ):
A__ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
A__ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"] )
else:
A__ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
A__ = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(lowercase , "w" , encoding="utf-8" ) as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
" Please check that the vocabulary is not corrupted!" )
A__ = token_index
writer.write(",".join(lowercase ) + "\n" )
index += 1
with open(lowercase , "w" , encoding="utf-8" ) as writer:
json.dump(self.emoji , lowercase )
return vocab_file, emoji_file
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , lowercase , lowercase , lowercase ) -> List[Any]:
'''simple docstring'''
A__ = vocab # same as swe
A__ = ids_to_tokens # same as bpe
A__ = emoji
A__ = np.max([len(lowercase ) for w in self.vocab.keys()] )
A__ = re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)" )
A__ = re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*" )
A__ = re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}" )
A__ = re.compile(
R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
A__ = re.compile(
R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*" )
A__ = re.compile(
R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*" )
A__ = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
A__ = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
A__ = str.maketrans({k: "<BLOCK>" for k in keisen + blocks} )
def __len__( self ) -> Dict:
'''simple docstring'''
return len(self.ids_to_tokens )
def UpperCamelCase ( self , lowercase ) -> Optional[int]:
'''simple docstring'''
A__ = self.content_repattera.sub("<URL>" , lowercase )
A__ = self.content_repattera.sub("<EMAIL>" , lowercase )
A__ = self.content_repattera.sub("<TEL>" , lowercase )
A__ = self.content_repattera.sub("<DATE>" , lowercase )
A__ = self.content_repattera.sub("<DATE>" , lowercase )
A__ = self.content_repattera.sub("<PRICE>" , lowercase )
A__ = content.translate(self.content_transa )
while "<BLOCK><BLOCK>" in content:
A__ = content.replace("<BLOCK><BLOCK>" , "<BLOCK>" )
return content
def UpperCamelCase ( self , lowercase , lowercase=False ) -> List[Any]:
'''simple docstring'''
A__ = text.replace(" " , "<SP>" )
A__ = text.replace(" " , "<SP>" )
A__ = text.replace("\r\n" , "<BR>" )
A__ = text.replace("\n" , "<BR>" )
A__ = text.replace("\r" , "<BR>" )
A__ = text.replace("\t" , "<TAB>" )
A__ = text.replace("—" , "ー" )
A__ = text.replace("−" , "ー" )
for k, v in self.emoji["emoji"].items():
if k in text:
A__ = text.replace(lowercase , lowercase )
if clean:
A__ = self.clean_text(lowercase )
def check_simbol(lowercase ):
A__ = x.encode()
if len(lowercase ) == 1 and len(lowercase ) == 2:
A__ = (int(e[0] ) << 8) + int(e[1] )
if (
(c >= 0XC_2_A_1 and c <= 0XC_2_B_F)
or (c >= 0XC_7_8_0 and c <= 0XC_7_8_3)
or (c >= 0XC_A_B_9 and c <= 0XC_B_B_F)
or (c >= 0XC_C_8_0 and c <= 0XC_D_A_2)
):
return True
return False
def checkuae(lowercase ):
A__ = x.encode()
if len(lowercase ) == 1 and len(lowercase ) == 3:
A__ = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] )
if c >= 0XE_2_8_0_8_0 and c <= 0XE_2_B_0_7_F:
return True
return False
A__ = 0
A__ = []
while pos < len(lowercase ):
A__ = min(len(lowercase ) , pos + self.maxlen + 1 ) if text[pos] == "<" else pos + 3
A__ = [] # (token_id, token, pos)
for e in range(lowercase , lowercase , -1 ):
A__ = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(lowercase ) > 2:
A__ = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e) )
if len(lowercase ) > 0:
# the smallest token_id is adopted
A__ , A__ , A__ = sorted(lowercase , key=lambda lowercase : x[0] )[0]
result.append(lowercase )
A__ = e
else:
A__ = pos + 1
A__ = text[pos:end]
if check_simbol(lowercase ):
result.append("<KIGOU>" )
elif checkuae(lowercase ):
result.append("<U2000U2BFF>" )
else:
for i in wd.encode("utf-8" ):
result.append("<|byte%d|>" % i )
A__ = end
return result
def UpperCamelCase ( self , lowercase , lowercase="\n" ) -> Dict:
'''simple docstring'''
A__ = []
A__ = []
A__ = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2] ) )
else:
if len(lowercase ) > 0:
words.append(bytearray(lowercase ).decode("utf-8" , errors="replace" ) )
A__ = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word] )
elif word == "<SP>":
words.append(" " )
elif word == "<BR>":
words.append(lowercase )
elif word == "<TAB>":
words.append("\t" )
elif word == "<BLOCK>":
words.append("▀" )
elif word == "<KIGOU>":
words.append("ǀ" )
elif word == "<U2000U2BFF>":
words.append("‖" )
else:
words.append(lowercase )
if len(lowercase ) > 0:
words.append(bytearray(lowercase ).decode("utf-8" , errors="replace" ) )
A__ = "".join(lowercase )
return text
| 68
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class a ( _lowerCamelCase ):
snake_case_ = "big_bird"
def __init__( self : Union[str, Any] , lowercase_ : List[Any]=5_0358 , lowercase_ : Tuple=768 , lowercase_ : Dict=12 , lowercase_ : str=12 , lowercase_ : Tuple=3072 , lowercase_ : Any="gelu_new" , lowercase_ : Optional[Any]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=4096 , lowercase_ : List[Any]=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[int]=1e-12 , lowercase_ : Tuple=True , lowercase_ : Tuple=0 , lowercase_ : str=1 , lowercase_ : Union[str, Any]=2 , lowercase_ : Optional[Any]=66 , lowercase_ : Optional[int]="block_sparse" , lowercase_ : Any=True , lowercase_ : List[str]=False , lowercase_ : Any=64 , lowercase_ : Tuple=3 , lowercase_ : Tuple=None , **lowercase_ : Tuple , ):
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , sep_token_id=lowercase_ , **lowercase_ , )
snake_case_ = vocab_size
snake_case_ = max_position_embeddings
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_ = type_vocab_size
snake_case_ = layer_norm_eps
snake_case_ = use_cache
snake_case_ = rescale_embeddings
snake_case_ = attention_type
snake_case_ = use_bias
snake_case_ = block_size
snake_case_ = num_random_blocks
snake_case_ = classifier_dropout
class a ( _lowerCamelCase ):
@property
def A_ ( self : str ):
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 56
| 0
|
"""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 UpperCamelCase ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE_ = "facebook/bart-large-mnli"
SCREAMING_SNAKE_CASE_ = (
"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."
)
SCREAMING_SNAKE_CASE_ = "text_classifier"
SCREAMING_SNAKE_CASE_ = AutoTokenizer
SCREAMING_SNAKE_CASE_ = AutoModelForSequenceClassification
SCREAMING_SNAKE_CASE_ = ["text", ["text"]]
SCREAMING_SNAKE_CASE_ = ["text"]
def a_ ( self) -> Dict:
super().setup()
snake_case_ = self.model.config
snake_case_ = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('entail'):
snake_case_ = 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, lowerCAmelCase__, lowerCAmelCase__) -> List[Any]:
snake_case_ = 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, lowerCAmelCase__) -> Tuple:
snake_case_ = outputs.logits
snake_case_ = torch.argmax(logits[:, 2]).item()
return self._labels[label_id]
| 69
|
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''simple docstring'''
assert isinstance(__UpperCAmelCase, __UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''', [False, True] )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = SqlDatasetReader(
'''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase, keep_in_memory=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
'''features''', [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
], )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, features=__UpperCAmelCase, cache_dir=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(__UpperCAmelCase ) ) as con:
snake_case_ = con.cursor()
cur.execute('''SELECT * FROM dataset''' )
for row in cur:
yield row
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=1 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=2 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
with pytest.raises(__UpperCAmelCase ):
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=0 ).write()
| 56
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
A__ : List[str] =logging.get_logger(__name__)
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if isinstance(lowerCAmelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowerCAmelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowerCAmelCase ):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}" )
class UpperCAmelCase ( snake_case_ ):
_lowercase: Any = ['''pixel_values''']
def __init__( self : Tuple , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : bool = True , __snake_case : Dict[str, int] = None , __snake_case : bool = True , __snake_case : Union[int, float] = 1 / 2_55 , __snake_case : bool = True , __snake_case : bool = True , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , **__snake_case : str , ) -> None:
super().__init__(**__snake_case )
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 2_56}
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
_lowerCAmelCase = get_size_dict(__snake_case , param_name="""crop_size""" )
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
_lowerCAmelCase = resample
_lowerCAmelCase = do_rescale
_lowerCAmelCase = rescale_factor
_lowerCAmelCase = offset
_lowerCAmelCase = do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self : int , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : PILImageResampling = PILImageResampling.BILINEAR , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
if "shortest_edge" in size:
_lowerCAmelCase = get_resize_output_image_size(__snake_case , size["""shortest_edge"""] , default_to_square=__snake_case )
elif "height" in size and "width" in size:
_lowerCAmelCase = (size["""height"""], size["""width"""])
else:
raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(__snake_case , size=__snake_case , resample=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : np.ndarray , __snake_case : Dict[str, int] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : List[Any] , ) -> np.ndarray:
_lowerCAmelCase = get_size_dict(__snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(__snake_case , size=(size["""height"""], size["""width"""]) , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] , __snake_case : np.ndarray , __snake_case : Union[int, float] , __snake_case : bool = True , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Optional[Any] , ) -> Dict:
_lowerCAmelCase = image.astype(np.floataa )
if offset:
_lowerCAmelCase = image - (scale / 2)
return rescale(__snake_case , scale=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : Optional[int] , __snake_case : np.ndarray , __snake_case : Union[float, List[float]] , __snake_case : Union[float, List[float]] , __snake_case : Optional[Union[str, ChannelDimension]] = None , **__snake_case : Tuple , ) -> np.ndarray:
return normalize(__snake_case , mean=__snake_case , std=__snake_case , data_format=__snake_case , **__snake_case )
def lowercase__ ( self : List[Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.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_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
_lowerCAmelCase = to_numpy_array(__snake_case )
if do_resize:
_lowerCAmelCase = self.resize(image=__snake_case , size=__snake_case , resample=__snake_case )
if do_center_crop:
_lowerCAmelCase = self.center_crop(__snake_case , size=__snake_case )
if do_rescale:
_lowerCAmelCase = self.rescale(image=__snake_case , scale=__snake_case , offset=__snake_case )
if do_normalize:
_lowerCAmelCase = self.normalize(image=__snake_case , mean=__snake_case , std=__snake_case )
_lowerCAmelCase = to_channel_dimension_format(__snake_case , __snake_case )
return image
def lowercase__ ( self : List[Any] , __snake_case : ImageInput , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : PILImageResampling = None , __snake_case : bool = None , __snake_case : Dict[str, int] = None , __snake_case : bool = None , __snake_case : float = None , __snake_case : bool = None , __snake_case : bool = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[float, List[float]]] = None , __snake_case : Optional[Union[str, TensorType]] = None , __snake_case : ChannelDimension = ChannelDimension.FIRST , **__snake_case : List[str] , ) -> PIL.Image.Image:
_lowerCAmelCase = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase = resample if resample is not None else self.resample
_lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase = offset if offset is not None else self.offset
_lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase = image_std if image_std is not None else self.image_std
_lowerCAmelCase = size if size is not None else self.size
_lowerCAmelCase = get_size_dict(__snake_case , default_to_square=__snake_case )
_lowerCAmelCase = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase = get_size_dict(__snake_case , param_name="""crop_size""" )
if not valid_images(__snake_case ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
_lowerCAmelCase = make_batched(__snake_case )
_lowerCAmelCase = [
[
self._preprocess_image(
image=__snake_case , do_resize=__snake_case , size=__snake_case , resample=__snake_case , do_center_crop=__snake_case , crop_size=__snake_case , do_rescale=__snake_case , rescale_factor=__snake_case , offset=__snake_case , do_normalize=__snake_case , image_mean=__snake_case , image_std=__snake_case , data_format=__snake_case , )
for img in video
]
for video in videos
]
_lowerCAmelCase = {"""pixel_values""": videos}
return BatchFeature(data=__snake_case , tensor_type=__snake_case )
| 70
|
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCase )
if ret % 2 == 0:
cuts.append(__UpperCAmelCase )
return ret
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
a ,a : Dict = 10, 9
a : Dict = defaultdict(list)
a : dict[int, bool] = {}
a : list[int] = []
a : Tuple = 0
a : str = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 56
| 0
|
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def A ( a_ ) -> List[Any]:
__UpperCamelCase : int =checkpoints.load_tax_checkpoint(a_ )
__UpperCamelCase : List[str] =flatten_dict(a_ )
return flax_params
def A ( a_ ) -> Optional[int]:
__UpperCamelCase : Union[str, Any] ={}
__UpperCamelCase : Any ={
'token_embedder': 'embeddings',
'encoder_norm': 'layernorm',
'kernel': 'weight',
'.out': '.output',
'scale': 'weight',
'embedders_0.pos_embedding': 'row_embedder.weight',
'embedders_1.pos_embedding': 'column_embedder.weight',
}
__UpperCamelCase : Any ={
'query': 'attention.query',
'key': 'attention.key',
'value': 'attention.value',
'output.dense': 'output',
'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
'pre_self_attention_layer_norm': 'self_attention.layer_norm',
'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
'mlp.': 'mlp.DenseReluDense.',
'pre_mlp_layer_norm': 'mlp.layer_norm',
'self_attention.o': 'self_attention.attention.o',
'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
'decoder.logits_dense.weight': 'decoder.lm_head.weight',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
__UpperCamelCase : Tuple ='.'.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
__UpperCamelCase : Union[str, Any] =new_key.replace(a_ ,a_ )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
__UpperCamelCase : int =new_key.replace(a_ ,a_ )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
__UpperCamelCase : Dict =re.sub(r'layers_(\d+)' ,r'layer.\1' ,a_ )
__UpperCamelCase : int =new_key.replace('encoder' ,'encoder.encoder' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
__UpperCamelCase : Tuple =re.sub(r'layers_(\d+)' ,r'layer.\1' ,a_ )
__UpperCamelCase : Union[str, Any] =flax_dict[key]
__UpperCamelCase : List[Any] ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
__UpperCamelCase : int =torch.from_numpy(converted_dict[key].T )
else:
__UpperCamelCase : Any =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def A ( a_ ,a_ ,a_=False ,a_=False ) -> Dict:
__UpperCamelCase : Dict =get_flax_param(a_ )
if not use_large:
__UpperCamelCase : Optional[Any] =PixaStructVisionConfig()
__UpperCamelCase : int =PixaStructTextConfig()
else:
__UpperCamelCase : Dict =PixaStructVisionConfig(
hidden_size=1_536 ,d_ff=3_968 ,num_attention_heads=24 ,num_hidden_layers=18 )
__UpperCamelCase : List[Any] =PixaStructTextConfig(hidden_size=1_536 ,d_ff=3_968 ,num_heads=24 ,num_layers=18 )
__UpperCamelCase : List[Any] =PixaStructConfig(
vision_config=encoder_config.to_dict() ,text_config=decoder_config.to_dict() ,is_vqa=a_ )
__UpperCamelCase : Union[str, Any] =PixaStructForConditionalGeneration(a_ )
__UpperCamelCase : Union[str, Any] =rename_and_convert_flax_params(a_ )
model.load_state_dict(a_ )
__UpperCamelCase : List[Any] =AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer' )
__UpperCamelCase : Optional[int] =PixaStructImageProcessor()
__UpperCamelCase : List[str] =PixaStructProcessor(image_processor=a_ ,tokenizer=a_ )
if use_large:
__UpperCamelCase : int =4_096
__UpperCamelCase : Any =True
# mkdir if needed
os.makedirs(a_ ,exist_ok=a_ )
model.save_pretrained(a_ )
processor.save_pretrained(a_ )
print('Model saved in {}'.format(a_ ) )
if __name__ == "__main__":
A_ :str = argparse.ArgumentParser()
parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
A_ :List[Any] = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 71
|
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56
| 0
|
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase__ = '''docs/source/en/_toctree.yml'''
def snake_case_ ( A_ : str ):
'''simple docstring'''
_lowerCamelCase : List[Any] = defaultdict(A_ )
_lowerCamelCase : List[Any] = []
_lowerCamelCase : Tuple = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']} )
else:
new_doc_list.append(A_ )
_lowerCamelCase : Optional[Any] = new_doc_list
_lowerCamelCase : Tuple = [key for key, value in counts.items() if value > 1]
_lowerCamelCase : List[str] = []
for duplicate_key in duplicates:
_lowerCamelCase : Optional[int] = list({doc['''title'''] for doc in doc_list 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 doc_list if '''local''' not in counts or counts[doc['''local''']] == 1] )
_lowerCamelCase : Optional[int] = sorted(A_, key=lambda A_ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(A_ ) > 1:
raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.''' )
overview_doc.extend(A_ )
# Sort
return overview_doc
def snake_case_ ( A_ : str=False ):
'''simple docstring'''
with open(A_, encoding='''utf-8''' ) as f:
_lowerCamelCase : Union[str, Any] = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCamelCase : Union[str, Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCamelCase : List[Any] = content[api_idx]['''sections''']
# Then to the model doc
_lowerCamelCase : str = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCamelCase : List[str] = api_doc[scheduler_idx]['''sections''']
_lowerCamelCase : Dict = clean_doc_toc(A_ )
_lowerCamelCase : Tuple = False
if new_scheduler_doc != scheduler_doc:
_lowerCamelCase : List[str] = True
if overwrite:
_lowerCamelCase : str = new_scheduler_doc
if diff:
if overwrite:
_lowerCamelCase : List[Any] = 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.''' )
def snake_case_ ( A_ : Any=False ):
'''simple docstring'''
with open(A_, encoding='''utf-8''' ) as f:
_lowerCamelCase : Dict = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCamelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCamelCase : Union[str, Any] = content[api_idx]['''sections''']
# Then to the model doc
_lowerCamelCase : Union[str, Any] = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCamelCase : str = False
_lowerCamelCase : Tuple = api_doc[pipeline_idx]['''sections''']
_lowerCamelCase : Any = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCamelCase : List[str] = pipeline_doc['''section''']
_lowerCamelCase : Optional[Any] = clean_doc_toc(A_ )
if overwrite:
_lowerCamelCase : List[str] = new_sub_pipeline_doc
new_pipeline_docs.append(A_ )
# sort overall pipeline doc
_lowerCamelCase : Any = clean_doc_toc(A_ )
if new_pipeline_docs != pipeline_docs:
_lowerCamelCase : Dict = True
if overwrite:
_lowerCamelCase : Optional[int] = new_pipeline_docs
if diff:
if overwrite:
_lowerCamelCase : Union[str, Any] = 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__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCAmelCase__ = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 72
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Any = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = DPTConfig()
if "large" in checkpoint_url:
snake_case_ = 1024
snake_case_ = 4096
snake_case_ = 24
snake_case_ = 16
snake_case_ = [5, 11, 17, 23]
snake_case_ = [256, 512, 1024, 1024]
snake_case_ = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case_ = True
snake_case_ = 150
snake_case_ = '''huggingface/label-files'''
snake_case_ = '''ade20k-id2label.json'''
snake_case_ = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset''' ) ), '''r''' ) )
snake_case_ = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = [1, 150, 480, 480]
return config, expected_shape
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case_ = name.replace('''pretrained.model''', '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case_ = name.replace('''pretrained.model''', '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case_ = name.replace('''patch_embed''', '''patch_embeddings''' )
if "pos_embed" in name:
snake_case_ = name.replace('''pos_embed''', '''position_embeddings''' )
if "attn.proj" in name:
snake_case_ = name.replace('''attn.proj''', '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case_ = name.replace('''proj''', '''projection''' )
if "blocks" in name:
snake_case_ = name.replace('''blocks''', '''layer''' )
if "mlp.fc1" in name:
snake_case_ = name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case_ = name.replace('''mlp.fc2''', '''output.dense''' )
if "norm1" in name:
snake_case_ = name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
snake_case_ = name.replace('''norm2''', '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case_ = name.replace('''scratch.output_conv''', '''head''' )
if "scratch" in name:
snake_case_ = name.replace('''scratch''', '''neck''' )
if "layer1_rn" in name:
snake_case_ = name.replace('''layer1_rn''', '''convs.0''' )
if "layer2_rn" in name:
snake_case_ = name.replace('''layer2_rn''', '''convs.1''' )
if "layer3_rn" in name:
snake_case_ = name.replace('''layer3_rn''', '''convs.2''' )
if "layer4_rn" in name:
snake_case_ = name.replace('''layer4_rn''', '''convs.3''' )
if "refinenet" in name:
snake_case_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case_ = name.replace(F"refinenet{layer_idx}", F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
snake_case_ = name.replace('''out_conv''', '''projection''' )
if "resConfUnit1" in name:
snake_case_ = name.replace('''resConfUnit1''', '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case_ = name.replace('''resConfUnit2''', '''residual_layer2''' )
if "conv1" in name:
snake_case_ = name.replace('''conv1''', '''convolution1''' )
if "conv2" in name:
snake_case_ = name.replace('''conv2''', '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.0.project.0''', '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.0.project.0''', '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.0.project.0''', '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.0.project.0''', '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.3''', '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.4''', '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.3''', '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.4''', '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.3''', '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.3''', '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.4''', '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case_ = name.replace('''pretrained''', '''dpt''' )
if "bn" in name:
snake_case_ = name.replace('''bn''', '''batch_norm''' )
if "head" in name:
snake_case_ = name.replace('''head''', '''head.head''' )
if "encoder.norm" in name:
snake_case_ = name.replace('''encoder.norm''', '''layernorm''' )
if "auxlayer" in name:
snake_case_ = name.replace('''auxlayer''', '''auxiliary_head.head''' )
return name
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{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 __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ = Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dpt_config(__UpperCAmelCase )
# load original state_dict from URL
snake_case_ = torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
snake_case_ = state_dict.pop(__UpperCAmelCase )
snake_case_ = val
# read in qkv matrices
read_in_q_k_v(__UpperCAmelCase, __UpperCAmelCase )
# load HuggingFace model
snake_case_ = DPTForSemanticSegmentation(__UpperCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
# Check outputs on an image
snake_case_ = 480 if '''ade''' in checkpoint_url else 384
snake_case_ = DPTImageProcessor(size=__UpperCAmelCase )
snake_case_ = prepare_img()
snake_case_ = image_processor(__UpperCAmelCase, return_tensors='''pt''' )
# forward pass
snake_case_ = model(**__UpperCAmelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase ).predicted_depth
# Assert logits
snake_case_ = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
snake_case_ = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__UpperCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3], __UpperCAmelCase, atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3], __UpperCAmelCase )
)
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add model''', use_temp_dir=__UpperCAmelCase, )
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add image processor''', use_temp_dir=__UpperCAmelCase, )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
a : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 56
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|
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> List[str]:
__lowerCamelCase : List[Any] = len(lowerCamelCase__ )
while cur > 1:
# Find the maximum number in arr
__lowerCamelCase : List[Any] = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
__lowerCamelCase : Any = arr[mi::-1] + arr[mi + 1 : len(lowerCamelCase__ )]
# Reverse whole list
__lowerCamelCase : str = arr[cur - 1 :: -1] + arr[cur : len(lowerCamelCase__ )]
cur -= 1
return arr
if __name__ == "__main__":
a =input("""Enter numbers separated by a comma:\n""").strip()
a =[int(item) for item in user_input.split(""",""")]
print(pancake_sort(unsorted))
| 73
|
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
a : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 56
| 0
|
"""simple docstring"""
import argparse
import json
import subprocess
def _snake_case ( snake_case__ : str , snake_case__ : List[Any] ):
A = []
A = (
F'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
' https://api.github.com/repos/huggingface/transformers/actions/runners'
)
A = subprocess.run(snake_case__ , shell=snake_case__ , stdout=subprocess.PIPE )
A = output.stdout.decode('utf-8' )
A = json.loads(snake_case__ )
A = status['runners']
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(snake_case__ )
# save the result so we can report them on Slack
with open('offline_runners.txt' , 'w' ) as fp:
fp.write(json.dumps(snake_case__ ) )
if len(snake_case__ ) > 0:
A = '\n'.join([x['name'] for x in offline_runners] )
raise ValueError(F'The following runners are offline:\n{failed}' )
if __name__ == "__main__":
def _snake_case ( snake_case__ : List[Any] ):
return values.split(',' )
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--target_runners''',
default=None,
type=list_str,
required=True,
help='''Comma-separated list of runners to check status.''',
)
parser.add_argument(
'''--token''', default=None, type=str, required=True, help='''A token that has actions:read permission.'''
)
_lowercase = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 74
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
re.sub('''<n>''', '''''', __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 56
| 0
|
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ : str = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_sentencepiece_available():
import sentencepiece as sp
a_ : Optional[Any] = 5
a_ : str = 10
@require_sentencepiece
@require_tokenizers
class __UpperCamelCase ( lowerCamelCase__ , unittest.TestCase ):
lowercase : int =SpeechaTextTokenizer
lowercase : int =False
lowercase : List[str] =True
def lowercase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCamelCase_ =sp.SentencePieceProcessor()
spm_model.Load(lowerCAmelCase )
lowerCamelCase_ =['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']
vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(lowerCAmelCase ) )]
lowerCamelCase_ =dict(zip(lowerCAmelCase, range(len(lowerCAmelCase ) ) ) )
lowerCamelCase_ =Path(self.tmpdirname )
save_json(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(lowerCAmelCase, save_dir / VOCAB_FILES_NAMES['''spm_file'''] )
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''<pad>'''
lowerCamelCase_ =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase ), lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase ), lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0], '''<s>''' )
self.assertEqual(vocab_keys[1], '''<pad>''' )
self.assertEqual(vocab_keys[-1], '''j''' )
self.assertEqual(len(lowerCAmelCase ), 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size, 1_001 )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
lowerCamelCase_ =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(lowerCAmelCase, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ), [289, 50, 14, 174, 386], )
lowerCamelCase_ =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.'''], )
lowerCamelCase_ =tokenizer.convert_tokens_to_ids(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase, [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] )
lowerCamelCase_ =tokenizer.convert_ids_to_tokens(lowerCAmelCase )
self.assertListEqual(
lowerCAmelCase, [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.'''], )
@slow
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={'''input_ids''': [[3_791, 797, 31, 11, 64, 797, 31, 2_429, 433, 12, 1_176, 12, 20, 786, 915, 142, 2_413, 240, 37, 3_238, 797, 31, 11, 35, 93, 915, 142, 2_413, 240, 37, 5_540, 567, 1_276, 93, 37, 610, 40, 62, 455, 657, 1_042, 123, 780, 177, 37, 309, 241, 1_298, 514, 20, 292, 2_737, 114, 2_469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3_388, 511, 459, 4, 3_555, 40, 321, 302, 705, 4, 3_388, 511, 583, 326, 5, 5, 5, 62, 3_310, 560, 177, 2_680, 217, 1_508, 32, 31, 853, 418, 64, 583, 511, 1_605, 62, 35, 93, 560, 177, 2_680, 217, 1_508, 1_521, 64, 583, 511, 519, 62, 20, 1_515, 764, 20, 149, 261, 5_625, 7_972, 20, 5_540, 567, 1_276, 93, 3_925, 1_675, 11, 15, 802, 7_972, 576, 217, 1_508, 11, 35, 93, 1_253, 2_441, 15, 289, 652, 31, 416, 321, 3_842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2_681, 1_153, 3_434, 20, 5_540, 37, 567, 126, 1_253, 2_441, 3_376, 449, 210, 431, 1_563, 177, 767, 5_540, 11, 1_203, 472, 11, 2_953, 685, 285, 364, 706, 1_153, 20, 6_799, 20, 2_869, 20, 4_464, 126, 40, 2_429, 20, 1_040, 866, 2_664, 418, 20, 318, 20, 1_726, 186, 20, 265, 522, 35, 93, 2_191, 4_634, 20, 1_040, 12, 6_799, 15, 228, 2_356, 142, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_575, 2_666, 684, 1_582, 1_176, 12, 627, 149, 619, 20, 4_902, 563, 11, 20, 149, 261, 3_420, 2_356, 174, 142, 4_714, 131, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowerCAmelCase, model_name='''facebook/s2t-small-mustc-en-de-st''', revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''', )
@require_sentencepiece
class __UpperCamelCase ( unittest.TestCase ):
lowercase : Tuple ='valhalla/s2t_mustc_multilinguial_medium'
lowercase : Dict ='C\'est trop cool'
lowercase : str ='Esto es genial'
@classmethod
def lowercase__ ( cls ):
"""simple docstring"""
lowerCamelCase_ =SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name )
return cls
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''], 4 )
self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''], 6 )
self.assertEqual(self.tokenizer.lang_code_to_id['''it'''], 9 )
self.assertEqual(self.tokenizer.lang_code_to_id['''de'''], 11 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertEqual(self.tokenizer.vocab_size, 10_000 )
def lowercase__ ( self ):
"""simple docstring"""
self.assertIn(lowerCAmelCase, self.tokenizer.all_special_ids )
lowerCamelCase_ =[ES_CODE, 4, 1_601, 47, 7_647, 2]
lowerCamelCase_ =self.tokenizer.decode(lowerCAmelCase, skip_special_tokens=lowerCAmelCase )
lowerCamelCase_ =self.tokenizer.decode(generated_ids[1:], skip_special_tokens=lowerCAmelCase )
self.assertEqual(lowerCAmelCase, lowerCAmelCase )
self.assertNotIn(self.tokenizer.eos_token, lowerCAmelCase )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
lowerCamelCase_ =self.tokenizer(self.french_text ).input_ids
self.assertEqual(encoded[0], lowerCAmelCase )
self.assertEqual(encoded[-1], self.tokenizer.eos_token_id )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ='''fr'''
self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE] )
lowerCamelCase_ ='''es'''
self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE] )
| 75
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
a : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 56
| 0
|
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] , a : Optional[int] , a : str=13 , a : str=7 , a : List[Any]=True , a : List[str]=True , a : int=True , a : Any=True , a : Tuple=99 , a : int=32 , a : Union[str, Any]=5 , a : str=4 , a : Optional[Any]=37 , a : Optional[Any]="gelu" , a : Any=0.1 , a : Optional[Any]=0.1 , a : Any=512 , a : int=16 , a : Optional[int]=2 , a : Optional[int]=0.02 , a : str=4 , ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = parent
SCREAMING_SNAKE_CASE : List[str] = batch_size
SCREAMING_SNAKE_CASE : Optional[Any] = seq_length
SCREAMING_SNAKE_CASE : List[Any] = is_training
SCREAMING_SNAKE_CASE : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE : Any = use_token_type_ids
SCREAMING_SNAKE_CASE : Optional[Any] = use_labels
SCREAMING_SNAKE_CASE : List[Any] = vocab_size
SCREAMING_SNAKE_CASE : int = hidden_size
SCREAMING_SNAKE_CASE : int = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = intermediate_size
SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : int = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : str = num_choices
def __UpperCamelCase ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE : Optional[Any] = RoFormerConfig(
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=a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCamelCase ( self : Optional[int] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE : Dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =True
lowerCamelCase__ =(
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = FlaxRoFormerModelTester(self )
@slow
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE : int = model_class_name.from_pretrained("junnyu/roformer_chinese_small" , from_pt=a )
SCREAMING_SNAKE_CASE : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(a )
@require_flax
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
SCREAMING_SNAKE_CASE : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE : Tuple = model(a )[0]
SCREAMING_SNAKE_CASE : Optional[int] = 5_0000
SCREAMING_SNAKE_CASE : Any = (1, 6, vocab_size)
self.assertEqual(output.shape , a )
SCREAMING_SNAKE_CASE : Any = jnp.array(
[[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , a , atol=1e-4 ) )
| 76
|
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Optional[Any] , lowercase_ : float , lowercase_ : Callable , lowercase_ : int , lowercase_ : float = 1.0 , lowercase_ : str = None , ):
super().__init__()
snake_case_ = initial_learning_rate
snake_case_ = warmup_steps
snake_case_ = power
snake_case_ = decay_schedule_fn
snake_case_ = name
def __call__( self : Tuple , lowercase_ : str ):
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
snake_case_ = tf.cast(lowercase_ , tf.floataa )
snake_case_ = tf.cast(self.warmup_steps , tf.floataa )
snake_case_ = global_step_float / warmup_steps_float
snake_case_ = self.initial_learning_rate * tf.math.pow(lowercase_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=lowercase_ , )
def A_ ( self : Any ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 0.9, __UpperCAmelCase = 0.9_9_9, __UpperCAmelCase = 1e-8, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0, __UpperCAmelCase = None, ) -> List[str]:
'''simple docstring'''
snake_case_ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=__UpperCAmelCase, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=init_lr * min_lr_ratio, power=__UpperCAmelCase, )
if num_warmup_steps:
snake_case_ = WarmUp(
initial_learning_rate=__UpperCAmelCase, decay_schedule_fn=__UpperCAmelCase, warmup_steps=__UpperCAmelCase, )
if weight_decay_rate > 0.0:
snake_case_ = AdamWeightDecay(
learning_rate=__UpperCAmelCase, weight_decay_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''], include_in_weight_decay=__UpperCAmelCase, )
else:
snake_case_ = tf.keras.optimizers.Adam(
learning_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class a ( _lowerCamelCase ):
def __init__( self : Dict , lowercase_ : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , lowercase_ : float = 0.9 , lowercase_ : float = 0.999 , lowercase_ : float = 1e-7 , lowercase_ : bool = False , lowercase_ : float = 0.0 , lowercase_ : Optional[List[str]] = None , lowercase_ : Optional[List[str]] = None , lowercase_ : str = "AdamWeightDecay" , **lowercase_ : Optional[int] , ):
super().__init__(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
snake_case_ = weight_decay_rate
snake_case_ = include_in_weight_decay
snake_case_ = exclude_from_weight_decay
@classmethod
def A_ ( cls : Dict , lowercase_ : Union[str, Any] ):
snake_case_ = {'''WarmUp''': WarmUp}
return super(lowercase_ , cls ).from_config(lowercase_ , custom_objects=lowercase_ )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Optional[int] ):
super(lowercase_ , self )._prepare_local(lowercase_ , lowercase_ , lowercase_ )
snake_case_ = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def A_ ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any ):
snake_case_ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : str=None , **lowercase_ : List[str] ):
snake_case_ ,snake_case_ = list(zip(*lowercase_ ) )
return super(lowercase_ , self ).apply_gradients(zip(lowercase_ , lowercase_ ) , name=lowercase_ , **lowercase_ )
def A_ ( self : List[Any] , lowercase_ : str , lowercase_ : str , lowercase_ : Any ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
snake_case_ = apply_state or {}
snake_case_ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
snake_case_ = self._fallback_apply_state(lowercase_ , lowercase_ )
snake_case_ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : Optional[int]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_dense(lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : str , lowercase_ : List[Any]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_sparse(lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def A_ ( self : Optional[int] , lowercase_ : int ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return False
return True
class a ( _lowerCamelCase ):
def __init__( self : List[Any] ):
snake_case_ = []
snake_case_ = None
@property
def A_ ( self : Union[str, Any] ):
if self._accum_steps is None:
snake_case_ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def A_ ( self : Dict ):
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Any , lowercase_ : int ):
if not self._gradients:
snake_case_ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(lowercase_ ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(lowercase_ ) != len(self._gradients ):
raise ValueError(F"Expected {len(self._gradients )} gradients, but got {len(lowercase_ )}" )
for accum_gradient, gradient in zip(self._gradients , lowercase_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(lowercase_ )
self._accum_steps.assign_add(1 )
def A_ ( self : Optional[int] ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(lowercase_ ) )
| 56
| 0
|
"""simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
_UpperCamelCase : Optional[int] = TypeVar("KT")
_UpperCamelCase : Tuple = TypeVar("VT")
class UpperCAmelCase_ ( Generic[KT, VT]):
def __init__( self , a = "root" , a = None ) -> Dict:
lowercase__ : str = key
lowercase__ : int = value
lowercase__ : list[Node[KT, VT]] = []
def __repr__( self ) -> str:
return f"""Node({self.key}: {self.value})"""
@property
def _UpperCAmelCase ( self ) -> int:
return len(self.forward )
class UpperCAmelCase_ ( Generic[KT, VT]):
def __init__( self , a = 0.5 , a = 1_6 ) -> Any:
lowercase__ : Node[KT, VT] = Node[KT, VT]()
lowercase__ : Any = 0
lowercase__ : int = p
lowercase__ : Optional[Any] = max_level
def __str__( self ) -> str:
lowercase__ : Any = list(self )
if len(a ) == 0:
return f"""SkipList(level={self.level})"""
lowercase__ : Optional[int] = max((len(str(a ) ) for item in items) , default=4 )
lowercase__ : Dict = max(a , 4 ) + 4
lowercase__ : Dict = self.head
lowercase__ : Tuple = []
lowercase__ : Any = node.forward.copy()
lines.append(f"""[{node.key}]""".ljust(a , '-' ) + '* ' * len(a ) )
lines.append(' ' * label_size + '| ' * len(a ) )
while len(node.forward ) != 0:
lowercase__ : Union[str, Any] = node.forward[0]
lines.append(
f"""[{node.key}]""".ljust(a , '-' )
+ ' '.join(str(n.key ) if n.key == node.key else '|' for n in forwards ) )
lines.append(' ' * label_size + '| ' * len(a ) )
lowercase__ : str = node.forward
lines.append('None'.ljust(a ) + '* ' * len(a ) )
return f"""SkipList(level={self.level})\n""" + "\n".join(a )
def __iter__( self ) -> Any:
lowercase__ : Dict = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
lowercase__ : List[str] = node.forward[0]
def _UpperCAmelCase ( self ) -> int:
lowercase__ : List[Any] = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def _UpperCAmelCase ( self , a ) -> tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
lowercase__ : Optional[int] = []
lowercase__ : Tuple = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
lowercase__ : Dict = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(a )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def _UpperCAmelCase ( self , a ) -> Dict:
lowercase__ , lowercase__ : Optional[Any] = self._locate_node(a )
if node is not None:
for i, update_node in enumerate(a ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
lowercase__ : List[str] = node.forward[i]
else:
lowercase__ : Optional[int] = update_node.forward[:i]
def _UpperCAmelCase ( self , a , a ) -> Optional[int]:
lowercase__ , lowercase__ : str = self._locate_node(a )
if node is not None:
lowercase__ : Optional[int] = value
else:
lowercase__ : List[str] = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , a ):
update_vector.append(self.head )
lowercase__ : List[Any] = level
lowercase__ : Tuple = Node(a , a )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(a )
else:
lowercase__ : str = new_node
def _UpperCAmelCase ( self , a ) -> VT | None:
lowercase__ , lowercase__ : Optional[Any] = self._locate_node(a )
if node is not None:
return node.value
return None
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = SkipList()
skip_list.insert('Key1' , 3 )
skip_list.insert('Key2' , 12 )
skip_list.insert('Key3' , 41 )
skip_list.insert('Key4' , -19 )
lowercase__ : Dict = skip_list.head
lowercase__ : Dict = {}
while node.level != 0:
lowercase__ : Dict = node.forward[0]
lowercase__ : int = node.value
assert len(_lowerCAmelCase ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = SkipList()
skip_list.insert('Key1' , 10 )
skip_list.insert('Key1' , 12 )
skip_list.insert('Key5' , 7 )
skip_list.insert('Key7' , 10 )
skip_list.insert('Key10' , 5 )
skip_list.insert('Key7' , 7 )
skip_list.insert('Key5' , 5 )
skip_list.insert('Key10' , 10 )
lowercase__ : Dict = skip_list.head
lowercase__ : Tuple = {}
while node.level != 0:
lowercase__ : Any = node.forward[0]
lowercase__ : Optional[int] = node.value
if len(_lowerCAmelCase ) != 4:
print()
assert len(_lowerCAmelCase ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = SkipList()
assert skip_list.find('Some key' ) is None
def a_ ( ):
'''simple docstring'''
lowercase__ : List[str] = SkipList()
skip_list.insert('Key2' , 20 )
assert skip_list.find('Key2' ) == 20
skip_list.insert('Some Key' , 10 )
skip_list.insert('Key2' , 8 )
skip_list.insert('V' , 13 )
assert skip_list.find('Y' ) is None
assert skip_list.find('Key2' ) == 8
assert skip_list.find('Some Key' ) == 10
assert skip_list.find('V' ) == 13
def a_ ( ):
'''simple docstring'''
lowercase__ : str = SkipList()
skip_list.delete('Some key' )
assert len(skip_list.head.forward ) == 0
def a_ ( ):
'''simple docstring'''
lowercase__ : Optional[Any] = SkipList()
skip_list.insert('Key1' , 12 )
skip_list.insert('V' , 13 )
skip_list.insert('X' , 14 )
skip_list.insert('Key2' , 15 )
skip_list.delete('V' )
skip_list.delete('Key2' )
assert skip_list.find('V' ) is None
assert skip_list.find('Key2' ) is None
def a_ ( ):
'''simple docstring'''
lowercase__ : List[str] = SkipList()
skip_list.insert('Key1' , 12 )
skip_list.insert('V' , 13 )
skip_list.insert('X' , 14 )
skip_list.insert('Key2' , 15 )
skip_list.delete('V' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) == 14
assert skip_list.find('Key1' ) == 12
assert skip_list.find('Key2' ) == 15
skip_list.delete('X' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) == 12
assert skip_list.find('Key2' ) == 15
skip_list.delete('Key1' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) is None
assert skip_list.find('Key2' ) == 15
skip_list.delete('Key2' )
assert skip_list.find('V' ) is None
assert skip_list.find('X' ) is None
assert skip_list.find('Key1' ) is None
assert skip_list.find('Key2' ) is None
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = SkipList()
skip_list.insert('Key1' , 12 )
skip_list.insert('V' , 13 )
skip_list.insert('X' , 142 )
skip_list.insert('Key2' , 15 )
skip_list.delete('X' )
def traverse_keys(_lowerCAmelCase : Tuple ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(_lowerCAmelCase )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def a_ ( ):
'''simple docstring'''
def is_sorted(_lowerCAmelCase : Dict ):
return all(next_item >= item for item, next_item in zip(_lowerCAmelCase , lst[1:] ) )
lowercase__ : int = SkipList()
for i in range(10 ):
skip_list.insert(_lowerCAmelCase , _lowerCAmelCase )
assert is_sorted(list(_lowerCAmelCase ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(_lowerCAmelCase ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(_lowerCAmelCase ) )
def a_ ( ):
'''simple docstring'''
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def a_ ( ):
'''simple docstring'''
lowercase__ : Union[str, Any] = SkipList()
skip_list.insert(2 , '2' )
skip_list.insert(4 , '4' )
skip_list.insert(6 , '4' )
skip_list.insert(4 , '5' )
skip_list.insert(8 , '4' )
skip_list.insert(9 , '4' )
skip_list.delete(4 )
print(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 77
|
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = "sample"
snake_case_ = 1e-2
@property
def A_ ( self : Dict ):
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
return {"sample": image}
@property
def A_ ( self : List[Any] ):
return (3, 32, 32)
@property
def A_ ( self : Dict ):
return (3, 32, 32)
def A_ ( self : Union[str, Any] ):
snake_case_ = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def A_ ( self : Any ):
pass
def A_ ( self : str ):
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def A_ ( self : Dict ):
# enable deterministic behavior for gradient checkpointing
snake_case_ ,snake_case_ = self.prepare_init_args_and_inputs_for_common()
snake_case_ = self.model_class(**lowercase_ )
model.to(lowercase_ )
assert not model.is_gradient_checkpointing and model.training
snake_case_ = model(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
snake_case_ = torch.randn_like(lowercase_ )
snake_case_ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
snake_case_ = self.model_class(**lowercase_ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase_ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
snake_case_ = model_a(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
snake_case_ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
snake_case_ = dict(model.named_parameters() )
snake_case_ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def A_ ( self : Tuple ):
snake_case_ ,snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(lowercase_ )
snake_case_ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def A_ ( self : Tuple ):
snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
snake_case_ = model.to(lowercase_ )
model.eval()
if torch_device == "mps":
snake_case_ = torch.manual_seed(0 )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(0 )
snake_case_ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case_ = image.to(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , sample_posterior=lowercase_ , generator=lowercase_ ).sample
snake_case_ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
snake_case_ = torch.tensor(
[
-4.0_078e-01,
-3.8_323e-04,
-1.2_681e-01,
-1.1_462e-01,
2.0_095e-01,
1.0_893e-01,
-8.8_247e-02,
-3.0_361e-01,
-9.8_644e-03,
] )
elif torch_device == "cpu":
snake_case_ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
snake_case_ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1e-2 ) )
@slow
class a ( unittest.TestCase ):
def A_ ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] ):
return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_ ) for s in shape] )}.npy"
def A_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Dict , lowercase_ : List[Any]=0 , lowercase_ : Union[str, Any]=(4, 3, 512, 512) , lowercase_ : Optional[Any]=False ):
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_ , lowercase_ ) ) ).to(lowercase_ ).to(lowercase_ )
return image
def A_ ( self : Any , lowercase_ : Dict="CompVis/stable-diffusion-v1-4" , lowercase_ : List[str]=False ):
snake_case_ = '''fp16''' if fpaa else None
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = AutoencoderKL.from_pretrained(
lowercase_ , subfolder='''vae''' , torch_dtype=lowercase_ , revision=lowercase_ , )
model.to(lowercase_ ).eval()
return model
def A_ ( self : Any , lowercase_ : int=0 ):
if torch_device == "mps":
return torch.manual_seed(lowercase_ )
return torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , fpaa=lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[Any] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : List[str] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model.encode(lowercase_ ).latent_dist
snake_case_ = dist.sample(generator=lowercase_ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
snake_case_ = sample[0, -1, -3:, -3:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
snake_case_ = 3e-3 if torch_device != '''mps''' else 1e-2
assert torch_all_close(lowercase_ , lowercase_ , atol=lowercase_ )
| 56
| 0
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
snake_case_ = logging.getLogger(__name__)
@dataclass
class A_ :
"""simple docstring"""
__UpperCamelCase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
__UpperCamelCase = field(default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether tp freeze the encoder."""} )
__UpperCamelCase = field(default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Whether to freeze the embeddings."""} )
@dataclass
class A_ :
"""simple docstring"""
__UpperCamelCase = field(
metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""} )
__UpperCamelCase = field(
default="""summarization""" , metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""} , )
__UpperCamelCase = field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__UpperCamelCase = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total sequence length for target text after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__UpperCamelCase = field(
default=1_42 , metadata={
"""help""": (
"""The maximum total sequence length for validation target text after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded. """
"""This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
"""during ``evaluate`` and ``predict``."""
)
} , )
__UpperCamelCase = field(
default=1_42 , metadata={
"""help""": (
"""The maximum total sequence length for test target text after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
__UpperCamelCase = field(default=-1 , metadata={"""help""": """# training examples. -1 means use all."""} )
__UpperCamelCase = field(default=-1 , metadata={"""help""": """# validation examples. -1 means use all."""} )
__UpperCamelCase = field(default=-1 , metadata={"""help""": """# test examples. -1 means use all."""} )
__UpperCamelCase = field(default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Source language id for translation."""} )
__UpperCamelCase = field(default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """Target language id for translation."""} )
__UpperCamelCase = field(default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """# num_beams to use for evaluation."""} )
__UpperCamelCase = field(
default=SCREAMING_SNAKE_CASE_ , metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""} , )
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(lowercase_ , os.path.join(lowercase_ , F"""{split}_results.json""" ) )
def _lowerCAmelCase ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
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 , UpperCAmelCase , UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(lowercase_ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info('Training/evaluation parameters %s' , lowercase_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCAmelCase = ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
for p in extra_model_params:
if getattr(lowercase_ , lowercase_ , lowercase_ ):
assert hasattr(lowercase_ , lowercase_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(lowercase_ , lowercase_ , getattr(lowercase_ , lowercase_ ) )
UpperCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
UpperCAmelCase = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf='.ckpt' in model_args.model_name_or_path , config=lowercase_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(lowercase_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
UpperCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(lowercase_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(lowercase_ , lowercase_ ):
UpperCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
UpperCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(lowercase_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
UpperCAmelCase = SeqaSeqDataset
# Get datasets
UpperCAmelCase = (
dataset_class(
lowercase_ , type_path='train' , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '' , )
if training_args.do_train
else None
)
UpperCAmelCase = (
dataset_class(
lowercase_ , type_path='val' , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '' , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
UpperCAmelCase = (
dataset_class(
lowercase_ , type_path='test' , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or '' , )
if training_args.do_predict
else None
)
# Initialize our Trainer
UpperCAmelCase = (
build_compute_metrics_fn(data_args.task , lowercase_ ) if training_args.predict_with_generate else None
)
UpperCAmelCase = SeqaSeqTrainer(
model=lowercase_ , args=lowercase_ , data_args=lowercase_ , train_dataset=lowercase_ , eval_dataset=lowercase_ , data_collator=SeqaSeqDataCollator(
lowercase_ , lowercase_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=lowercase_ , tokenizer=lowercase_ , )
UpperCAmelCase = {}
# Training
if training_args.do_train:
logger.info('*** Train ***' )
UpperCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
UpperCAmelCase = train_result.metrics
UpperCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics('train' , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , 'trainer_state.json' ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
UpperCAmelCase = trainer.evaluate(metric_key_prefix='val' )
UpperCAmelCase = data_args.n_val
UpperCAmelCase = round(metrics['val_loss'] , 4 )
if trainer.is_world_process_zero():
handle_metrics('val' , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.do_predict:
logger.info('*** Predict ***' )
UpperCAmelCase = trainer.predict(test_dataset=lowercase_ , metric_key_prefix='test' )
UpperCAmelCase = test_output.metrics
UpperCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
UpperCAmelCase = round(metrics['test_loss'] , 4 )
handle_metrics('test' , lowercase_ , training_args.output_dir )
all_metrics.update(lowercase_ )
if training_args.predict_with_generate:
UpperCAmelCase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ )
UpperCAmelCase = lmap(str.strip , lowercase_ )
write_txt_file(lowercase_ , os.path.join(training_args.output_dir , 'test_generations.txt' ) )
if trainer.is_world_process_zero():
save_json(lowercase_ , os.path.join(training_args.output_dir , 'all_results.json' ) )
return all_metrics
def _lowerCAmelCase ( lowercase_ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 78
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class a ( _lowerCamelCase ):
snake_case_ = 42
@flax_register_to_config
class a ( nn.Module , _lowerCamelCase , _lowerCamelCase ):
snake_case_ = 32
snake_case_ = 4
snake_case_ = 4
snake_case_ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
snake_case_ = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
snake_case_ = False
snake_case_ = (320, 640, 1_280, 1_280)
snake_case_ = 2
snake_case_ = 8
snake_case_ = None
snake_case_ = 1_280
snake_case_ = 0.0
snake_case_ = False
snake_case_ = jnp.floataa
snake_case_ = True
snake_case_ = 0
snake_case_ = False
def A_ ( self : Optional[int] , lowercase_ : jax.random.KeyArray ):
# init input tensors
snake_case_ = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case_ = jnp.zeros(lowercase_ , dtype=jnp.floataa )
snake_case_ = jnp.ones((1,) , dtype=jnp.intaa )
snake_case_ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case_ ,snake_case_ = jax.random.split(lowercase_ )
snake_case_ = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(lowercase_ , lowercase_ , lowercase_ , lowercase_ )["params"]
def A_ ( self : List[str] ):
snake_case_ = self.block_out_channels
snake_case_ = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
snake_case_ = self.num_attention_heads or self.attention_head_dim
# input
snake_case_ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case_ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case_ = FlaxTimestepEmbedding(lowercase_ , dtype=self.dtype )
snake_case_ = self.only_cross_attention
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case_ = []
snake_case_ = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
snake_case_ = output_channel
snake_case_ = block_out_channels[i]
snake_case_ = i == len(lowercase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case_ = FlaxCrossAttnDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowercase_ )
snake_case_ = down_blocks
# mid
snake_case_ = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
snake_case_ = []
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
snake_case_ = output_channel
snake_case_ = reversed_block_out_channels[i]
snake_case_ = reversed_block_out_channels[min(i + 1 , len(lowercase_ ) - 1 )]
snake_case_ = i == len(lowercase_ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
snake_case_ = FlaxCrossAttnUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(lowercase_ )
snake_case_ = output_channel
snake_case_ = up_blocks
# out
snake_case_ = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
snake_case_ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int , lowercase_ : Any , lowercase_ : int=None , lowercase_ : Any=None , lowercase_ : bool = True , lowercase_ : bool = False , ):
# 1. time
if not isinstance(lowercase_ , jnp.ndarray ):
snake_case_ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowercase_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case_ = timesteps.astype(dtype=jnp.floataa )
snake_case_ = jnp.expand_dims(lowercase_ , 0 )
snake_case_ = self.time_proj(lowercase_ )
snake_case_ = self.time_embedding(lowercase_ )
# 2. pre-process
snake_case_ = jnp.transpose(lowercase_ , (0, 2, 3, 1) )
snake_case_ = self.conv_in(lowercase_ )
# 3. down
snake_case_ = (sample,)
for down_block in self.down_blocks:
if isinstance(lowercase_ , lowercase_ ):
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
else:
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
snake_case_ = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowercase_ , lowercase_ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
snake_case_ = new_down_block_res_samples
# 4. mid
snake_case_ = self.mid_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
snake_case_ = down_block_res_samples[-(self.layers_per_block + 1) :]
snake_case_ = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = up_block(
lowercase_ , temb=lowercase_ , encoder_hidden_states=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train , )
else:
snake_case_ = up_block(lowercase_ , temb=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train )
# 6. post-process
snake_case_ = self.conv_norm_out(lowercase_ )
snake_case_ = nn.silu(lowercase_ )
snake_case_ = self.conv_out(lowercase_ )
snake_case_ = jnp.transpose(lowercase_ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowercase_ )
| 56
| 0
|
'''simple docstring'''
def __lowercase ( __lowercase , __lowercase ) -> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
_A = str(bin(__lowercase ) )
binary_number += "0" * shift_amount
return binary_number
def __lowercase ( __lowercase , __lowercase ) -> str:
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
_A = str(bin(__lowercase ) )[2:]
if shift_amount >= len(__lowercase ):
return "0b0"
_A = binary_number[: len(__lowercase ) - shift_amount]
return "0b" + shifted_binary_number
def __lowercase ( __lowercase , __lowercase ) -> str:
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
_A = "0" + str(bin(__lowercase ) ).strip("-" )[2:]
else: # Get binary (2's complement) representation of negative number
_A = len(bin(__lowercase )[3:] ) # Find 2's complement of number
_A = bin(abs(__lowercase ) - (1 << binary_number_length) )[3:]
_A = (
"1" + "0" * (binary_number_length - len(__lowercase )) + binary_number
)
if shift_amount >= len(__lowercase ):
return "0b" + binary_number[0] * len(__lowercase )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(__lowercase ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79
|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a : Dict = (720, 1280) # Height, Width
a : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a : Dict = 1 / 100
a : str = ''
a : Any = ''
a : Optional[int] = ''
a : List[str] = 250
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dataset(__UpperCAmelCase, __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
snake_case_ = random.sample(range(len(__UpperCAmelCase ) ), 4 )
snake_case_ ,snake_case_ ,snake_case_ = update_image_and_anno(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, filter_scale=__UpperCAmelCase, )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ = random_chars(32 )
snake_case_ = path.split(os.sep )[-1].rsplit('''.''', 1 )[0]
snake_case_ = F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(F"{file_root}.jpg", __UpperCAmelCase, [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
snake_case_ = []
for anno in new_annos:
snake_case_ = anno[3] - anno[1]
snake_case_ = anno[4] - anno[2]
snake_case_ = anno[1] + width / 2
snake_case_ = anno[2] + height / 2
snake_case_ = F"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(F"{file_root}.txt", '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> tuple[list, list]:
'''simple docstring'''
snake_case_ = []
snake_case_ = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase, '''*.txt''' ) ):
snake_case_ = label_file.split(os.sep )[-1].rsplit('''.''', 1 )[0]
with open(__UpperCAmelCase ) as in_file:
snake_case_ = in_file.readlines()
snake_case_ = os.path.join(__UpperCAmelCase, F"{label_name}.jpg" )
snake_case_ = []
for obj_list in obj_lists:
snake_case_ = obj_list.rstrip('''\n''' ).split(''' ''' )
snake_case_ = float(obj[1] ) - float(obj[3] ) / 2
snake_case_ = float(obj[2] ) - float(obj[4] ) / 2
snake_case_ = float(obj[1] ) + float(obj[3] ) / 2
snake_case_ = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, ) -> tuple[list, list, str]:
'''simple docstring'''
snake_case_ = np.zeros([output_size[0], output_size[1], 3], dtype=np.uinta )
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = int(scale_x * output_size[1] )
snake_case_ = int(scale_y * output_size[0] )
snake_case_ = []
snake_case_ = []
for i, index in enumerate(__UpperCAmelCase ):
snake_case_ = all_img_list[index]
path_list.append(__UpperCAmelCase )
snake_case_ = all_annos[index]
snake_case_ = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = bbox[2] * scale_y
snake_case_ = bbox[3] * scale_x
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case_ = cva.resize(__UpperCAmelCase, (output_size[1] - divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = bbox[2] * scale_y
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = bbox[3] * scale_x
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case_ = cva.resize(
__UpperCAmelCase, (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case_ = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case_ = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 56
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase_ ( a__ , unittest.TestCase ):
__UpperCAmelCase = DiTPipeline
__UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCAmelCase = False
def __a ( self ):
torch.manual_seed(0 )
UpperCamelCase__ = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=a , activation_fn="gelu-approximate" , num_embeds_ada_norm=10_00 , norm_type="ada_norm_zero" , norm_elementwise_affine=a , )
UpperCamelCase__ = AutoencoderKL()
UpperCamelCase__ = DDIMScheduler()
UpperCamelCase__ = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def __a ( self , a , a=0 ):
if str(a ).startswith("mps" ):
UpperCamelCase__ = torch.manual_seed(a )
else:
UpperCamelCase__ = torch.Generator(device=a ).manual_seed(a )
UpperCamelCase__ = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def __a ( self ):
UpperCamelCase__ = "cpu"
UpperCamelCase__ = self.get_dummy_components()
UpperCamelCase__ = self.pipeline_class(**a )
pipe.to(a )
pipe.set_progress_bar_config(disable=a )
UpperCamelCase__ = self.get_dummy_inputs(a )
UpperCamelCase__ = pipe(**a ).images
UpperCamelCase__ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
UpperCamelCase__ = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
UpperCamelCase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(a , 1e-3 )
def __a ( self ):
self._test_inference_batch_single_identical(relax_max_difference=a , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def __a ( self ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class lowercase_ ( unittest.TestCase ):
def __a ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self ):
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
UpperCamelCase__ = ["vase", "umbrella", "white shark", "white wolf"]
UpperCamelCase__ = pipe.get_label_ids(a )
UpperCamelCase__ = pipe(a , generator=a , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(a , a ):
UpperCamelCase__ = load_numpy(
f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-2
def __a ( self ):
UpperCamelCase__ = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
UpperCamelCase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
UpperCamelCase__ = ["vase", "umbrella"]
UpperCamelCase__ = pipe.get_label_ids(a )
UpperCamelCase__ = torch.manual_seed(0 )
UpperCamelCase__ = pipe(a , generator=a , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(a , a ):
UpperCamelCase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
f'''/dit/{word}_512.npy''' )
assert np.abs((expected_image - image).max() ) < 1e-1
| 80
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class a :
@staticmethod
def A_ ( *lowercase_ : int , **lowercase_ : str ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class a ( unittest.TestCase ):
snake_case_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def A_ ( self : Any , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str] ):
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , image_processor=lowercase_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def A_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : int ):
snake_case_ = object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' , threshold=0.0 )
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = [
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
]
snake_case_ = object_detector(lowercase_ , threshold=0.0 )
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for outputs in batch_outputs:
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
@require_tf
@unittest.skip('''Object detection not implemented in TF''' )
def A_ ( self : int ):
pass
@require_torch
def A_ ( self : Tuple ):
snake_case_ = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
] , )
@require_torch
@slow
def A_ ( self : Optional[int] ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : Tuple ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : str ):
snake_case_ = 0.9985
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=lowercase_ )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def A_ ( self : Dict ):
snake_case_ = '''Narsil/layoutlmv3-finetuned-funsd'''
snake_case_ = 0.9993
snake_case_ = pipeline('''object-detection''' , model=lowercase_ , threshold=lowercase_ )
snake_case_ = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
] , )
| 56
| 0
|
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowercase , int(b / 2 ) ) * actual_power(lowercase , int(b / 2 ) )
else:
return a * actual_power(lowercase , int(b / 2 ) ) * actual_power(lowercase , int(b / 2 ) )
def _A ( lowercase , lowercase ):
"""simple docstring"""
if b < 0:
return 1 / actual_power(lowercase , lowercase )
return actual_power(lowercase , lowercase )
if __name__ == "__main__":
print(power(-2, -3))
| 81
|
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class a :
def __init__( self : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any=13 , lowercase_ : Optional[Any]=7 , lowercase_ : Optional[Any]=True , lowercase_ : Dict=True , lowercase_ : Tuple=False , lowercase_ : Optional[Any]=True , lowercase_ : Any=99 , lowercase_ : Union[str, Any]=64 , lowercase_ : str=5 , lowercase_ : int=4 , lowercase_ : List[Any]=64 , lowercase_ : Dict="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : List[Any]=16 , lowercase_ : str=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
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_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def A_ ( self : List[str] ):
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def A_ ( self : str ):
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_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Tuple ):
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def A_ ( self : Any , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int] ):
snake_case_ = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 : Tuple , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.num_labels
snake_case_ = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Any , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.num_choices
snake_case_ = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : int , lowercase_ : List[str] ):
snake_case_ = self.num_labels
snake_case_ = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Union[str, Any] ):
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_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = True
def A_ ( self : Tuple ):
snake_case_ = MPNetModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def A_ ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ):
snake_case_ = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
snake_case_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
snake_case_ = model(lowercase_ )[0]
snake_case_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowercase_ )
snake_case_ = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1e-4 ) )
| 56
| 0
|
from collections import defaultdict
from math import ceil, sqrt
def _UpperCAmelCase ( snake_case = 1_00_00_00 , snake_case = 10 ):
"""simple docstring"""
_lowerCAmelCase = defaultdict(snake_case )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
_lowerCAmelCase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
_lowerCAmelCase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(snake_case , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f"{solution() = }")
| 82
|
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class a ( _lowerCamelCase ):
def A_ ( self : str ):
snake_case_ = tempfile.mkdtemp()
snake_case_ = 8
# DPR tok
snake_case_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
snake_case_ = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
snake_case_ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
snake_case_ = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
snake_case_ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case_ = {'''unk_token''': '''<unk>'''}
snake_case_ = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def A_ ( self : Union[str, Any] ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : Union[str, Any] ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : int ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def A_ ( self : str ):
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def A_ ( self : str ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def A_ ( self : str , lowercase_ : bool ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
snake_case_ = os.path.join(self.tmpdirname , '''dataset''' )
snake_case_ = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , lowercase_ ) , )
return retriever
def A_ ( self : Tuple ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
snake_case_ = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
snake_case_ = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
snake_case_ = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(lowercase_ , open(lowercase_ , '''wb''' ) )
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def A_ ( self : Optional[Any] ):
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : str ):
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = self.get_dummy_dataset()
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : int ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : str ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : Any ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : Any ):
snake_case_ = 1
snake_case_ = self.get_dummy_legacy_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : List[str] ):
import torch
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
snake_case_ ,snake_case_ ,snake_case_ = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , np.ndarray )
snake_case_ = retriever(
lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ , return_tensors='''pt''' , )
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : Tuple ):
snake_case_ = self.get_dpr_ctx_encoder_tokenizer()
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
retriever.set_ctx_encoder_tokenizer(lowercase_ )
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
self.assertEqual(
len(lowercase_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , lowercase_ ) # check for doc token related keys in dictionary.
| 56
| 0
|
'''simple docstring'''
import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler
snake_case_ : int = 16
snake_case_ : int = 32
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ = 1_6 , UpperCAmelCase_ = "bert-base-cased" ):
_UpperCamelCase : List[Any] = AutoTokenizer.from_pretrained(UpperCAmelCase_ )
_UpperCamelCase : List[Any] = load_dataset('glue' , 'mrpc' )
def tokenize_function(UpperCAmelCase_ ):
# max_length=None => use the model max length (it's actually the default)
_UpperCamelCase : Dict = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=UpperCAmelCase_ , max_length=UpperCAmelCase_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_UpperCamelCase : int = datasets.map(
UpperCAmelCase_ , batched=UpperCAmelCase_ , remove_columns=['idx', 'sentence1', 'sentence2'] , load_from_cache_file=UpperCAmelCase_ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_UpperCamelCase : Optional[Any] = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(UpperCAmelCase_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCAmelCase_ , padding='max_length' , max_length=1_2_8 , return_tensors='pt' )
return tokenizer.pad(UpperCAmelCase_ , padding='longest' , return_tensors='pt' )
# Instantiate dataloaders.
_UpperCamelCase : Any = DataLoader(
tokenized_datasets['train'] , shuffle=UpperCAmelCase_ , collate_fn=UpperCAmelCase_ , batch_size=UpperCAmelCase_ )
_UpperCamelCase : int = DataLoader(
tokenized_datasets['validation'] , shuffle=UpperCAmelCase_ , collate_fn=UpperCAmelCase_ , batch_size=UpperCAmelCase_ )
return train_dataloader, eval_dataloader
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
# Initialize accelerator
_UpperCamelCase : List[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_UpperCamelCase : Union[str, Any] = config['lr']
_UpperCamelCase : Optional[Any] = int(config['num_epochs'] )
_UpperCamelCase : str = int(config['seed'] )
_UpperCamelCase : List[Any] = int(config['batch_size'] )
_UpperCamelCase : int = args.model_name_or_path
set_seed(UpperCAmelCase_ )
_UpperCamelCase , _UpperCamelCase : Dict = get_dataloaders(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_UpperCamelCase : str = AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_ , return_dict=UpperCAmelCase_ )
# Instantiate optimizer
_UpperCamelCase : Tuple = (
AdamW
if accelerator.state.deepspeed_plugin is None
or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_UpperCamelCase : Union[str, Any] = optimizer_cls(params=model.parameters() , lr=UpperCAmelCase_ )
if accelerator.state.deepspeed_plugin is not None:
_UpperCamelCase : int = accelerator.state.deepspeed_plugin.deepspeed_config[
'gradient_accumulation_steps'
]
else:
_UpperCamelCase : List[Any] = 1
_UpperCamelCase : str = (len(UpperCAmelCase_ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_UpperCamelCase : Union[str, Any] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase_ , num_warmup_steps=0 , num_training_steps=UpperCAmelCase_ , )
else:
_UpperCamelCase : str = DummyScheduler(UpperCAmelCase_ , total_num_steps=UpperCAmelCase_ , warmup_num_steps=0 )
# 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.
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Dict = accelerator.prepare(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# We need to keep track of how many total steps we have iterated over
_UpperCamelCase : str = 0
# We also need to keep track of the stating epoch so files are named properly
_UpperCamelCase : int = 0
# Now we train the model
_UpperCamelCase : Any = evaluate.load('glue' , 'mrpc' )
_UpperCamelCase : Union[str, Any] = 0
_UpperCamelCase : str = {}
for epoch in range(UpperCAmelCase_ , UpperCAmelCase_ ):
model.train()
for step, batch in enumerate(UpperCAmelCase_ ):
_UpperCamelCase : Dict = model(**UpperCAmelCase_ )
_UpperCamelCase : Dict = outputs.loss
_UpperCamelCase : List[str] = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase_ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_UpperCamelCase : int = 0
for step, batch in enumerate(UpperCAmelCase_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_UpperCamelCase : Any = model(**UpperCAmelCase_ )
_UpperCamelCase : int = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_UpperCamelCase , _UpperCamelCase : List[str] = accelerator.gather(
(predictions, batch['labels']) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(UpperCAmelCase_ ) - 1:
_UpperCamelCase : Dict = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_UpperCamelCase : Union[str, Any] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=UpperCAmelCase_ , references=UpperCAmelCase_ , )
_UpperCamelCase : Any = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , UpperCAmelCase_ )
_UpperCamelCase : Optional[Any] = eval_metric['accuracy']
if best_performance < eval_metric["accuracy"]:
_UpperCamelCase : Dict = eval_metric['accuracy']
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , 'all_results.json' ) , 'w' ) as f:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
def A__ ( ):
_UpperCamelCase : Union[str, Any] = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.' )
parser.add_argument(
'--model_name_or_path' , type=UpperCAmelCase_ , default='bert-base-cased' , help='Path to pretrained model or model identifier from huggingface.co/models.' , required=UpperCAmelCase_ , )
parser.add_argument(
'--output_dir' , type=UpperCAmelCase_ , default='.' , help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' , )
parser.add_argument(
'--performance_lower_bound' , type=UpperCAmelCase_ , default=UpperCAmelCase_ , help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.' , )
parser.add_argument(
'--num_epochs' , type=UpperCAmelCase_ , default=3 , help='Number of train epochs.' , )
_UpperCamelCase : List[Any] = parser.parse_args()
_UpperCamelCase : Optional[int] = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}
training_function(UpperCAmelCase_ , UpperCAmelCase_ )
if __name__ == "__main__":
main()
| 83
|
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
a : Dict = None
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
a : str = {
'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',
},
'tokenizer_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
},
}
# TODO(PVP) - this should be removed in Transformers v5
a : List[Any] = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
class a ( _lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = TaTokenizer
snake_case_ = []
def __init__( self : List[Any] , lowercase_ : int=None , lowercase_ : Dict=None , lowercase_ : Dict="</s>" , lowercase_ : List[Any]="<unk>" , lowercase_ : int="<pad>" , lowercase_ : int=100 , lowercase_ : List[Any]=None , **lowercase_ : List[str] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
snake_case_ = [F"<extra_id_{i}>" for i in range(lowercase_ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
snake_case_ = len(set(filter(lambda lowercase_ : bool('''extra_id_''' in str(lowercase_ ) ) , lowercase_ ) ) )
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''' )
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , extra_ids=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ = vocab_file
snake_case_ = False if not self.vocab_file else True
snake_case_ = extra_ids
@staticmethod
def A_ ( lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : int ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
snake_case_ = TaTokenizerFast.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.''' , lowercase_ , )
return max_model_length
def A_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
logger.info(F"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def A_ ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
snake_case_ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def A_ ( self : int , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = [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 A_ ( self : Dict ):
return list(
set(filter(lambda lowercase_ : bool(re.search(R'''<extra_id_\d+>''' , lowercase_ ) ) is not None , self.additional_special_tokens ) ) )
def A_ ( self : Any ):
return [self.convert_tokens_to_ids(lowercase_ ) for token in self.get_sentinel_tokens()]
| 56
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|
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
| 84
|
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(__UpperCAmelCase ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
return min(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ = [90, 23, 6, 33, 21, 65, 123, 3_4423]
snake_case_ = math.log(len(__UpperCAmelCase ), 2 )
print('''Optimal value : ''', end='''''' )
print(minimax(0, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 56
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|
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
_SCREAMING_SNAKE_CASE : Tuple = tuple[int, int]
class _snake_case :
def __init__( self , a__ , a__ ) -> None:
'''simple docstring'''
snake_case_ = vertices
snake_case_ = {
(min(a__ ), max(a__ )): weight for edge, weight in edges.items()
}
def lowerCAmelCase__ ( self , a__ , a__ ) -> None:
'''simple docstring'''
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
snake_case_ = weight
def lowerCAmelCase__ ( self ) -> Graph:
'''simple docstring'''
snake_case_ = Graph({min(self.vertices )} , {} )
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
while len(subgraph.vertices ) < len(self.vertices ):
snake_case_ = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
snake_case_ = edge
snake_case_ = weight
subgraph.add_edge(a__ , a__ )
return subgraph
def UpperCamelCase_( snake_case : str = "p107_network.txt" ):
'''simple docstring'''
snake_case_ = os.path.abspath(os.path.dirname(snake_case ) )
snake_case_ = os.path.join(snake_case , snake_case )
snake_case_ = {}
snake_case_ = 42
snake_case_ = 42
snake_case_ = 42
with open(snake_case ) as f:
snake_case_ = f.read().strip().split("\n" )
snake_case_ = [line.split("," ) for line in data]
for edgea in range(1 , len(snake_case ) ):
for edgea in range(snake_case ):
if adjaceny_matrix[edgea][edgea] != "-":
snake_case_ = int(adjaceny_matrix[edgea][edgea] )
snake_case_ = Graph(set(range(len(snake_case ) ) ) , snake_case )
snake_case_ = graph.prims_algorithm()
snake_case_ = sum(graph.edges.values() )
snake_case_ = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F"{solution() = }")
| 85
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
elif "subsample" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ ,snake_case_ = emb.weight.shape
snake_case_ = nn.Linear(__UpperCAmelCase, __UpperCAmelCase, bias=__UpperCAmelCase )
snake_case_ = emb.weight.data
return lin_layer
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = torch.load(__UpperCAmelCase, map_location='''cpu''' )
snake_case_ = mam_aaa['''args''']
snake_case_ = mam_aaa['''model''']
snake_case_ = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__UpperCAmelCase )
rename_keys(__UpperCAmelCase )
snake_case_ = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case_ = args.share_decoder_input_output_embed
snake_case_ = [int(__UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case_ = SpeechaTextConfig(
vocab_size=__UpperCAmelCase, max_source_positions=args.max_source_positions, max_target_positions=args.max_target_positions, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', num_conv_layers=len(__UpperCAmelCase ), conv_channels=args.conv_channels, conv_kernel_sizes=__UpperCAmelCase, input_feat_per_channel=args.input_feat_per_channel, input_channels=args.input_channels, tie_word_embeddings=__UpperCAmelCase, num_beams=5, max_length=200, use_cache=__UpperCAmelCase, decoder_start_token_id=2, early_stopping=__UpperCAmelCase, )
snake_case_ = SpeechaTextForConditionalGeneration(__UpperCAmelCase )
snake_case_ ,snake_case_ = model.model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0 and not set(__UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F" but all the following weights are missing {missing}" )
if tie_embeds:
snake_case_ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ = lm_head_weights
model.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
a : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 56
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"""simple docstring"""
import enum
import shutil
import sys
lowerCamelCase__ , lowerCamelCase__ = shutil.get_terminal_size()
lowerCamelCase__ = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}
class A__ ( enum.Enum):
A_ : Union[str, Any] = 0
A_ : List[Any] = 1
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase="" ):
sys.stdout.write(str(_UpperCamelCase ) + end )
sys.stdout.flush()
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase="" ):
forceWrite(F"\u001b[{color}m{content}\u001b[0m" , _UpperCamelCase )
def __lowerCAmelCase ():
forceWrite('\r' )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
forceWrite(F"\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}" )
def __lowerCAmelCase ():
forceWrite(' ' * TERMINAL_WIDTH )
reset_cursor()
def __lowerCAmelCase ():
reset_cursor()
forceWrite('-' * TERMINAL_WIDTH )
| 86
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : str ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 56
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from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class snake_case_ ( __A ):
__A : List[Any] = "blenderbot-small"
__A : Tuple = ["past_key_values"]
__A : Union[str, Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Any , lowercase_ : Any=5_02_65 , lowercase_ : Optional[Any]=5_12 , lowercase_ : Optional[int]=8 , lowercase_ : Tuple=20_48 , lowercase_ : Any=16 , lowercase_ : Optional[int]=8 , lowercase_ : Any=20_48 , lowercase_ : Any=16 , lowercase_ : Tuple=0.0 , lowercase_ : Optional[Any]=0.0 , lowercase_ : Union[str, Any]=True , lowercase_ : Optional[Any]=True , lowercase_ : int="gelu" , lowercase_ : str=5_12 , lowercase_ : str=0.1 , lowercase_ : Optional[int]=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : str=1 , lowercase_ : int=False , lowercase_ : Optional[int]=0 , lowercase_ : Tuple=1 , lowercase_ : int=2 , lowercase_ : List[str]=2 , **lowercase_ : Tuple , ) -> Union[str, Any]:
lowercase__ : Any = vocab_size
lowercase__ : int = max_position_embeddings
lowercase__ : Optional[Any] = d_model
lowercase__ : List[str] = encoder_ffn_dim
lowercase__ : List[str] = encoder_layers
lowercase__ : List[Any] = encoder_attention_heads
lowercase__ : List[str] = decoder_ffn_dim
lowercase__ : Optional[Any] = decoder_layers
lowercase__ : Union[str, Any] = decoder_attention_heads
lowercase__ : int = dropout
lowercase__ : Optional[int] = attention_dropout
lowercase__ : Dict = activation_dropout
lowercase__ : Union[str, Any] = activation_function
lowercase__ : Dict = init_std
lowercase__ : int = encoder_layerdrop
lowercase__ : List[str] = decoder_layerdrop
lowercase__ : str = use_cache
lowercase__ : Dict = encoder_layers
lowercase__ : int = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class snake_case_ ( __A ):
@property
def __UpperCamelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : str = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowercase__ : Tuple = {0: "batch"}
lowercase__ : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
lowercase__ : Dict = {0: "batch", 1: "decoder_sequence"}
lowercase__ : Tuple = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase__ : Optional[int] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowercase__ , lowercase__ : Any = self.num_layers
for i in range(lowercase_ ):
lowercase__ : List[str] = {0: "batch", 2: "past_sequence + sequence"}
lowercase__ : Any = {0: "batch", 2: "past_sequence + sequence"}
else:
lowercase__ : int = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def __UpperCamelCase ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : Dict = super().outputs
else:
lowercase__ : List[str] = super(lowercase_ , self ).outputs
if self.use_past:
lowercase__ , lowercase__ : Optional[Any] = self.num_layers
for i in range(lowercase_ ):
lowercase__ : Dict = {0: "batch", 2: "past_sequence + sequence"}
lowercase__ : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __UpperCamelCase ( self : Tuple , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
lowercase__ : Optional[int] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
lowercase__ : str = seq_length if not self.use_past else 1
lowercase__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase__ : Union[str, Any] = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowercase__ : Union[str, Any] = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowercase__ , lowercase__ : Union[str, Any] = common_inputs["input_ids"].shape
lowercase__ : Optional[int] = common_inputs["decoder_input_ids"].shape[1]
lowercase__ , lowercase__ : List[str] = self.num_attention_heads
lowercase__ : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ : List[str] = decoder_seq_length + 3
lowercase__ : Union[str, Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase__ : Tuple = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
lowercase__ : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase__ , lowercase__ : List[str] = self.num_layers
lowercase__ : List[Any] = min(lowercase_ , lowercase_ )
lowercase__ : List[Any] = max(lowercase_ , lowercase_ ) - min_num_layers
lowercase__ : int = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
lowercase__ : str = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
lowercase__ : Any = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowercase__ , lowercase__ : str = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowercase__ : Dict = seqlen + 2
lowercase__ , lowercase__ : List[str] = self.num_layers
lowercase__ , lowercase__ : Optional[Any] = self.num_attention_heads
lowercase__ : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ : Optional[int] = common_inputs["attention_mask"].dtype
lowercase__ : List[Any] = torch.cat(
[common_inputs["attention_mask"], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
lowercase__ : Dict = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def __UpperCamelCase ( self : List[Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase__ : List[Any] = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase__ : Optional[Any] = tokenizer.num_special_tokens_to_add(lowercase_ )
lowercase__ : List[Any] = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
lowercase__ : int = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase__ : Union[str, Any] = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def __UpperCamelCase ( self : str , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : str = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
elif self.task == "causal-lm":
lowercase__ : List[str] = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
lowercase__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def __UpperCamelCase ( self : Tuple , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : List[Any] ) -> Any:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : Dict = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
lowercase__ : str = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
| 87
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a : int = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
config.addinivalue_line(
'''markers''', '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''', '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''', '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''', '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''', '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''', '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
snake_case_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCAmelCase, id=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if exitstatus == 5:
snake_case_ = 0
# Doctest custom flag to ignore output.
a : Union[str, Any] = doctest.register_optionflag('IGNORE_RESULT')
a : Optional[int] = doctest.OutputChecker
class a ( _lowerCamelCase ):
def A_ ( self : List[Any] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Optional[int] ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , lowercase_ , lowercase_ , lowercase_ )
a : List[Any] = CustomOutputChecker
a : Optional[int] = HfDoctestModule
a : Tuple = HfDocTestParser
| 56
| 0
|
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__lowerCAmelCase : List[Any] = {'configuration_gpt_neox': ['GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXConfig']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : List[Any] = ['GPTNeoXTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[int] = [
'GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTNeoXForCausalLM',
'GPTNeoXForQuestionAnswering',
'GPTNeoXForSequenceClassification',
'GPTNeoXForTokenClassification',
'GPTNeoXLayer',
'GPTNeoXModel',
'GPTNeoXPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__lowerCAmelCase : Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 88
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
a : Dict = logging.get_logger(__name__)
a : List[str] = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class a ( _lowerCamelCase ):
snake_case_ = "marian"
snake_case_ = ["past_key_values"]
snake_case_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , lowercase_ : Optional[Any]=5_8101 , lowercase_ : Dict=None , lowercase_ : List[str]=1024 , lowercase_ : Optional[Any]=12 , lowercase_ : int=4096 , lowercase_ : Any=16 , lowercase_ : Optional[int]=12 , lowercase_ : str=4096 , lowercase_ : Union[str, Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Union[str, Any]=True , lowercase_ : int="gelu" , lowercase_ : Dict=1024 , lowercase_ : int=0.1 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.02 , lowercase_ : int=5_8100 , lowercase_ : Optional[Any]=False , lowercase_ : Any=5_8100 , lowercase_ : Optional[int]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=True , **lowercase_ : Any , ):
snake_case_ = vocab_size
snake_case_ = decoder_vocab_size or vocab_size
snake_case_ = max_position_embeddings
snake_case_ = d_model
snake_case_ = encoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class a ( _lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A_ ( self : Union[str, Any] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ = {0: '''batch'''}
snake_case_ = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A_ ( self : Dict ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super().outputs
else:
snake_case_ = super(lowercase_ , self ).outputs
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A_ ( self : Dict , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
snake_case_ = seq_length if not self.use_past else 1
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case_ = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
snake_case_ = common_inputs['''decoder_input_ids'''].shape[1]
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = decoder_seq_length + 3
snake_case_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
snake_case_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ ,snake_case_ = self.num_layers
snake_case_ = min(lowercase_ , lowercase_ )
snake_case_ = max(lowercase_ , lowercase_ ) - min_num_layers
snake_case_ = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
snake_case_ = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def A_ ( self : Union[str, Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case_ = seqlen + 2
snake_case_ ,snake_case_ = self.num_layers
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = common_inputs['''attention_mask'''].dtype
snake_case_ = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
snake_case_ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def A_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ = tokenizer.num_special_tokens_to_add(lowercase_ )
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def A_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
snake_case_ = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def A_ ( self : Dict , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : List[str] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
snake_case_ = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
@property
def A_ ( self : List[str] ):
return 1e-4
| 56
| 0
|
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__lowerCAmelCase = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
'''self.proj''': '''output.dense''',
'''attention.self_mask''': '''attn_mask''',
'''mlp.fc1''': '''intermediate.dense''',
'''mlp.fc2''': '''output.dense''',
'''norm1''': '''layernorm_before''',
'''norm2''': '''layernorm_after''',
'''bn0''': '''batch_norm''',
}
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=False ) -> Any:
_a , _a : Union[str, Any] = create_model(
'HTSAT-tiny' , 'roberta' , lowerCAmelCase_ , precision='fp32' , device='cuda:0' if torch.cuda.is_available() else 'cpu' , enable_fusion=lowerCAmelCase_ , fusion_type='aff_2d' if enable_fusion else None , )
return model, model_cfg
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
_a : Optional[int] = {}
_a : Tuple = r'.*sequential.(\d+).*'
_a : int = r'.*_projection.(\d+).*'
for key, value in state_dict.items():
# check if any key needs to be modified
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
_a : Union[str, Any] = key.replace(lowerCAmelCase_ , lowerCAmelCase_ )
if re.match(lowerCAmelCase_ , lowerCAmelCase_ ):
# replace sequential layers with list
_a : List[str] = re.match(lowerCAmelCase_ , lowerCAmelCase_ ).group(1 )
_a : Optional[Any] = key.replace(f"""sequential.{sequential_layer}.""" , f"""layers.{int(lowerCAmelCase_ )//3}.linear.""" )
elif re.match(lowerCAmelCase_ , lowerCAmelCase_ ):
_a : str = int(re.match(lowerCAmelCase_ , lowerCAmelCase_ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
_a : Optional[Any] = 1 if projecton_layer == 0 else 2
_a : int = key.replace(f"""_projection.{projecton_layer}.""" , f"""_projection.linear{transformers_projection_layer}.""" )
if "audio" and "qkv" in key:
# split qkv into query key and value
_a : str = value
_a : List[str] = mixed_qkv.size(0 ) // 3
_a : str = mixed_qkv[:qkv_dim]
_a : int = mixed_qkv[qkv_dim : qkv_dim * 2]
_a : Any = mixed_qkv[qkv_dim * 2 :]
_a : List[Any] = query_layer
_a : Union[str, Any] = key_layer
_a : Tuple = value_layer
else:
_a : Dict = value
return model_state_dict
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False ) -> int:
_a , _a : Optional[Any] = init_clap(lowerCAmelCase_ , enable_fusion=lowerCAmelCase_ )
clap_model.eval()
_a : Tuple = clap_model.state_dict()
_a : Optional[int] = rename_state_dict(lowerCAmelCase_ )
_a : List[str] = ClapConfig()
_a : Tuple = enable_fusion
_a : int = ClapModel(lowerCAmelCase_ )
# ignore the spectrogram embedding layer
model.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
model.save_pretrained(lowerCAmelCase_ )
transformers_config.save_pretrained(lowerCAmelCase_ )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
__lowerCAmelCase = parser.parse_args()
convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)
| 89
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = CycleDiffusionPipeline
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
snake_case_ = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A_ ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ = 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 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
snake_case_ = 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 , )
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case_ = CLIPTextModel(lowercase_ )
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 A_ ( self : Any , lowercase_ : int , lowercase_ : Optional[Any]=0 ):
snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ = image / 2 + 0.5
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(lowercase_ )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def A_ ( self : Union[str, Any] ):
snake_case_ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , '''half''' ):
snake_case_ = module.half()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Optional[int] ):
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def A_ ( self : List[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_save_load_optional_components()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Union[str, Any] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def A_ ( self : List[str] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
assert np.abs(image - expected_image ).max() < 2e-2
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|
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ) -> Tuple:
"""simple docstring"""
if (
(cp >= 0x4_e00 and cp <= 0x9_fff)
or (cp >= 0x3_400 and cp <= 0x4_dbf) #
or (cp >= 0x20_000 and cp <= 0x2a_6df) #
or (cp >= 0x2a_700 and cp <= 0x2b_73f) #
or (cp >= 0x2b_740 and cp <= 0x2b_81f) #
or (cp >= 0x2b_820 and cp <= 0x2c_eaf) #
or (cp >= 0xf_900 and cp <= 0xf_aff)
or (cp >= 0x2f_800 and cp <= 0x2f_a1f) #
): #
return True
return False
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> List[str]:
"""simple docstring"""
for char in word:
__lowerCamelCase = ord(UpperCamelCase__ )
if not _is_chinese_char(UpperCamelCase__ ):
return 0
return 1
def lowerCamelCase_ ( UpperCamelCase__ : List[str] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = set()
for token in tokens:
__lowerCamelCase = len(UpperCamelCase__ ) > 1 and is_chinese(UpperCamelCase__ )
if chinese_word:
word_set.add(UpperCamelCase__ )
__lowerCamelCase = list(UpperCamelCase__ )
return word_list
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : set() ) -> Tuple:
"""simple docstring"""
if not chinese_word_set:
return bert_tokens
__lowerCamelCase = max([len(UpperCamelCase__ ) for w in chinese_word_set] )
__lowerCamelCase = bert_tokens
__lowerCamelCase , __lowerCamelCase = 0, len(UpperCamelCase__ )
while start < end:
__lowerCamelCase = True
if is_chinese(bert_word[start] ):
__lowerCamelCase = min(end - start , UpperCamelCase__ )
for i in range(UpperCamelCase__ , 1 , -1 ):
__lowerCamelCase = ''.join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
__lowerCamelCase = '##' + bert_word[j]
__lowerCamelCase = start + i
__lowerCamelCase = False
break
if single_word:
start += 1
return bert_word
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : LTP , UpperCamelCase__ : BertTokenizer ) -> Optional[int]:
"""simple docstring"""
__lowerCamelCase = []
for i in range(0 , len(UpperCamelCase__ ) , 100 ):
__lowerCamelCase = ltp_tokenizer.seg(lines[i : i + 100] )[0]
__lowerCamelCase = [get_chinese_word(UpperCamelCase__ ) for r in res]
ltp_res.extend(UpperCamelCase__ )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
__lowerCamelCase = []
for i in range(0 , len(UpperCamelCase__ ) , 100 ):
__lowerCamelCase = bert_tokenizer(lines[i : i + 100] , add_special_tokens=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=512 )
bert_res.extend(res['input_ids'] )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
__lowerCamelCase = []
for input_ids, chinese_word in zip(UpperCamelCase__ , UpperCamelCase__ ):
__lowerCamelCase = []
for id in input_ids:
__lowerCamelCase = bert_tokenizer._convert_id_to_token(UpperCamelCase__ )
input_tokens.append(UpperCamelCase__ )
__lowerCamelCase = add_sub_symbol(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(UpperCamelCase__ ):
if token[:2] == "##":
__lowerCamelCase = token[2:]
# save chinese tokens' pos
if len(UpperCamelCase__ ) == 1 and _is_chinese_char(ord(UpperCamelCase__ ) ):
ref_id.append(UpperCamelCase__ )
ref_ids.append(UpperCamelCase__ )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
return ref_ids
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> Optional[int]:
"""simple docstring"""
with open(args.file_name , 'r' , encoding='utf-8' ) as f:
__lowerCamelCase = f.readlines()
__lowerCamelCase = [line.strip() for line in data if len(UpperCamelCase__ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
__lowerCamelCase = LTP(args.ltp ) # faster in GPU device
__lowerCamelCase = BertTokenizer.from_pretrained(args.bert )
__lowerCamelCase = prepare_ref(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
with open(args.save_path , 'w' , encoding='utf-8' ) as f:
__lowerCamelCase = [json.dumps(UpperCamelCase__ ) + '\n' for ref in ref_ids]
f.writelines(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
__A = parser.parse_args()
main(args)
| 90
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class a ( _lowerCamelCase ):
snake_case_ = "big_bird"
def __init__( self : Union[str, Any] , lowercase_ : List[Any]=5_0358 , lowercase_ : Tuple=768 , lowercase_ : Dict=12 , lowercase_ : str=12 , lowercase_ : Tuple=3072 , lowercase_ : Any="gelu_new" , lowercase_ : Optional[Any]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=4096 , lowercase_ : List[Any]=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[int]=1e-12 , lowercase_ : Tuple=True , lowercase_ : Tuple=0 , lowercase_ : str=1 , lowercase_ : Union[str, Any]=2 , lowercase_ : Optional[Any]=66 , lowercase_ : Optional[int]="block_sparse" , lowercase_ : Any=True , lowercase_ : List[str]=False , lowercase_ : Any=64 , lowercase_ : Tuple=3 , lowercase_ : Tuple=None , **lowercase_ : Tuple , ):
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , sep_token_id=lowercase_ , **lowercase_ , )
snake_case_ = vocab_size
snake_case_ = max_position_embeddings
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_ = type_vocab_size
snake_case_ = layer_norm_eps
snake_case_ = use_cache
snake_case_ = rescale_embeddings
snake_case_ = attention_type
snake_case_ = use_bias
snake_case_ = block_size
snake_case_ = num_random_blocks
snake_case_ = classifier_dropout
class a ( _lowerCamelCase ):
@property
def A_ ( self : str ):
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 56
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|
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "tokenizer"]
__UpperCamelCase = "CLIPImageProcessor"
__UpperCamelCase = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self : List[Any] , lowercase_ : Dict=None , lowercase_ : List[str]=None , **lowercase_ : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowercase_ , )
SCREAMING_SNAKE_CASE_ : List[Any] = kwargs.pop('''feature_extractor''')
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 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__(lowercase_ , lowercase_)
def __call__( self : str , lowercase_ : int=None , lowercase_ : int=None , lowercase_ : Dict=None , **lowercase_ : 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:
SCREAMING_SNAKE_CASE_ : Tuple = self.tokenizer(lowercase_ , return_tensors=lowercase_ , **lowercase_)
if images is not None:
SCREAMING_SNAKE_CASE_ : Dict = self.image_processor(lowercase_ , return_tensors=lowercase_ , **lowercase_)
if text is not None and images is not None:
SCREAMING_SNAKE_CASE_ : str = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase_) , tensor_type=lowercase_)
def _SCREAMING_SNAKE_CASE ( self : str , *lowercase_ : int , **lowercase_ : Any):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_)
def _SCREAMING_SNAKE_CASE ( self : List[str] , *lowercase_ : Union[str, Any] , **lowercase_ : Optional[int]):
'''simple docstring'''
return self.tokenizer.decode(*lowercase_ , **lowercase_)
@property
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE_ : Any = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[int]):
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , lowercase_ , )
return self.image_processor_class
@property
def _SCREAMING_SNAKE_CASE ( self : List[str]):
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowercase_ , )
return self.image_processor
| 91
|
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''simple docstring'''
assert isinstance(__UpperCAmelCase, __UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''', [False, True] )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = SqlDatasetReader(
'''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase, keep_in_memory=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
'''features''', [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
], )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, features=__UpperCAmelCase, cache_dir=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(__UpperCAmelCase ) ) as con:
snake_case_ = con.cursor()
cur.execute('''SELECT * FROM dataset''' )
for row in cur:
yield row
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=1 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=2 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
with pytest.raises(__UpperCAmelCase ):
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=0 ).write()
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|
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = R"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
"""
class a__ ( snake_case__ ):
@add_start_docstrings(_A )
def __call__( self , _A , _A , **_A ):
"""simple docstring"""
raise NotImplementedError("StoppingCriteria needs to be subclassed" )
class a__ ( snake_case__ ):
def __init__( self , _A , _A = None ):
"""simple docstring"""
__lowerCAmelCase = max_length
__lowerCAmelCase = max_position_embeddings
@add_start_docstrings(_A )
def __call__( self , _A , _A , **_A ):
"""simple docstring"""
__lowerCAmelCase = input_ids.shape[-1]
__lowerCAmelCase = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
"This is a friendly reminder - the current text generation call will exceed the model's predefined "
f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
"exceptions, performance degradation, or nothing at all." )
return is_done
class a__ ( snake_case__ ):
def __init__( self , _A , _A ):
"""simple docstring"""
warnings.warn(
"The class `MaxNewTokensCriteria` is deprecated. "
f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
"with `max_length = start_length + max_new_tokens` instead." , _A , )
__lowerCAmelCase = start_length
__lowerCAmelCase = max_new_tokens
__lowerCAmelCase = start_length + max_new_tokens
@add_start_docstrings(_A )
def __call__( self , _A , _A , **_A ):
"""simple docstring"""
return input_ids.shape[-1] >= self.max_length
class a__ ( snake_case__ ):
def __init__( self , _A , _A = None ):
"""simple docstring"""
__lowerCAmelCase = max_time
__lowerCAmelCase = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(_A )
def __call__( self , _A , _A , **_A ):
"""simple docstring"""
return time.time() - self.initial_timestamp > self.max_time
class a__ ( snake_case__ ):
@add_start_docstrings(_A )
def __call__( self , _A , _A , **_A ):
"""simple docstring"""
return any(criteria(_A , _A ) for criteria in self )
@property
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for stopping_criterium in self:
if isinstance(_A , _A ):
return stopping_criterium.max_length
elif isinstance(_A , _A ):
return stopping_criterium.max_length
return None
def _a ( SCREAMING_SNAKE_CASE_ : StoppingCriteriaList , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = stopping_criteria.max_length
__lowerCAmelCase = deepcopy(SCREAMING_SNAKE_CASE_ )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter" , SCREAMING_SNAKE_CASE_ )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=SCREAMING_SNAKE_CASE_ ) )
return new_stopping_criteria
| 92
|
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCase )
if ret % 2 == 0:
cuts.append(__UpperCAmelCase )
return ret
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
a ,a : Dict = 10, 9
a : Dict = defaultdict(list)
a : dict[int, bool] = {}
a : list[int] = []
a : Tuple = 0
a : str = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 56
| 0
|
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_lowercase : Optional[int] = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''paws''', '''csv'''] )
def snake_case_ ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
inspect_dataset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : Dict = path + '''.py'''
assert script_name in os.listdir(__SCREAMING_SNAKE_CASE )
assert "__pycache__" not in os.listdir(__SCREAMING_SNAKE_CASE )
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning''' )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''' )
@pytest.mark.parametrize('''path''' , ['''accuracy'''] )
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
inspect_metric(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : str = path + '''.py'''
assert script_name in os.listdir(__SCREAMING_SNAKE_CASE )
assert "__pycache__" not in os.listdir(__SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''path, config_name, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
lowercase_ : Optional[int] = get_dataset_config_info(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
with pytest.raises(__SCREAMING_SNAKE_CASE ):
get_dataset_config_info(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize(
'''path, expected''' , [
('''squad''', '''plain_text'''),
('''acronym_identification''', '''default'''),
('''lhoestq/squad''', '''plain_text'''),
('''lhoestq/test''', '''default'''),
('''lhoestq/demo1''', '''lhoestq--demo1'''),
('''dalle-mini/wit''', '''dalle-mini--wit'''),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
lowercase_ : Dict = get_dataset_config_names(__SCREAMING_SNAKE_CASE )
assert expected in config_names
@pytest.mark.parametrize(
'''path, expected_configs, expected_splits_in_first_config''' , [
('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
lowercase_ : Union[str, Any] = get_dataset_infos(__SCREAMING_SNAKE_CASE )
assert list(infos.keys() ) == expected_configs
lowercase_ : List[Any] = expected_configs[0]
assert expected_config in infos
lowercase_ : Tuple = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
'''path, expected_config, expected_splits''' , [
('''squad''', '''plain_text''', ['''train''', '''validation''']),
('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
lowercase_ : Dict = get_dataset_infos(__SCREAMING_SNAKE_CASE )
assert expected_config in infos
lowercase_ : List[Any] = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
'''path, config_name, expected_exception''' , [
('''paws''', None, ValueError),
] , )
def snake_case_ ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
with pytest.raises(__SCREAMING_SNAKE_CASE ):
get_dataset_split_names(__SCREAMING_SNAKE_CASE , config_name=__SCREAMING_SNAKE_CASE )
| 93
|
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56
| 0
|
import numpy as np
snake_case : List[str] = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
class _snake_case :
def __init__( self ):
a :Dict = np.array(_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a , a :Optional[int] = np.where(letter == self.SQUARE )
a :List[str] = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase ):
a :Tuple = self.SQUARE[indexa - 1, indexa - 1]
return letter
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :List[str] = message.lower()
a :str = message.replace(''' ''' , '''''' )
a :Optional[Any] = message.replace('''j''' , '''i''' )
a :Optional[Any] = np.empty((2, len(_lowerCamelCase )) )
for letter_index in range(len(_lowerCamelCase ) ):
a :str = self.letter_to_numbers(message[letter_index] )
a :Union[str, Any] = numbers[0]
a :Dict = numbers[1]
a :Optional[Any] = first_step.reshape(2 * len(_lowerCamelCase ) )
a :List[Any] = ''''''
for numbers_index in range(len(_lowerCamelCase ) ):
a :Tuple = int(second_step[numbers_index * 2] )
a :List[Any] = int(second_step[(numbers_index * 2) + 1] )
a :Dict = self.numbers_to_letter(_lowerCamelCase , _lowerCamelCase )
a :Dict = encoded_message + letter
return encoded_message
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase ):
a :str = message.lower()
message.replace(''' ''' , '''''' )
a :Any = np.empty(2 * len(_lowerCamelCase ) )
for letter_index in range(len(_lowerCamelCase ) ):
a :Union[str, Any] = self.letter_to_numbers(message[letter_index] )
a :int = numbers[0]
a :Optional[Any] = numbers[1]
a :Optional[Any] = first_step.reshape((2, len(_lowerCamelCase )) )
a :Tuple = ''''''
for numbers_index in range(len(_lowerCamelCase ) ):
a :Union[str, Any] = int(second_step[0, numbers_index] )
a :Union[str, Any] = int(second_step[1, numbers_index] )
a :Tuple = self.numbers_to_letter(_lowerCamelCase , _lowerCamelCase )
a :Union[str, Any] = decoded_message + letter
return decoded_message
| 94
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Any = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = DPTConfig()
if "large" in checkpoint_url:
snake_case_ = 1024
snake_case_ = 4096
snake_case_ = 24
snake_case_ = 16
snake_case_ = [5, 11, 17, 23]
snake_case_ = [256, 512, 1024, 1024]
snake_case_ = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case_ = True
snake_case_ = 150
snake_case_ = '''huggingface/label-files'''
snake_case_ = '''ade20k-id2label.json'''
snake_case_ = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset''' ) ), '''r''' ) )
snake_case_ = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = [1, 150, 480, 480]
return config, expected_shape
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case_ = name.replace('''pretrained.model''', '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case_ = name.replace('''pretrained.model''', '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case_ = name.replace('''patch_embed''', '''patch_embeddings''' )
if "pos_embed" in name:
snake_case_ = name.replace('''pos_embed''', '''position_embeddings''' )
if "attn.proj" in name:
snake_case_ = name.replace('''attn.proj''', '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case_ = name.replace('''proj''', '''projection''' )
if "blocks" in name:
snake_case_ = name.replace('''blocks''', '''layer''' )
if "mlp.fc1" in name:
snake_case_ = name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case_ = name.replace('''mlp.fc2''', '''output.dense''' )
if "norm1" in name:
snake_case_ = name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
snake_case_ = name.replace('''norm2''', '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case_ = name.replace('''scratch.output_conv''', '''head''' )
if "scratch" in name:
snake_case_ = name.replace('''scratch''', '''neck''' )
if "layer1_rn" in name:
snake_case_ = name.replace('''layer1_rn''', '''convs.0''' )
if "layer2_rn" in name:
snake_case_ = name.replace('''layer2_rn''', '''convs.1''' )
if "layer3_rn" in name:
snake_case_ = name.replace('''layer3_rn''', '''convs.2''' )
if "layer4_rn" in name:
snake_case_ = name.replace('''layer4_rn''', '''convs.3''' )
if "refinenet" in name:
snake_case_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case_ = name.replace(F"refinenet{layer_idx}", F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
snake_case_ = name.replace('''out_conv''', '''projection''' )
if "resConfUnit1" in name:
snake_case_ = name.replace('''resConfUnit1''', '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case_ = name.replace('''resConfUnit2''', '''residual_layer2''' )
if "conv1" in name:
snake_case_ = name.replace('''conv1''', '''convolution1''' )
if "conv2" in name:
snake_case_ = name.replace('''conv2''', '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.0.project.0''', '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.0.project.0''', '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.0.project.0''', '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.0.project.0''', '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.3''', '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.4''', '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.3''', '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.4''', '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.3''', '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.3''', '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.4''', '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case_ = name.replace('''pretrained''', '''dpt''' )
if "bn" in name:
snake_case_ = name.replace('''bn''', '''batch_norm''' )
if "head" in name:
snake_case_ = name.replace('''head''', '''head.head''' )
if "encoder.norm" in name:
snake_case_ = name.replace('''encoder.norm''', '''layernorm''' )
if "auxlayer" in name:
snake_case_ = name.replace('''auxlayer''', '''auxiliary_head.head''' )
return name
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{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 __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ = Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dpt_config(__UpperCAmelCase )
# load original state_dict from URL
snake_case_ = torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
snake_case_ = state_dict.pop(__UpperCAmelCase )
snake_case_ = val
# read in qkv matrices
read_in_q_k_v(__UpperCAmelCase, __UpperCAmelCase )
# load HuggingFace model
snake_case_ = DPTForSemanticSegmentation(__UpperCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
# Check outputs on an image
snake_case_ = 480 if '''ade''' in checkpoint_url else 384
snake_case_ = DPTImageProcessor(size=__UpperCAmelCase )
snake_case_ = prepare_img()
snake_case_ = image_processor(__UpperCAmelCase, return_tensors='''pt''' )
# forward pass
snake_case_ = model(**__UpperCAmelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase ).predicted_depth
# Assert logits
snake_case_ = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
snake_case_ = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__UpperCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3], __UpperCAmelCase, atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3], __UpperCAmelCase )
)
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add model''', use_temp_dir=__UpperCAmelCase, )
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add image processor''', use_temp_dir=__UpperCAmelCase, )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
a : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 56
| 0
|
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCamelCase__):
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> None:
'''simple docstring'''
warnings.warn(
"The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use SegformerImageProcessor instead." , lowerCAmelCase__ , )
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
| 95
|
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
a : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 56
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
"""google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """mobilenet_v1"""
def __init__( self , lowercase=3 , lowercase=224 , lowercase=1.0 , lowercase=8 , lowercase="relu6" , lowercase=True , lowercase=0.9_99 , lowercase=0.02 , lowercase=0.0_01 , **lowercase , ):
super().__init__(**lowercase )
if depth_multiplier <= 0:
raise ValueError('depth_multiplier must be greater than zero.' )
_lowerCamelCase : Optional[int] = num_channels
_lowerCamelCase : Any = image_size
_lowerCamelCase : str = depth_multiplier
_lowerCamelCase : Dict = min_depth
_lowerCamelCase : List[str] = hidden_act
_lowerCamelCase : Union[str, Any] = tf_padding
_lowerCamelCase : str = classifier_dropout_prob
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : int = layer_norm_eps
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = version.parse("""1.11""" )
@property
def A_ ( self ):
return OrderedDict([('pixel_values', {0: 'batch'})] )
@property
def A_ ( self ):
if self.task == "image-classification":
return OrderedDict([('logits', {0: 'batch'})] )
else:
return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})] )
@property
def A_ ( self ):
return 1E-4
| 96
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
re.sub('''<n>''', '''''', __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 56
| 0
|
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a ( __a ) -> list[list[float]]:
'''simple docstring'''
UpperCamelCase__ :List[str] = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(__a ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
UpperCamelCase__ :Optional[int] = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creates a copy of the matrix with swapped positions of the elements
UpperCamelCase__ :List[Any] = [[0.0, 0.0], [0.0, 0.0]]
UpperCamelCase__ , UpperCamelCase__ :int = matrix[1][1], matrix[0][0]
UpperCamelCase__ , UpperCamelCase__ :Union[str, Any] = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(__a ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(__a ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
UpperCamelCase__ :Tuple = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creating cofactor matrix
UpperCamelCase__ :Any = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
UpperCamelCase__ :int = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
UpperCamelCase__ :Union[str, Any] = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
UpperCamelCase__ :Tuple = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
UpperCamelCase__ :Any = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
UpperCamelCase__ :Dict = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
UpperCamelCase__ :Tuple = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
UpperCamelCase__ :List[Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
UpperCamelCase__ :str = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
UpperCamelCase__ :Tuple = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
UpperCamelCase__ :Optional[int] = array(__a )
for i in range(3 ):
for j in range(3 ):
UpperCamelCase__ :Optional[int] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
UpperCamelCase__ :str = array(__a )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(__a )
# Calculate the inverse of the matrix
return [[float(d(__a ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''' )
| 97
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
a : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 56
| 0
|
"""simple docstring"""
from typing import Dict, Iterable, Optional, 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, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
return [
int(1_0_0_0 * (box[0] / width) ),
int(1_0_0_0 * (box[1] / height) ),
int(1_0_0_0 * (box[2] / width) ),
int(1_0_0_0 * (box[3] / height) ),
]
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = to_pil_image(lowerCamelCase )
UpperCAmelCase__ , UpperCAmelCase__ = pil_image.size
UpperCAmelCase__ = pytesseract.image_to_data(lowerCamelCase , lang=lowerCamelCase , output_type='dict' , config=lowerCamelCase )
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = data['text'], data['left'], data['top'], data['width'], data['height']
# filter empty words and corresponding coordinates
UpperCAmelCase__ = [idx for idx, word in enumerate(lowerCamelCase ) if not word.strip()]
UpperCAmelCase__ = [word for idx, word in enumerate(lowerCamelCase ) if idx not in irrelevant_indices]
UpperCAmelCase__ = [coord for idx, coord in enumerate(lowerCamelCase ) if idx not in irrelevant_indices]
UpperCAmelCase__ = [coord for idx, coord in enumerate(lowerCamelCase ) if idx not in irrelevant_indices]
UpperCAmelCase__ = [coord for idx, coord in enumerate(lowerCamelCase ) if idx not in irrelevant_indices]
UpperCAmelCase__ = [coord for idx, coord in enumerate(lowerCamelCase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
UpperCAmelCase__ = []
for x, y, w, h in zip(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = [x, y, x + w, y + h]
actual_boxes.append(lowerCamelCase )
# finally, normalize the bounding boxes
UpperCAmelCase__ = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(lowerCamelCase , lowerCamelCase , lowerCamelCase ) )
assert len(lowerCamelCase ) == len(lowerCamelCase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class snake_case ( __UpperCAmelCase ):
"""simple docstring"""
snake_case__ = ["pixel_values"]
def __init__( self : int ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : PILImageResampling = PILImageResampling.BILINEAR ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1 / 255 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Union[float, Iterable[float]] = None ,lowerCamelCase__ : Union[float, Iterable[float]] = None ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Optional[str] = None ,lowerCamelCase__ : Optional[str] = "" ,**lowerCamelCase__ : Any ,):
super().__init__(**lowerCamelCase__ )
UpperCAmelCase__ = size if size is not None else {'height': 224, 'width': 224}
UpperCAmelCase__ = get_size_dict(lowerCamelCase__ )
UpperCAmelCase__ = do_resize
UpperCAmelCase__ = size
UpperCAmelCase__ = resample
UpperCAmelCase__ = do_rescale
UpperCAmelCase__ = rescale_value
UpperCAmelCase__ = do_normalize
UpperCAmelCase__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
UpperCAmelCase__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
UpperCAmelCase__ = apply_ocr
UpperCAmelCase__ = ocr_lang
UpperCAmelCase__ = tesseract_config
def __lowerCAmelCase ( self : List[str] ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Dict[str, int] ,lowerCamelCase__ : PILImageResampling = PILImageResampling.BILINEAR ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : str ,):
UpperCAmelCase__ = 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()}''' )
UpperCAmelCase__ = (size['height'], size['width'])
return resize(lowerCamelCase__ ,size=lowerCamelCase__ ,resample=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def __lowerCAmelCase ( self : str ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Union[int, float] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Dict ,):
return rescale(lowerCamelCase__ ,scale=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def __lowerCAmelCase ( self : Dict ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Union[float, Iterable[float]] ,lowerCamelCase__ : Union[float, Iterable[float]] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Optional[int] ,):
return normalize(lowerCamelCase__ ,mean=lowerCamelCase__ ,std=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def __lowerCAmelCase ( self : List[Any] ,lowerCamelCase__ : ImageInput ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : Union[str, Any]=None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : float = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Union[float, Iterable[float]] = None ,lowerCamelCase__ : Union[float, Iterable[float]] = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Optional[str] = None ,lowerCamelCase__ : Optional[str] = None ,lowerCamelCase__ : Optional[Union[str, TensorType]] = None ,lowerCamelCase__ : ChannelDimension = ChannelDimension.FIRST ,**lowerCamelCase__ : str ,):
UpperCAmelCase__ = do_resize if do_resize is not None else self.do_resize
UpperCAmelCase__ = size if size is not None else self.size
UpperCAmelCase__ = get_size_dict(lowerCamelCase__ )
UpperCAmelCase__ = resample if resample is not None else self.resample
UpperCAmelCase__ = do_rescale if do_rescale is not None else self.do_rescale
UpperCAmelCase__ = rescale_factor if rescale_factor is not None else self.rescale_factor
UpperCAmelCase__ = do_normalize if do_normalize is not None else self.do_normalize
UpperCAmelCase__ = image_mean if image_mean is not None else self.image_mean
UpperCAmelCase__ = image_std if image_std is not None else self.image_std
UpperCAmelCase__ = apply_ocr if apply_ocr is not None else self.apply_ocr
UpperCAmelCase__ = ocr_lang if ocr_lang is not None else self.ocr_lang
UpperCAmelCase__ = tesseract_config if tesseract_config is not None else self.tesseract_config
UpperCAmelCase__ = 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:
raise ValueError('Size 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('If do_normalize is True, image_mean and image_std must be specified.' )
# All transformations expect numpy arrays.
UpperCAmelCase__ = [to_numpy_array(lowerCamelCase__ ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self ,'pytesseract' )
UpperCAmelCase__ = []
UpperCAmelCase__ = []
for image in images:
UpperCAmelCase__ , UpperCAmelCase__ = apply_tesseract(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
words_batch.append(lowerCamelCase__ )
boxes_batch.append(lowerCamelCase__ )
if do_resize:
UpperCAmelCase__ = [self.resize(image=lowerCamelCase__ ,size=lowerCamelCase__ ,resample=lowerCamelCase__ ) for image in images]
if do_rescale:
UpperCAmelCase__ = [self.rescale(image=lowerCamelCase__ ,scale=lowerCamelCase__ ) for image in images]
if do_normalize:
UpperCAmelCase__ = [self.normalize(image=lowerCamelCase__ ,mean=lowerCamelCase__ ,std=lowerCamelCase__ ) for image in images]
UpperCAmelCase__ = [to_channel_dimension_format(lowerCamelCase__ ,lowerCamelCase__ ) for image in images]
UpperCAmelCase__ = BatchFeature(data={'pixel_values': images} ,tensor_type=lowerCamelCase__ )
if apply_ocr:
UpperCAmelCase__ = words_batch
UpperCAmelCase__ = boxes_batch
return data
| 98
|
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Optional[Any] , lowercase_ : float , lowercase_ : Callable , lowercase_ : int , lowercase_ : float = 1.0 , lowercase_ : str = None , ):
super().__init__()
snake_case_ = initial_learning_rate
snake_case_ = warmup_steps
snake_case_ = power
snake_case_ = decay_schedule_fn
snake_case_ = name
def __call__( self : Tuple , lowercase_ : str ):
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
snake_case_ = tf.cast(lowercase_ , tf.floataa )
snake_case_ = tf.cast(self.warmup_steps , tf.floataa )
snake_case_ = global_step_float / warmup_steps_float
snake_case_ = self.initial_learning_rate * tf.math.pow(lowercase_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=lowercase_ , )
def A_ ( self : Any ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 0.9, __UpperCAmelCase = 0.9_9_9, __UpperCAmelCase = 1e-8, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0, __UpperCAmelCase = None, ) -> List[str]:
'''simple docstring'''
snake_case_ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=__UpperCAmelCase, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=init_lr * min_lr_ratio, power=__UpperCAmelCase, )
if num_warmup_steps:
snake_case_ = WarmUp(
initial_learning_rate=__UpperCAmelCase, decay_schedule_fn=__UpperCAmelCase, warmup_steps=__UpperCAmelCase, )
if weight_decay_rate > 0.0:
snake_case_ = AdamWeightDecay(
learning_rate=__UpperCAmelCase, weight_decay_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''], include_in_weight_decay=__UpperCAmelCase, )
else:
snake_case_ = tf.keras.optimizers.Adam(
learning_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class a ( _lowerCamelCase ):
def __init__( self : Dict , lowercase_ : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , lowercase_ : float = 0.9 , lowercase_ : float = 0.999 , lowercase_ : float = 1e-7 , lowercase_ : bool = False , lowercase_ : float = 0.0 , lowercase_ : Optional[List[str]] = None , lowercase_ : Optional[List[str]] = None , lowercase_ : str = "AdamWeightDecay" , **lowercase_ : Optional[int] , ):
super().__init__(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
snake_case_ = weight_decay_rate
snake_case_ = include_in_weight_decay
snake_case_ = exclude_from_weight_decay
@classmethod
def A_ ( cls : Dict , lowercase_ : Union[str, Any] ):
snake_case_ = {'''WarmUp''': WarmUp}
return super(lowercase_ , cls ).from_config(lowercase_ , custom_objects=lowercase_ )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Optional[int] ):
super(lowercase_ , self )._prepare_local(lowercase_ , lowercase_ , lowercase_ )
snake_case_ = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def A_ ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any ):
snake_case_ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : str=None , **lowercase_ : List[str] ):
snake_case_ ,snake_case_ = list(zip(*lowercase_ ) )
return super(lowercase_ , self ).apply_gradients(zip(lowercase_ , lowercase_ ) , name=lowercase_ , **lowercase_ )
def A_ ( self : List[Any] , lowercase_ : str , lowercase_ : str , lowercase_ : Any ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
snake_case_ = apply_state or {}
snake_case_ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
snake_case_ = self._fallback_apply_state(lowercase_ , lowercase_ )
snake_case_ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : Optional[int]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_dense(lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : str , lowercase_ : List[Any]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_sparse(lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def A_ ( self : Optional[int] , lowercase_ : int ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return False
return True
class a ( _lowerCamelCase ):
def __init__( self : List[Any] ):
snake_case_ = []
snake_case_ = None
@property
def A_ ( self : Union[str, Any] ):
if self._accum_steps is None:
snake_case_ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def A_ ( self : Dict ):
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Any , lowercase_ : int ):
if not self._gradients:
snake_case_ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(lowercase_ ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(lowercase_ ) != len(self._gradients ):
raise ValueError(F"Expected {len(self._gradients )} gradients, but got {len(lowercase_ )}" )
for accum_gradient, gradient in zip(self._gradients , lowercase_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(lowercase_ )
self._accum_steps.assign_add(1 )
def A_ ( self : Optional[int] ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(lowercase_ ) )
| 56
| 0
|
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( A__ ) -> Tuple:
# A local function to see if a dot lands in the circle.
def is_in_circle(A__ , A__ ) -> bool:
a__ : List[str] = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
a__ : List[str] = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(A__ ) )
# The ratio of the area for circle to square is pi/4.
a__ : Optional[Any] = proportion * 4
print(F'The estimated value of pi is {pi_estimate}' )
print(F'The numpy value of pi is {pi}' )
print(F'The total error is {abs(pi - pi_estimate )}' )
def A_ ( A__ , A__ , A__ = 0.0 , A__ = 1.0 , ) -> float:
return mean(
function_to_integrate(uniform(A__ , A__ ) ) for _ in range(A__ ) ) * (max_value - min_value)
def A_ ( A__ , A__ = 0.0 , A__ = 1.0 ) -> None:
def identity_function(A__ ) -> float:
return x
a__ : List[Any] = area_under_curve_estimator(
A__ , A__ , A__ , A__ )
a__ : Union[str, Any] = (max_value * max_value - min_value * min_value) / 2
print('******************' )
print(F'Estimating area under y=x where x varies from {min_value} to {max_value}' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {expected_value}' )
print(F'Total error is {abs(estimated_value - expected_value )}' )
print('******************' )
def A_ ( A__ ) -> None:
def function_to_integrate(A__ ) -> float:
return sqrt(4.0 - x * x )
a__ : Dict = area_under_curve_estimator(
A__ , A__ , 0.0 , 2.0 )
print('******************' )
print('Estimating pi using area_under_curve_estimator' )
print(F'Estimated value is {estimated_value}' )
print(F'Expected value is {pi}' )
print(F'Total error is {abs(estimated_value - pi )}' )
print('******************' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 99
|
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = "sample"
snake_case_ = 1e-2
@property
def A_ ( self : Dict ):
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
return {"sample": image}
@property
def A_ ( self : List[Any] ):
return (3, 32, 32)
@property
def A_ ( self : Dict ):
return (3, 32, 32)
def A_ ( self : Union[str, Any] ):
snake_case_ = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def A_ ( self : Any ):
pass
def A_ ( self : str ):
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def A_ ( self : Dict ):
# enable deterministic behavior for gradient checkpointing
snake_case_ ,snake_case_ = self.prepare_init_args_and_inputs_for_common()
snake_case_ = self.model_class(**lowercase_ )
model.to(lowercase_ )
assert not model.is_gradient_checkpointing and model.training
snake_case_ = model(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
snake_case_ = torch.randn_like(lowercase_ )
snake_case_ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
snake_case_ = self.model_class(**lowercase_ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase_ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
snake_case_ = model_a(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
snake_case_ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
snake_case_ = dict(model.named_parameters() )
snake_case_ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def A_ ( self : Tuple ):
snake_case_ ,snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(lowercase_ )
snake_case_ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def A_ ( self : Tuple ):
snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
snake_case_ = model.to(lowercase_ )
model.eval()
if torch_device == "mps":
snake_case_ = torch.manual_seed(0 )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(0 )
snake_case_ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case_ = image.to(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , sample_posterior=lowercase_ , generator=lowercase_ ).sample
snake_case_ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
snake_case_ = torch.tensor(
[
-4.0_078e-01,
-3.8_323e-04,
-1.2_681e-01,
-1.1_462e-01,
2.0_095e-01,
1.0_893e-01,
-8.8_247e-02,
-3.0_361e-01,
-9.8_644e-03,
] )
elif torch_device == "cpu":
snake_case_ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
snake_case_ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1e-2 ) )
@slow
class a ( unittest.TestCase ):
def A_ ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] ):
return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_ ) for s in shape] )}.npy"
def A_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Dict , lowercase_ : List[Any]=0 , lowercase_ : Union[str, Any]=(4, 3, 512, 512) , lowercase_ : Optional[Any]=False ):
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_ , lowercase_ ) ) ).to(lowercase_ ).to(lowercase_ )
return image
def A_ ( self : Any , lowercase_ : Dict="CompVis/stable-diffusion-v1-4" , lowercase_ : List[str]=False ):
snake_case_ = '''fp16''' if fpaa else None
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = AutoencoderKL.from_pretrained(
lowercase_ , subfolder='''vae''' , torch_dtype=lowercase_ , revision=lowercase_ , )
model.to(lowercase_ ).eval()
return model
def A_ ( self : Any , lowercase_ : int=0 ):
if torch_device == "mps":
return torch.manual_seed(lowercase_ )
return torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , fpaa=lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[Any] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : List[str] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model.encode(lowercase_ ).latent_dist
snake_case_ = dist.sample(generator=lowercase_ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
snake_case_ = sample[0, -1, -3:, -3:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
snake_case_ = 3e-3 if torch_device != '''mps''' else 1e-2
assert torch_all_close(lowercase_ , lowercase_ , atol=lowercase_ )
| 56
| 0
|
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ = 2 , UpperCamelCase_ = 1 , UpperCamelCase_ = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
return (pow(UpperCamelCase_ , 2 ) + step) % modulus
for _ in range(UpperCamelCase_ ):
# These track the position within the cycle detection logic.
__SCREAMING_SNAKE_CASE = seed
__SCREAMING_SNAKE_CASE = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
__SCREAMING_SNAKE_CASE = rand_fn(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = rand_fn(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = rand_fn(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
__SCREAMING_SNAKE_CASE = gcd(hare - tortoise , UpperCamelCase_ )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
__SCREAMING_SNAKE_CASE = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
__magic_name__ = argparse.ArgumentParser()
parser.add_argument(
"num",
type=int,
help="The value to find a divisor of",
)
parser.add_argument(
"--attempts",
type=int,
default=3,
help="The number of attempts before giving up",
)
__magic_name__ = parser.parse_args()
__magic_name__ = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F"""{args.num} is probably prime""")
else:
__magic_name__ = args.num // divisor
print(F"""{args.num} = {divisor} * {quotient}""")
| 100
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class a ( _lowerCamelCase ):
snake_case_ = 42
@flax_register_to_config
class a ( nn.Module , _lowerCamelCase , _lowerCamelCase ):
snake_case_ = 32
snake_case_ = 4
snake_case_ = 4
snake_case_ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
snake_case_ = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
snake_case_ = False
snake_case_ = (320, 640, 1_280, 1_280)
snake_case_ = 2
snake_case_ = 8
snake_case_ = None
snake_case_ = 1_280
snake_case_ = 0.0
snake_case_ = False
snake_case_ = jnp.floataa
snake_case_ = True
snake_case_ = 0
snake_case_ = False
def A_ ( self : Optional[int] , lowercase_ : jax.random.KeyArray ):
# init input tensors
snake_case_ = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case_ = jnp.zeros(lowercase_ , dtype=jnp.floataa )
snake_case_ = jnp.ones((1,) , dtype=jnp.intaa )
snake_case_ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case_ ,snake_case_ = jax.random.split(lowercase_ )
snake_case_ = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(lowercase_ , lowercase_ , lowercase_ , lowercase_ )["params"]
def A_ ( self : List[str] ):
snake_case_ = self.block_out_channels
snake_case_ = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
snake_case_ = self.num_attention_heads or self.attention_head_dim
# input
snake_case_ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case_ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case_ = FlaxTimestepEmbedding(lowercase_ , dtype=self.dtype )
snake_case_ = self.only_cross_attention
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case_ = []
snake_case_ = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
snake_case_ = output_channel
snake_case_ = block_out_channels[i]
snake_case_ = i == len(lowercase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case_ = FlaxCrossAttnDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowercase_ )
snake_case_ = down_blocks
# mid
snake_case_ = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
snake_case_ = []
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
snake_case_ = output_channel
snake_case_ = reversed_block_out_channels[i]
snake_case_ = reversed_block_out_channels[min(i + 1 , len(lowercase_ ) - 1 )]
snake_case_ = i == len(lowercase_ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
snake_case_ = FlaxCrossAttnUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(lowercase_ )
snake_case_ = output_channel
snake_case_ = up_blocks
# out
snake_case_ = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
snake_case_ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int , lowercase_ : Any , lowercase_ : int=None , lowercase_ : Any=None , lowercase_ : bool = True , lowercase_ : bool = False , ):
# 1. time
if not isinstance(lowercase_ , jnp.ndarray ):
snake_case_ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowercase_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case_ = timesteps.astype(dtype=jnp.floataa )
snake_case_ = jnp.expand_dims(lowercase_ , 0 )
snake_case_ = self.time_proj(lowercase_ )
snake_case_ = self.time_embedding(lowercase_ )
# 2. pre-process
snake_case_ = jnp.transpose(lowercase_ , (0, 2, 3, 1) )
snake_case_ = self.conv_in(lowercase_ )
# 3. down
snake_case_ = (sample,)
for down_block in self.down_blocks:
if isinstance(lowercase_ , lowercase_ ):
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
else:
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
snake_case_ = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowercase_ , lowercase_ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
snake_case_ = new_down_block_res_samples
# 4. mid
snake_case_ = self.mid_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
snake_case_ = down_block_res_samples[-(self.layers_per_block + 1) :]
snake_case_ = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = up_block(
lowercase_ , temb=lowercase_ , encoder_hidden_states=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train , )
else:
snake_case_ = up_block(lowercase_ , temb=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train )
# 6. post-process
snake_case_ = self.conv_norm_out(lowercase_ )
snake_case_ = nn.silu(lowercase_ )
snake_case_ = self.conv_out(lowercase_ )
snake_case_ = jnp.transpose(lowercase_ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowercase_ )
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|
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( lowerCAmelCase__ = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
lowercase = BeautifulSoup(requests.get(lowerCAmelCase__ ).text , '''html.parser''' )
lowercase = soup.findAll('''h1''' )
lowercase = soup.findAll('''div''' , {'''class''': '''maincounter-number'''} )
keys += soup.findAll('''span''' , {'''class''': '''panel-title'''} )
values += soup.findAll('''div''' , {'''class''': '''number-table-main'''} )
return {key.text.strip(): value.text.strip() for key, value in zip(lowerCAmelCase__ , lowerCAmelCase__ )}
if __name__ == "__main__":
print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
for key, value in world_covidaa_stats().items():
print(F'{key}\n{value}\n')
| 101
|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a : Dict = (720, 1280) # Height, Width
a : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a : Dict = 1 / 100
a : str = ''
a : Any = ''
a : Optional[int] = ''
a : List[str] = 250
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dataset(__UpperCAmelCase, __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
snake_case_ = random.sample(range(len(__UpperCAmelCase ) ), 4 )
snake_case_ ,snake_case_ ,snake_case_ = update_image_and_anno(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, filter_scale=__UpperCAmelCase, )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ = random_chars(32 )
snake_case_ = path.split(os.sep )[-1].rsplit('''.''', 1 )[0]
snake_case_ = F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(F"{file_root}.jpg", __UpperCAmelCase, [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
snake_case_ = []
for anno in new_annos:
snake_case_ = anno[3] - anno[1]
snake_case_ = anno[4] - anno[2]
snake_case_ = anno[1] + width / 2
snake_case_ = anno[2] + height / 2
snake_case_ = F"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(F"{file_root}.txt", '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> tuple[list, list]:
'''simple docstring'''
snake_case_ = []
snake_case_ = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase, '''*.txt''' ) ):
snake_case_ = label_file.split(os.sep )[-1].rsplit('''.''', 1 )[0]
with open(__UpperCAmelCase ) as in_file:
snake_case_ = in_file.readlines()
snake_case_ = os.path.join(__UpperCAmelCase, F"{label_name}.jpg" )
snake_case_ = []
for obj_list in obj_lists:
snake_case_ = obj_list.rstrip('''\n''' ).split(''' ''' )
snake_case_ = float(obj[1] ) - float(obj[3] ) / 2
snake_case_ = float(obj[2] ) - float(obj[4] ) / 2
snake_case_ = float(obj[1] ) + float(obj[3] ) / 2
snake_case_ = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, ) -> tuple[list, list, str]:
'''simple docstring'''
snake_case_ = np.zeros([output_size[0], output_size[1], 3], dtype=np.uinta )
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = int(scale_x * output_size[1] )
snake_case_ = int(scale_y * output_size[0] )
snake_case_ = []
snake_case_ = []
for i, index in enumerate(__UpperCAmelCase ):
snake_case_ = all_img_list[index]
path_list.append(__UpperCAmelCase )
snake_case_ = all_annos[index]
snake_case_ = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = bbox[2] * scale_y
snake_case_ = bbox[3] * scale_x
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case_ = cva.resize(__UpperCAmelCase, (output_size[1] - divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = bbox[2] * scale_y
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = bbox[3] * scale_x
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case_ = cva.resize(
__UpperCAmelCase, (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case_ = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case_ = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 56
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|
"""simple docstring"""
def lowercase ( _snake_case : str ) ->str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class a :
@staticmethod
def A_ ( *lowercase_ : int , **lowercase_ : str ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class a ( unittest.TestCase ):
snake_case_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def A_ ( self : Any , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str] ):
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , image_processor=lowercase_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def A_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : int ):
snake_case_ = object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' , threshold=0.0 )
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = [
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
]
snake_case_ = object_detector(lowercase_ , threshold=0.0 )
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for outputs in batch_outputs:
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
@require_tf
@unittest.skip('''Object detection not implemented in TF''' )
def A_ ( self : int ):
pass
@require_torch
def A_ ( self : Tuple ):
snake_case_ = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
] , )
@require_torch
@slow
def A_ ( self : Optional[int] ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : Tuple ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : str ):
snake_case_ = 0.9985
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=lowercase_ )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def A_ ( self : Dict ):
snake_case_ = '''Narsil/layoutlmv3-finetuned-funsd'''
snake_case_ = 0.9993
snake_case_ = pipeline('''object-detection''' , model=lowercase_ , threshold=lowercase_ )
snake_case_ = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
] , )
| 56
| 0
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__ : Tuple = logging.get_logger(__name__)
A__ : List[str] = {
'''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class __snake_case ( UpperCamelCase_ ):
_a = '''blip_2_vision_model'''
def __init__( self : Tuple , A_ : Optional[int]=1_4_0_8 , A_ : Tuple=6_1_4_4 , A_ : Tuple=3_9 , A_ : Dict=1_6 , A_ : Tuple=2_2_4 , A_ : Any=1_4 , A_ : Dict="gelu" , A_ : Any=0.0_0001 , A_ : int=0.0 , A_ : Dict=1e-10 , A_ : Optional[Any]=True , **A_ : Tuple , ):
super().__init__(**A_)
lowerCAmelCase_ : Tuple = hidden_size
lowerCAmelCase_ : Dict = intermediate_size
lowerCAmelCase_ : List[str] = num_hidden_layers
lowerCAmelCase_ : Dict = num_attention_heads
lowerCAmelCase_ : Dict = patch_size
lowerCAmelCase_ : Dict = image_size
lowerCAmelCase_ : str = initializer_range
lowerCAmelCase_ : List[str] = attention_dropout
lowerCAmelCase_ : Optional[int] = layer_norm_eps
lowerCAmelCase_ : Union[str, Any] = hidden_act
lowerCAmelCase_ : Tuple = qkv_bias
@classmethod
def UpperCAmelCase__ ( cls : str , A_ : Union[str, os.PathLike] , **A_ : List[str]):
cls._set_token_in_kwargs(A_)
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = cls.get_config_dict(A_ , **A_)
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('''model_type''') == "blip-2":
lowerCAmelCase_ : Optional[Any] = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''') and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")
return cls.from_dict(A_ , **A_)
class __snake_case ( UpperCamelCase_ ):
_a = '''blip_2_qformer'''
def __init__( self : List[str] , A_ : List[str]=3_0_5_2_2 , A_ : Union[str, Any]=7_6_8 , A_ : Optional[int]=1_2 , A_ : Optional[Any]=1_2 , A_ : str=3_0_7_2 , A_ : Tuple="gelu" , A_ : Union[str, Any]=0.1 , A_ : Optional[Any]=0.1 , A_ : Any=5_1_2 , A_ : Optional[Any]=0.02 , A_ : List[Any]=1e-12 , A_ : Optional[int]=0 , A_ : Dict="absolute" , A_ : List[Any]=2 , A_ : Union[str, Any]=1_4_0_8 , **A_ : Optional[int] , ):
super().__init__(pad_token_id=A_ , **A_)
lowerCAmelCase_ : Dict = vocab_size
lowerCAmelCase_ : str = hidden_size
lowerCAmelCase_ : Union[str, Any] = num_hidden_layers
lowerCAmelCase_ : Tuple = num_attention_heads
lowerCAmelCase_ : List[str] = hidden_act
lowerCAmelCase_ : Optional[int] = intermediate_size
lowerCAmelCase_ : Any = hidden_dropout_prob
lowerCAmelCase_ : int = attention_probs_dropout_prob
lowerCAmelCase_ : str = max_position_embeddings
lowerCAmelCase_ : Union[str, Any] = initializer_range
lowerCAmelCase_ : Union[str, Any] = layer_norm_eps
lowerCAmelCase_ : List[Any] = position_embedding_type
lowerCAmelCase_ : Optional[Any] = cross_attention_frequency
lowerCAmelCase_ : Dict = encoder_hidden_size
@classmethod
def UpperCAmelCase__ ( cls : Dict , A_ : Union[str, os.PathLike] , **A_ : Tuple):
cls._set_token_in_kwargs(A_)
lowerCAmelCase_ , lowerCAmelCase_ : str = cls.get_config_dict(A_ , **A_)
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('''model_type''') == "blip-2":
lowerCAmelCase_ : List[Any] = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''') and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")
return cls.from_dict(A_ , **A_)
class __snake_case ( UpperCamelCase_ ):
_a = '''blip-2'''
_a = True
def __init__( self : str , A_ : str=None , A_ : Dict=None , A_ : List[str]=None , A_ : str=3_2 , **A_ : Any):
super().__init__(**A_)
if vision_config is None:
lowerCAmelCase_ : Any = {}
logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''')
if qformer_config is None:
lowerCAmelCase_ : List[str] = {}
logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''')
if text_config is None:
lowerCAmelCase_ : Any = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''')
lowerCAmelCase_ : Union[str, Any] = BlipaVisionConfig(**A_)
lowerCAmelCase_ : int = BlipaQFormerConfig(**A_)
lowerCAmelCase_ : Any = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
lowerCAmelCase_ : Dict = CONFIG_MAPPING[text_model_type](**A_)
lowerCAmelCase_ : List[str] = self.text_config.tie_word_embeddings
lowerCAmelCase_ : Optional[Any] = self.text_config.is_encoder_decoder
lowerCAmelCase_ : Tuple = num_query_tokens
lowerCAmelCase_ : Tuple = self.vision_config.hidden_size
lowerCAmelCase_ : str = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
lowerCAmelCase_ : List[Any] = 1.0
lowerCAmelCase_ : List[Any] = 0.02
@classmethod
def UpperCAmelCase__ ( cls : Dict , A_ : BlipaVisionConfig , A_ : BlipaQFormerConfig , A_ : PretrainedConfig , **A_ : List[Any] , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **A_ , )
def UpperCAmelCase__ ( self : Tuple):
lowerCAmelCase_ : List[Any] = copy.deepcopy(self.__dict__)
lowerCAmelCase_ : Union[str, Any] = self.vision_config.to_dict()
lowerCAmelCase_ : List[Any] = self.qformer_config.to_dict()
lowerCAmelCase_ : Any = self.text_config.to_dict()
lowerCAmelCase_ : Any = self.__class__.model_type
return output
| 103
|
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class a :
def __init__( self : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any=13 , lowercase_ : Optional[Any]=7 , lowercase_ : Optional[Any]=True , lowercase_ : Dict=True , lowercase_ : Tuple=False , lowercase_ : Optional[Any]=True , lowercase_ : Any=99 , lowercase_ : Union[str, Any]=64 , lowercase_ : str=5 , lowercase_ : int=4 , lowercase_ : List[Any]=64 , lowercase_ : Dict="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : List[Any]=16 , lowercase_ : str=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
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_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def A_ ( self : List[str] ):
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def A_ ( self : str ):
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_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Tuple ):
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def A_ ( self : Any , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int] ):
snake_case_ = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 : Tuple , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.num_labels
snake_case_ = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Any , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.num_choices
snake_case_ = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : int , lowercase_ : List[str] ):
snake_case_ = self.num_labels
snake_case_ = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Union[str, Any] ):
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_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = True
def A_ ( self : Tuple ):
snake_case_ = MPNetModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def A_ ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ):
snake_case_ = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
snake_case_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
snake_case_ = model(lowercase_ )[0]
snake_case_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowercase_ )
snake_case_ = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1e-4 ) )
| 56
| 0
|
'''simple docstring'''
def _A ( A__ , A__ ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowercase = str(bin(A__ ) )[2:] # remove the leading "0b"
__lowercase = str(bin(A__ ) )[2:]
__lowercase = max(len(A__ ) , len(A__ ) )
return "0b" + "".join(
str(int('''1''' in (char_a, char_b) ) )
for char_a, char_b in zip(a_binary.zfill(A__ ) , b_binary.zfill(A__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 104
|
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class a ( _lowerCamelCase ):
def A_ ( self : str ):
snake_case_ = tempfile.mkdtemp()
snake_case_ = 8
# DPR tok
snake_case_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
snake_case_ = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
snake_case_ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
snake_case_ = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
snake_case_ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case_ = {'''unk_token''': '''<unk>'''}
snake_case_ = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def A_ ( self : Union[str, Any] ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : Union[str, Any] ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : int ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def A_ ( self : str ):
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def A_ ( self : str ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def A_ ( self : str , lowercase_ : bool ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
snake_case_ = os.path.join(self.tmpdirname , '''dataset''' )
snake_case_ = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , lowercase_ ) , )
return retriever
def A_ ( self : Tuple ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
snake_case_ = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
snake_case_ = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
snake_case_ = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(lowercase_ , open(lowercase_ , '''wb''' ) )
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def A_ ( self : Optional[Any] ):
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : str ):
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = self.get_dummy_dataset()
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : int ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : str ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : Any ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : Any ):
snake_case_ = 1
snake_case_ = self.get_dummy_legacy_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : List[str] ):
import torch
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
snake_case_ ,snake_case_ ,snake_case_ = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , np.ndarray )
snake_case_ = retriever(
lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ , return_tensors='''pt''' , )
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : Tuple ):
snake_case_ = self.get_dpr_ctx_encoder_tokenizer()
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
retriever.set_ctx_encoder_tokenizer(lowercase_ )
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
self.assertEqual(
len(lowercase_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , lowercase_ ) # check for doc token related keys in dictionary.
| 56
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|
"""simple docstring"""
from collections import defaultdict
class __UpperCamelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
a : Optional[int] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to -1
a : Dict = [
[-1 for i in range(total + 1 )] for j in range(2 ** len(lowerCAmelCase__ ) )
]
a : int = defaultdict(lowerCAmelCase__ ) # stores the list of persons for each task
# final_mask is used to check if all persons are included by setting all bits
# to 1
a : List[str] = (1 << len(lowerCAmelCase__ )) - 1
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
# if mask == self.finalmask all persons are distributed tasks, return 1
if mask == self.final_mask:
return 1
# if not everyone gets the task and no more tasks are available, return 0
if task_no > self.total_tasks:
return 0
# if case already considered
if self.dp[mask][task_no] != -1:
return self.dp[mask][task_no]
# Number of ways when we don't this task in the arrangement
a : str = self.count_ways_until(lowerCAmelCase__ , task_no + 1 )
# now assign the tasks one by one to all possible persons and recursively
# assign for the remaining tasks.
if task_no in self.task:
for p in self.task[task_no]:
# if p is already given a task
if mask & (1 << p):
continue
# assign this task to p and change the mask value. And recursively
# assign tasks with the new mask value.
total_ways_util += self.count_ways_until(mask | (1 << p) , task_no + 1 )
# save the value.
a : List[str] = total_ways_util
return self.dp[mask][task_no]
def __a ( self , lowerCAmelCase__ ) -> Optional[Any]:
# Store the list of persons for each task
for i in range(len(lowerCAmelCase__ ) ):
for j in task_performed[i]:
self.task[j].append(lowerCAmelCase__ )
# call the function to fill the DP table, final answer is stored in dp[0][1]
return self.count_ways_until(0 , 1 )
if __name__ == "__main__":
a : List[Any] = 5 # total no of tasks (the value of N)
# the list of tasks that can be done by M persons.
a : List[Any] = [[1, 3, 4], [1, 2, 5], [3, 4]]
print(
AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
task_performed
)
)
| 105
|
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
a : Dict = None
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
a : str = {
'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',
},
'tokenizer_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
},
}
# TODO(PVP) - this should be removed in Transformers v5
a : List[Any] = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
class a ( _lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = TaTokenizer
snake_case_ = []
def __init__( self : List[Any] , lowercase_ : int=None , lowercase_ : Dict=None , lowercase_ : Dict="</s>" , lowercase_ : List[Any]="<unk>" , lowercase_ : int="<pad>" , lowercase_ : int=100 , lowercase_ : List[Any]=None , **lowercase_ : List[str] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
snake_case_ = [F"<extra_id_{i}>" for i in range(lowercase_ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
snake_case_ = len(set(filter(lambda lowercase_ : bool('''extra_id_''' in str(lowercase_ ) ) , lowercase_ ) ) )
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''' )
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , extra_ids=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ = vocab_file
snake_case_ = False if not self.vocab_file else True
snake_case_ = extra_ids
@staticmethod
def A_ ( lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : int ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
snake_case_ = TaTokenizerFast.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.''' , lowercase_ , )
return max_model_length
def A_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
logger.info(F"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def A_ ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
snake_case_ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def A_ ( self : int , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = [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 A_ ( self : Dict ):
return list(
set(filter(lambda lowercase_ : bool(re.search(R'''<extra_id_\d+>''' , lowercase_ ) ) is not None , self.additional_special_tokens ) ) )
def A_ ( self : Any ):
return [self.convert_tokens_to_ids(lowercase_ ) for token in self.get_sentinel_tokens()]
| 56
| 0
|
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__UpperCamelCase : Any = {'''UserAgent''': UserAgent().random}
def __SCREAMING_SNAKE_CASE ( A_ ):
lowerCAmelCase__ : int = script.contents[0]
lowerCAmelCase__ : Tuple = json.loads(data[data.find('''{"config"''' ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Any ,lowercase_ : Dict ):
lowerCAmelCase__ : Any = F'https://www.instagram.com/{username}/'
lowerCAmelCase__ : List[str] = self.get_json()
def __lowerCAmelCase ( self : Optional[int] ):
lowerCAmelCase__ : int = requests.get(self.url ,headers=lowercase_ ).text
lowerCAmelCase__ : Optional[Any] = BeautifulSoup(lowercase_ ,'''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self : Optional[Any] ):
return F'{self.__class__.__name__}(\'{self.username}\')'
def __str__( self : Union[str, Any] ):
return F'{self.fullname} ({self.username}) is {self.biography}'
@property
def __lowerCAmelCase ( self : Optional[Any] ):
return self.user_data["username"]
@property
def __lowerCAmelCase ( self : int ):
return self.user_data["full_name"]
@property
def __lowerCAmelCase ( self : Optional[int] ):
return self.user_data["biography"]
@property
def __lowerCAmelCase ( self : List[str] ):
return self.user_data["business_email"]
@property
def __lowerCAmelCase ( self : Optional[Any] ):
return self.user_data["external_url"]
@property
def __lowerCAmelCase ( self : Tuple ):
return self.user_data["edge_followed_by"]["count"]
@property
def __lowerCAmelCase ( self : Any ):
return self.user_data["edge_follow"]["count"]
@property
def __lowerCAmelCase ( self : Dict ):
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def __lowerCAmelCase ( self : List[str] ):
return self.user_data["profile_pic_url_hd"]
@property
def __lowerCAmelCase ( self : str ):
return self.user_data["is_verified"]
@property
def __lowerCAmelCase ( self : Optional[int] ):
return self.user_data["is_private"]
def __SCREAMING_SNAKE_CASE ( A_ = "github" ):
import os
if os.environ.get('''CI''' ):
return # test failing on GitHub Actions
lowerCAmelCase__ : int = InstagramUser(A_ )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , A_ )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 1_50
assert instagram_user.number_of_followers > 12_00_00
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''' )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCamelCase : Any = InstagramUser('''github''')
print(instagram_user)
print(F'''{instagram_user.number_of_posts = }''')
print(F'''{instagram_user.number_of_followers = }''')
print(F'''{instagram_user.number_of_followings = }''')
print(F'''{instagram_user.email = }''')
print(F'''{instagram_user.website = }''')
print(F'''{instagram_user.profile_picture_url = }''')
print(F'''{instagram_user.is_verified = }''')
print(F'''{instagram_user.is_private = }''')
| 106
|
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(__UpperCAmelCase ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
return min(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ = [90, 23, 6, 33, 21, 65, 123, 3_4423]
snake_case_ = math.log(len(__UpperCAmelCase ), 2 )
print('''Optimal value : ''', end='''''' )
print(minimax(0, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 56
| 0
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : List[str] = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Dict = [
'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'ResNetForImageClassification',
'ResNetModel',
'ResNetPreTrainedModel',
'ResNetBackbone',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : int = [
'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFResNetForImageClassification',
'TFResNetModel',
'TFResNetPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Optional[Any] = [
'FlaxResNetForImageClassification',
'FlaxResNetModel',
'FlaxResNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
__lowerCAmelCase : int = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 107
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
elif "subsample" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ ,snake_case_ = emb.weight.shape
snake_case_ = nn.Linear(__UpperCAmelCase, __UpperCAmelCase, bias=__UpperCAmelCase )
snake_case_ = emb.weight.data
return lin_layer
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = torch.load(__UpperCAmelCase, map_location='''cpu''' )
snake_case_ = mam_aaa['''args''']
snake_case_ = mam_aaa['''model''']
snake_case_ = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__UpperCAmelCase )
rename_keys(__UpperCAmelCase )
snake_case_ = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case_ = args.share_decoder_input_output_embed
snake_case_ = [int(__UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case_ = SpeechaTextConfig(
vocab_size=__UpperCAmelCase, max_source_positions=args.max_source_positions, max_target_positions=args.max_target_positions, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', num_conv_layers=len(__UpperCAmelCase ), conv_channels=args.conv_channels, conv_kernel_sizes=__UpperCAmelCase, input_feat_per_channel=args.input_feat_per_channel, input_channels=args.input_channels, tie_word_embeddings=__UpperCAmelCase, num_beams=5, max_length=200, use_cache=__UpperCAmelCase, decoder_start_token_id=2, early_stopping=__UpperCAmelCase, )
snake_case_ = SpeechaTextForConditionalGeneration(__UpperCAmelCase )
snake_case_ ,snake_case_ = model.model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0 and not set(__UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F" but all the following weights are missing {missing}" )
if tie_embeds:
snake_case_ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ = lm_head_weights
model.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
a : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 56
| 0
|
"""simple docstring"""
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 SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : Optional[int] ="longformer"
def __init__( self , snake_case__ = 512 , snake_case__ = 2 , snake_case__ = 1 , snake_case__ = 0 , snake_case__ = 2 , snake_case__ = 30_522 , snake_case__ = 768 , snake_case__ = 12 , snake_case__ = 12 , snake_case__ = 3_072 , snake_case__ = "gelu" , snake_case__ = 0.1 , snake_case__ = 0.1 , snake_case__ = 512 , snake_case__ = 2 , snake_case__ = 0.02 , snake_case__ = 1e-12 , snake_case__ = False , **snake_case__ , ):
"""simple docstring"""
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
lowerCAmelCase : Union[str, Any] = attention_window
lowerCAmelCase : List[Any] = sep_token_id
lowerCAmelCase : Dict = bos_token_id
lowerCAmelCase : int = eos_token_id
lowerCAmelCase : Dict = vocab_size
lowerCAmelCase : Dict = hidden_size
lowerCAmelCase : Optional[Any] = num_hidden_layers
lowerCAmelCase : Tuple = num_attention_heads
lowerCAmelCase : Union[str, Any] = hidden_act
lowerCAmelCase : Optional[int] = intermediate_size
lowerCAmelCase : Dict = hidden_dropout_prob
lowerCAmelCase : int = attention_probs_dropout_prob
lowerCAmelCase : Tuple = max_position_embeddings
lowerCAmelCase : Optional[int] = type_vocab_size
lowerCAmelCase : Any = initializer_range
lowerCAmelCase : Union[str, Any] = layer_norm_eps
lowerCAmelCase : List[str] = onnx_export
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ = "default" , snake_case__ = None ):
"""simple docstring"""
super().__init__(snake_case__ , snake_case__ , snake_case__ )
lowerCAmelCase : Any = True
@property
def lowercase__ ( self ):
"""simple docstring"""
if self.task == "multiple-choice":
lowerCAmelCase : Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCAmelCase : Any = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("global_attention_mask", dynamic_axis),
] )
@property
def lowercase__ ( self ):
"""simple docstring"""
lowerCAmelCase : Tuple = super().outputs
if self.task == "default":
lowerCAmelCase : Tuple = {0: "batch"}
return outputs
@property
def lowercase__ ( self ):
"""simple docstring"""
return 1e-4
@property
def lowercase__ ( self ):
"""simple docstring"""
return max(super().default_onnx_opset , 14 )
def lowercase__ ( self , snake_case__ , snake_case__ = -1 , snake_case__ = -1 , snake_case__ = False , snake_case__ = None , ):
"""simple docstring"""
lowerCAmelCase : Dict = super().generate_dummy_inputs(
preprocessor=snake_case__ , batch_size=snake_case__ , seq_length=snake_case__ , is_pair=snake_case__ , framework=snake_case__ )
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
lowerCAmelCase : Dict = torch.zeros_like(inputs["input_ids"] )
# make every second token global
lowerCAmelCase : List[Any] = 1
return inputs
| 108
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : str ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 56
| 0
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A: Optional[int] = logging.get_logger(__name__)
A: List[Any] = {
"google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
__lowerCAmelCase : Optional[int] = 'bit'
__lowerCAmelCase : Union[str, Any] = ['preactivation', 'bottleneck']
__lowerCAmelCase : Union[str, Any] = ['SAME', 'VALID']
def __init__( self , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=[256, 512, 1024, 2048] , _SCREAMING_SNAKE_CASE=[3, 4, 6, 3] , _SCREAMING_SNAKE_CASE="preactivation" , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE , ) -> int:
'''simple docstring'''
super().__init__(**_SCREAMING_SNAKE_CASE )
if layer_type not in self.layer_types:
raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types )}" )
if global_padding is not None:
if global_padding.upper() in self.supported_padding:
UpperCAmelCase : List[str] = global_padding.upper()
else:
raise ValueError(F"Padding strategy {global_padding} not supported" )
UpperCAmelCase : Optional[Any] = num_channels
UpperCAmelCase : Optional[int] = embedding_size
UpperCAmelCase : List[str] = hidden_sizes
UpperCAmelCase : int = depths
UpperCAmelCase : Tuple = layer_type
UpperCAmelCase : Tuple = hidden_act
UpperCAmelCase : Optional[int] = global_padding
UpperCAmelCase : Any = num_groups
UpperCAmelCase : Any = drop_path_rate
UpperCAmelCase : List[str] = embedding_dynamic_padding
UpperCAmelCase : Dict = output_stride
UpperCAmelCase : Union[str, Any] = width_factor
UpperCAmelCase : Tuple = ["""stem"""] + [F"stage{idx}" for idx in range(1 , len(_SCREAMING_SNAKE_CASE ) + 1 )]
UpperCAmelCase , UpperCAmelCase : Optional[Any] = get_aligned_output_features_output_indices(
out_features=_SCREAMING_SNAKE_CASE , out_indices=_SCREAMING_SNAKE_CASE , stage_names=self.stage_names )
| 109
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a : int = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
config.addinivalue_line(
'''markers''', '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''', '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''', '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''', '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''', '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''', '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
snake_case_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCAmelCase, id=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if exitstatus == 5:
snake_case_ = 0
# Doctest custom flag to ignore output.
a : Union[str, Any] = doctest.register_optionflag('IGNORE_RESULT')
a : Optional[int] = doctest.OutputChecker
class a ( _lowerCamelCase ):
def A_ ( self : List[Any] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Optional[int] ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , lowercase_ , lowercase_ , lowercase_ )
a : List[Any] = CustomOutputChecker
a : Optional[int] = HfDoctestModule
a : Tuple = HfDocTestParser
| 56
| 0
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 110
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
a : Dict = logging.get_logger(__name__)
a : List[str] = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class a ( _lowerCamelCase ):
snake_case_ = "marian"
snake_case_ = ["past_key_values"]
snake_case_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , lowercase_ : Optional[Any]=5_8101 , lowercase_ : Dict=None , lowercase_ : List[str]=1024 , lowercase_ : Optional[Any]=12 , lowercase_ : int=4096 , lowercase_ : Any=16 , lowercase_ : Optional[int]=12 , lowercase_ : str=4096 , lowercase_ : Union[str, Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Union[str, Any]=True , lowercase_ : int="gelu" , lowercase_ : Dict=1024 , lowercase_ : int=0.1 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.02 , lowercase_ : int=5_8100 , lowercase_ : Optional[Any]=False , lowercase_ : Any=5_8100 , lowercase_ : Optional[int]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=True , **lowercase_ : Any , ):
snake_case_ = vocab_size
snake_case_ = decoder_vocab_size or vocab_size
snake_case_ = max_position_embeddings
snake_case_ = d_model
snake_case_ = encoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class a ( _lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A_ ( self : Union[str, Any] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ = {0: '''batch'''}
snake_case_ = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A_ ( self : Dict ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super().outputs
else:
snake_case_ = super(lowercase_ , self ).outputs
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A_ ( self : Dict , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
snake_case_ = seq_length if not self.use_past else 1
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case_ = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
snake_case_ = common_inputs['''decoder_input_ids'''].shape[1]
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = decoder_seq_length + 3
snake_case_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
snake_case_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ ,snake_case_ = self.num_layers
snake_case_ = min(lowercase_ , lowercase_ )
snake_case_ = max(lowercase_ , lowercase_ ) - min_num_layers
snake_case_ = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
snake_case_ = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def A_ ( self : Union[str, Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case_ = seqlen + 2
snake_case_ ,snake_case_ = self.num_layers
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = common_inputs['''attention_mask'''].dtype
snake_case_ = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
snake_case_ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def A_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ = tokenizer.num_special_tokens_to_add(lowercase_ )
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def A_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
snake_case_ = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def A_ ( self : Dict , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : List[str] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
snake_case_ = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
@property
def A_ ( self : List[str] ):
return 1e-4
| 56
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from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase = logging.get_logger(__name__)
class UpperCAmelCase ( _lowerCamelCase ,_lowerCamelCase ):
A__ : List[str] = "maskformer-swin"
A__ : Tuple = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(self : Dict , snake_case__ : List[str]=2_24 , snake_case__ : List[str]=4 , snake_case__ : List[str]=3 , snake_case__ : Tuple=96 , snake_case__ : Any=[2, 2, 6, 2] , snake_case__ : Any=[3, 6, 12, 24] , snake_case__ : Any=7 , snake_case__ : int=4.0 , snake_case__ : str=True , snake_case__ : List[str]=0.0 , snake_case__ : int=0.0 , snake_case__ : Optional[Any]=0.1 , snake_case__ : Optional[int]="gelu" , snake_case__ : Optional[Any]=False , snake_case__ : Tuple=0.02 , snake_case__ : int=1e-5 , snake_case__ : Any=None , snake_case__ : int=None , **snake_case__ : Tuple , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowercase_ )
snake_case : List[str] = image_size
snake_case : List[Any] = patch_size
snake_case : Optional[int] = num_channels
snake_case : Dict = embed_dim
snake_case : int = depths
snake_case : Optional[int] = len(lowercase_ )
snake_case : Optional[int] = num_heads
snake_case : Dict = window_size
snake_case : Dict = mlp_ratio
snake_case : str = qkv_bias
snake_case : Any = hidden_dropout_prob
snake_case : Any = attention_probs_dropout_prob
snake_case : str = drop_path_rate
snake_case : Any = hidden_act
snake_case : Optional[int] = use_absolute_embeddings
snake_case : Tuple = layer_norm_eps
snake_case : Any = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
snake_case : str = int(embed_dim * 2 ** (len(lowercase_ ) - 1) )
snake_case : Union[str, Any] = ["stem"] + [f"""stage{idx}""" for idx in range(1 , len(lowercase_ ) + 1 )]
snake_case , snake_case : Tuple = get_aligned_output_features_output_indices(
out_features=lowercase_ , out_indices=lowercase_ , stage_names=self.stage_names )
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|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = CycleDiffusionPipeline
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
snake_case_ = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A_ ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ = 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 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
snake_case_ = 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 , )
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case_ = CLIPTextModel(lowercase_ )
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 A_ ( self : Any , lowercase_ : int , lowercase_ : Optional[Any]=0 ):
snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ = image / 2 + 0.5
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(lowercase_ )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def A_ ( self : Union[str, Any] ):
snake_case_ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , '''half''' ):
snake_case_ = module.half()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Optional[int] ):
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def A_ ( self : List[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_save_load_optional_components()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Union[str, Any] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def A_ ( self : List[str] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
assert np.abs(image - expected_image ).max() < 2e-2
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|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'RagModel',
'RagPreTrainedModel',
'RagSequenceForGeneration',
'RagTokenForGeneration',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
'TFRagModel',
'TFRagPreTrainedModel',
'TFRagSequenceForGeneration',
'TFRagTokenForGeneration',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 283
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class a ( _lowerCamelCase ):
snake_case_ = "big_bird"
def __init__( self : Union[str, Any] , lowercase_ : List[Any]=5_0358 , lowercase_ : Tuple=768 , lowercase_ : Dict=12 , lowercase_ : str=12 , lowercase_ : Tuple=3072 , lowercase_ : Any="gelu_new" , lowercase_ : Optional[Any]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=4096 , lowercase_ : List[Any]=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[int]=1e-12 , lowercase_ : Tuple=True , lowercase_ : Tuple=0 , lowercase_ : str=1 , lowercase_ : Union[str, Any]=2 , lowercase_ : Optional[Any]=66 , lowercase_ : Optional[int]="block_sparse" , lowercase_ : Any=True , lowercase_ : List[str]=False , lowercase_ : Any=64 , lowercase_ : Tuple=3 , lowercase_ : Tuple=None , **lowercase_ : Tuple , ):
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , sep_token_id=lowercase_ , **lowercase_ , )
snake_case_ = vocab_size
snake_case_ = max_position_embeddings
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_ = type_vocab_size
snake_case_ = layer_norm_eps
snake_case_ = use_cache
snake_case_ = rescale_embeddings
snake_case_ = attention_type
snake_case_ = use_bias
snake_case_ = block_size
snake_case_ = num_random_blocks
snake_case_ = classifier_dropout
class a ( _lowerCamelCase ):
@property
def A_ ( self : str ):
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
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|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
'umberto-commoncrawl-cased-v1': (
'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
),
'umberto-wikipedia-uncased-v1': (
'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
),
}
class snake_case_ ( _lowerCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = "camembert"
def __init__( self : List[Any] , _UpperCamelCase : Dict=3_0_5_2_2 , _UpperCamelCase : Dict=7_6_8 , _UpperCamelCase : str=1_2 , _UpperCamelCase : Optional[Any]=1_2 , _UpperCamelCase : Dict=3_0_7_2 , _UpperCamelCase : Union[str, Any]="gelu" , _UpperCamelCase : int=0.1 , _UpperCamelCase : str=0.1 , _UpperCamelCase : Union[str, Any]=5_1_2 , _UpperCamelCase : List[str]=2 , _UpperCamelCase : Optional[int]=0.02 , _UpperCamelCase : Union[str, Any]=1e-12 , _UpperCamelCase : Dict=1 , _UpperCamelCase : Any=0 , _UpperCamelCase : int=2 , _UpperCamelCase : List[str]="absolute" , _UpperCamelCase : Any=True , _UpperCamelCase : int=None , **_UpperCamelCase : Union[str, Any] , ) ->str:
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = position_embedding_type
snake_case_ = use_cache
snake_case_ = classifier_dropout
class snake_case_ ( _lowerCamelCase ):
'''simple docstring'''
@property
def snake_case__( self : Any ) ->Tuple:
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 8
|
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''simple docstring'''
assert isinstance(__UpperCAmelCase, __UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''', [False, True] )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = SqlDatasetReader(
'''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase, keep_in_memory=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
'''features''', [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
], )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, features=__UpperCAmelCase, cache_dir=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(__UpperCAmelCase ) ) as con:
snake_case_ = con.cursor()
cur.execute('''SELECT * FROM dataset''' )
for row in cur:
yield row
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=1 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=2 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
with pytest.raises(__UpperCAmelCase ):
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=0 ).write()
| 56
| 0
|
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
_A : str = nn.ModuleList([src_layers[i] for i in layers_to_copy] )
assert len(__UpperCAmelCase ) == len(__UpperCAmelCase ), f'''{len(__UpperCAmelCase )} != {len(__UpperCAmelCase )}'''
dest_layers.load_state_dict(layers_to_copy.state_dict() )
_snake_case = {
# maps num layers in teacher -> num_layers in student -> which teacher layers to copy.
# 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
12: {
1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
2: [0, 6],
3: [0, 6, 11],
4: [0, 4, 8, 11],
6: [0, 2, 4, 7, 9, 11],
9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
12: list(range(12)),
},
16: { # maps num layers in student -> which teacher layers to copy
1: [0],
2: [0, 15],
3: [0, 8, 15],
4: [0, 5, 10, 15],
6: [0, 3, 6, 9, 12, 15],
8: [0, 2, 4, 6, 8, 10, 12, 15],
9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
16: list(range(16)),
},
6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_snake_case = {
# maps num layers in student -> which teacher layers to copy.
6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
try:
_A : int = LAYERS_TO_COPY[n_teacher][n_student]
return val
except KeyError:
if n_student != n_teacher:
warnings.warn(
f'''no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'''
f''' {n_student}''' )
return list(range(__UpperCAmelCase ) )
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
if n_student > n_teacher:
raise ValueError(f'''Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}''' )
elif n_teacher == n_student:
return list(range(__UpperCAmelCase ) )
elif n_student == 1:
return [n_teacher - 1]
else:
return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def lowerCAmelCase_ ( snake_case_,snake_case_ = "student",snake_case_ = None,snake_case_ = None,snake_case_=False,snake_case_=None,snake_case_=None,**snake_case_,):
_A : Dict = """encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."""
assert (e is not None) or (d is not None), _msg
if isinstance(__UpperCAmelCase,__UpperCAmelCase ):
AutoTokenizer.from_pretrained(__UpperCAmelCase ).save_pretrained(__UpperCAmelCase ) # purely for convenience
_A : Any = AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase ).eval()
else:
assert isinstance(__UpperCAmelCase,__UpperCAmelCase ), f'''teacher must be a model or string got type {type(__UpperCAmelCase )}'''
_A : Dict = teacher.config.to_diff_dict()
try:
_A , _A : List[Any] = teacher.config.encoder_layers, teacher.config.decoder_layers
if e is None:
_A : Optional[int] = teacher_e
if d is None:
_A : Tuple = teacher_d
init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d} )
except AttributeError: # T5
if hasattr(teacher.config,"""num_encoder_layers""" ):
_A , _A : Optional[Any] = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
else:
_A , _A : Union[str, Any] = teacher.config.num_layers, teacher.config.num_decoder_layers
if e is None:
_A : Dict = teacher_e
if d is None:
_A : List[Any] = teacher_d
if hasattr(teacher.config,"""num_encoder_layers""" ):
init_kwargs.update({"""num_encoder_layers""": e, """num_decoder_layers""": d} )
else:
init_kwargs.update({"""num_layers""": e, """num_decoder_layers""": d} )
# Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
init_kwargs.update(__UpperCAmelCase )
# Copy weights
_A : List[Any] = teacher.config_class(**__UpperCAmelCase )
_A : Tuple = AutoModelForSeqaSeqLM.from_config(__UpperCAmelCase )
# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
_A : str = student.load_state_dict(teacher.state_dict(),strict=__UpperCAmelCase )
assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys.
if copy_first_teacher_layers: # Our copying is done. We just log and save
_A , _A : int = list(range(__UpperCAmelCase ) ), list(range(__UpperCAmelCase ) )
logger.info(
f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'''
f''' {save_path}''' )
student.save_pretrained(__UpperCAmelCase )
return student, e_layers_to_copy, d_layers_to_copy
# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
if e_layers_to_copy is None:
_A : Dict = pick_layers_to_copy(__UpperCAmelCase,__UpperCAmelCase )
if d_layers_to_copy is None:
_A : Optional[Any] = pick_layers_to_copy(__UpperCAmelCase,__UpperCAmelCase )
try:
if hasattr(
__UpperCAmelCase,"""prophetnet""" ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
copy_layers(teacher.prophetnet.encoder.layers,student.prophetnet.encoder.layers,__UpperCAmelCase )
copy_layers(teacher.prophetnet.decoder.layers,student.prophetnet.decoder.layers,__UpperCAmelCase )
else:
copy_layers(teacher.model.encoder.layers,student.model.encoder.layers,__UpperCAmelCase )
copy_layers(teacher.model.decoder.layers,student.model.decoder.layers,__UpperCAmelCase )
except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block
copy_layers(teacher.encoder.block,student.encoder.block,__UpperCAmelCase )
copy_layers(teacher.decoder.block,student.decoder.block,__UpperCAmelCase )
logger.info(
f'''Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}''' )
_A : Optional[int] = {
"""teacher_type""": teacher.config.model_type,
"""copied_encoder_layers""": e_layers_to_copy,
"""copied_decoder_layers""": d_layers_to_copy,
}
student.save_pretrained(__UpperCAmelCase )
# Save information about copying for easier reproducibility
return student, e_layers_to_copy, d_layers_to_copy
if __name__ == "__main__":
fire.Fire(create_student_by_copying_alternating_layers)
| 26
|
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCase )
if ret % 2 == 0:
cuts.append(__UpperCAmelCase )
return ret
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
a ,a : Dict = 10, 9
a : Dict = defaultdict(list)
a : dict[int, bool] = {}
a : list[int] = []
a : Tuple = 0
a : str = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 56
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class __lowerCAmelCase ( _lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = KandinskyVaaControlnetPipeline
__UpperCAmelCase : Optional[Any] = ['image_embeds', 'negative_image_embeds', 'hint']
__UpperCAmelCase : Dict = ['image_embeds', 'negative_image_embeds', 'hint']
__UpperCAmelCase : int = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
__UpperCAmelCase : List[Any] = False
@property
def __UpperCAmelCase ( self ):
return 32
@property
def __UpperCAmelCase ( self ):
return 32
@property
def __UpperCAmelCase ( self ):
return self.time_input_dim
@property
def __UpperCAmelCase ( self ):
return self.time_input_dim * 4
@property
def __UpperCAmelCase ( self ):
return 100
@property
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
__a = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__a = UNetaDConditionModel(**lowercase_ )
return model
@property
def __UpperCAmelCase ( self ):
return {
"block_out_channels": [32, 32, 64, 64],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
__a = VQModel(**self.dummy_movq_kwargs )
return model
def __UpperCAmelCase ( self ):
__a = self.dummy_unet
__a = self.dummy_movq
__a = DDIMScheduler(
num_train_timesteps=1_000 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , steps_offset=1 , prediction_type='''epsilon''' , thresholding=lowercase_ , )
__a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __UpperCAmelCase ( self , _a , _a=0 ):
__a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
__a = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowercase_ )
# create hint
__a = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
if str(lowercase_ ).startswith('''mps''' ):
__a = torch.manual_seed(lowercase_ )
else:
__a = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
__a = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def __UpperCAmelCase ( self ):
__a = '''cpu'''
__a = self.get_dummy_components()
__a = self.pipeline_class(**lowercase_ )
__a = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
__a = pipe(**self.get_dummy_inputs(lowercase_ ) )
__a = output.images
__a = pipe(
**self.get_dummy_inputs(lowercase_ ) , return_dict=lowercase_ , )[0]
__a = image[0, -3:, -3:, -1]
__a = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__a = np.array(
[0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''
@slow
@require_torch_gpu
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self ):
__a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
__a = torch.from_numpy(np.array(lowercase_ ) ).float() / 255.0
__a = hint.permute(2 , 0 , 1 ).unsqueeze(0 )
__a = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(lowercase_ )
__a = KandinskyVaaControlnetPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' , torch_dtype=torch.floataa )
__a = pipeline.to(lowercase_ )
pipeline.set_progress_bar_config(disable=lowercase_ )
__a = '''A robot, 4k photo'''
__a = torch.Generator(device='''cuda''' ).manual_seed(0 )
__a , __a = pipe_prior(
lowercase_ , generator=lowercase_ , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__a = torch.Generator(device='''cuda''' ).manual_seed(0 )
__a = pipeline(
image_embeds=lowercase_ , negative_image_embeds=lowercase_ , hint=lowercase_ , generator=lowercase_ , num_inference_steps=100 , output_type='''np''' , )
__a = output.images[0]
assert image.shape == (512, 512, 3)
assert_mean_pixel_difference(lowercase_ , lowercase_ )
| 45
|
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56
| 0
|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
'facebook/data2vec-vision-base-ft': (
'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
),
}
class A_ ( _lowerCamelCase ):
_UpperCAmelCase : str = '''data2vec-vision'''
def __init__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : str=7_6_8 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=1_2 ,SCREAMING_SNAKE_CASE__ : int=1_2 ,SCREAMING_SNAKE_CASE__ : str=3_0_7_2 ,SCREAMING_SNAKE_CASE__ : Dict="gelu" ,SCREAMING_SNAKE_CASE__ : Any=0.0 ,SCREAMING_SNAKE_CASE__ : Optional[int]=0.0 ,SCREAMING_SNAKE_CASE__ : int=0.02 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=1E-12 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=2_2_4 ,SCREAMING_SNAKE_CASE__ : List[Any]=1_6 ,SCREAMING_SNAKE_CASE__ : List[Any]=3 ,SCREAMING_SNAKE_CASE__ : Tuple=False ,SCREAMING_SNAKE_CASE__ : Dict=False ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ,SCREAMING_SNAKE_CASE__ : List[Any]=False ,SCREAMING_SNAKE_CASE__ : Dict=0.1 ,SCREAMING_SNAKE_CASE__ : Optional[int]=0.1 ,SCREAMING_SNAKE_CASE__ : Optional[int]=True ,SCREAMING_SNAKE_CASE__ : Optional[int]=[3, 5, 7, 1_1] ,SCREAMING_SNAKE_CASE__ : Tuple=[1, 2, 3, 6] ,SCREAMING_SNAKE_CASE__ : List[Any]=True ,SCREAMING_SNAKE_CASE__ : Tuple=0.4 ,SCREAMING_SNAKE_CASE__ : Tuple=2_5_6 ,SCREAMING_SNAKE_CASE__ : Optional[int]=1 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=False ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=2_5_5 ,**SCREAMING_SNAKE_CASE__ : str ,):
super().__init__(**lowercase_)
__lowerCamelCase : Tuple = hidden_size
__lowerCamelCase : int = num_hidden_layers
__lowerCamelCase : List[Any] = num_attention_heads
__lowerCamelCase : str = intermediate_size
__lowerCamelCase : Optional[int] = hidden_act
__lowerCamelCase : Any = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : Union[str, Any] = initializer_range
__lowerCamelCase : Optional[Any] = layer_norm_eps
__lowerCamelCase : str = image_size
__lowerCamelCase : Union[str, Any] = patch_size
__lowerCamelCase : Tuple = num_channels
__lowerCamelCase : int = use_mask_token
__lowerCamelCase : Union[str, Any] = use_absolute_position_embeddings
__lowerCamelCase : Any = use_relative_position_bias
__lowerCamelCase : Any = use_shared_relative_position_bias
__lowerCamelCase : Dict = layer_scale_init_value
__lowerCamelCase : Union[str, Any] = drop_path_rate
__lowerCamelCase : Optional[int] = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCamelCase : Any = out_indices
__lowerCamelCase : List[Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCamelCase : Tuple = use_auxiliary_head
__lowerCamelCase : List[str] = auxiliary_loss_weight
__lowerCamelCase : Any = auxiliary_channels
__lowerCamelCase : int = auxiliary_num_convs
__lowerCamelCase : int = auxiliary_concat_input
__lowerCamelCase : str = semantic_loss_ignore_index
class A_ ( _lowerCamelCase ):
_UpperCAmelCase : Dict = version.parse('''1.11''' )
@property
def lowerCAmelCase ( self : Any):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
])
@property
def lowerCAmelCase ( self : str):
return 1E-4
| 73
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Any = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = DPTConfig()
if "large" in checkpoint_url:
snake_case_ = 1024
snake_case_ = 4096
snake_case_ = 24
snake_case_ = 16
snake_case_ = [5, 11, 17, 23]
snake_case_ = [256, 512, 1024, 1024]
snake_case_ = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case_ = True
snake_case_ = 150
snake_case_ = '''huggingface/label-files'''
snake_case_ = '''ade20k-id2label.json'''
snake_case_ = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset''' ) ), '''r''' ) )
snake_case_ = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = [1, 150, 480, 480]
return config, expected_shape
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case_ = name.replace('''pretrained.model''', '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case_ = name.replace('''pretrained.model''', '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case_ = name.replace('''patch_embed''', '''patch_embeddings''' )
if "pos_embed" in name:
snake_case_ = name.replace('''pos_embed''', '''position_embeddings''' )
if "attn.proj" in name:
snake_case_ = name.replace('''attn.proj''', '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case_ = name.replace('''proj''', '''projection''' )
if "blocks" in name:
snake_case_ = name.replace('''blocks''', '''layer''' )
if "mlp.fc1" in name:
snake_case_ = name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case_ = name.replace('''mlp.fc2''', '''output.dense''' )
if "norm1" in name:
snake_case_ = name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
snake_case_ = name.replace('''norm2''', '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case_ = name.replace('''scratch.output_conv''', '''head''' )
if "scratch" in name:
snake_case_ = name.replace('''scratch''', '''neck''' )
if "layer1_rn" in name:
snake_case_ = name.replace('''layer1_rn''', '''convs.0''' )
if "layer2_rn" in name:
snake_case_ = name.replace('''layer2_rn''', '''convs.1''' )
if "layer3_rn" in name:
snake_case_ = name.replace('''layer3_rn''', '''convs.2''' )
if "layer4_rn" in name:
snake_case_ = name.replace('''layer4_rn''', '''convs.3''' )
if "refinenet" in name:
snake_case_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case_ = name.replace(F"refinenet{layer_idx}", F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
snake_case_ = name.replace('''out_conv''', '''projection''' )
if "resConfUnit1" in name:
snake_case_ = name.replace('''resConfUnit1''', '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case_ = name.replace('''resConfUnit2''', '''residual_layer2''' )
if "conv1" in name:
snake_case_ = name.replace('''conv1''', '''convolution1''' )
if "conv2" in name:
snake_case_ = name.replace('''conv2''', '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.0.project.0''', '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.0.project.0''', '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.0.project.0''', '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.0.project.0''', '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.3''', '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.4''', '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.3''', '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.4''', '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.3''', '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.3''', '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.4''', '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case_ = name.replace('''pretrained''', '''dpt''' )
if "bn" in name:
snake_case_ = name.replace('''bn''', '''batch_norm''' )
if "head" in name:
snake_case_ = name.replace('''head''', '''head.head''' )
if "encoder.norm" in name:
snake_case_ = name.replace('''encoder.norm''', '''layernorm''' )
if "auxlayer" in name:
snake_case_ = name.replace('''auxlayer''', '''auxiliary_head.head''' )
return name
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{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 __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ = Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dpt_config(__UpperCAmelCase )
# load original state_dict from URL
snake_case_ = torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
snake_case_ = state_dict.pop(__UpperCAmelCase )
snake_case_ = val
# read in qkv matrices
read_in_q_k_v(__UpperCAmelCase, __UpperCAmelCase )
# load HuggingFace model
snake_case_ = DPTForSemanticSegmentation(__UpperCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
# Check outputs on an image
snake_case_ = 480 if '''ade''' in checkpoint_url else 384
snake_case_ = DPTImageProcessor(size=__UpperCAmelCase )
snake_case_ = prepare_img()
snake_case_ = image_processor(__UpperCAmelCase, return_tensors='''pt''' )
# forward pass
snake_case_ = model(**__UpperCAmelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase ).predicted_depth
# Assert logits
snake_case_ = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
snake_case_ = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__UpperCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3], __UpperCAmelCase, atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3], __UpperCAmelCase )
)
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add model''', use_temp_dir=__UpperCAmelCase, )
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add image processor''', use_temp_dir=__UpperCAmelCase, )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
a : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 56
| 0
|
import collections
import os
import re
from pathlib import Path
lowercase__ : str = 'src/transformers'
# Matches is_xxx_available()
lowercase__ : Tuple = re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowercase__ : List[str] = re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase__ : List[str] = re.compile(R"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
lowercase__ : Optional[int] = re.compile(R"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
lowercase__ : str = re.compile(R"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase__ : Tuple = re.compile(R"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
lowercase__ : int = re.compile(R"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
lowercase__ : Optional[int] = re.compile(R"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
lowercase__ : Optional[int] = re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
lowercase__ : List[str] = re.compile(R"^\s*try:")
# Catches a line with else:
lowercase__ : str = re.compile(R"^\s*else:")
def A_ ( snake_case : Union[str, Any] ) -> Dict:
'''simple docstring'''
if _re_test_backend.search(__UpperCAmelCase ) is None:
return None
__UpperCamelCase = [b[0] for b in _re_backend.findall(__UpperCAmelCase )]
backends.sort()
return "_and_".join(__UpperCAmelCase )
def A_ ( snake_case : int ) -> Optional[Any]:
'''simple docstring'''
with open(__UpperCAmelCase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
__UpperCamelCase = f.readlines()
__UpperCamelCase = 0
while line_index < len(__UpperCAmelCase ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__UpperCAmelCase ):
return None
# First grab the objects without a specific backend in _import_structure
__UpperCamelCase = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
__UpperCamelCase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__UpperCAmelCase ):
__UpperCamelCase = _re_one_line_import_struct.search(__UpperCAmelCase ).groups()[0]
__UpperCamelCase = re.findall(r'''\[([^\]]+)\]''' , __UpperCAmelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
__UpperCamelCase = _re_import_struct_key_value.search(__UpperCAmelCase )
if single_line_import_search is not None:
__UpperCamelCase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
__UpperCamelCase = {'''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.
__UpperCamelCase = 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:
__UpperCamelCase = 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
__UpperCamelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
__UpperCamelCase = lines[line_index]
if _re_import_struct_add_one.search(__UpperCAmelCase ) is not None:
objects.append(_re_import_struct_add_one.search(__UpperCAmelCase ).groups()[0] )
elif _re_import_struct_add_many.search(__UpperCAmelCase ) is not None:
__UpperCamelCase = _re_import_struct_add_many.search(__UpperCAmelCase ).groups()[0].split(''', ''' )
__UpperCamelCase = [obj[1:-1] for obj in imports if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif _re_between_brackets.search(__UpperCAmelCase ) is not None:
__UpperCamelCase = _re_between_brackets.search(__UpperCAmelCase ).groups()[0].split(''', ''' )
__UpperCamelCase = [obj[1:-1] for obj in imports if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif _re_quote_object.search(__UpperCAmelCase ) is not None:
objects.append(_re_quote_object.search(__UpperCAmelCase ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 12 + '''"''' ):
objects.append(line[13:-3] )
line_index += 1
__UpperCamelCase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
__UpperCamelCase = []
while (
line_index < len(__UpperCAmelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
__UpperCamelCase = lines[line_index]
__UpperCamelCase = _re_import.search(__UpperCAmelCase )
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
__UpperCamelCase = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(__UpperCAmelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
__UpperCamelCase = 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:
__UpperCamelCase = 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
__UpperCamelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
__UpperCamelCase = lines[line_index]
__UpperCamelCase = _re_import.search(__UpperCAmelCase )
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
__UpperCamelCase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def A_ ( snake_case : List[str] , snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
def find_duplicates(snake_case : List[Any] ):
return [k for k, v in collections.Counter(__UpperCAmelCase ).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!"]
__UpperCamelCase = []
for key in import_dict_objects.keys():
__UpperCamelCase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f"Duplicate _import_structure definitions for: {duplicate_imports}" )
__UpperCamelCase = 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] ) ):
__UpperCamelCase = '''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_ ( ) -> Tuple:
'''simple docstring'''
__UpperCamelCase = []
for root, _, files in os.walk(__UpperCAmelCase ):
if "__init__.py" in files:
__UpperCamelCase = os.path.join(__UpperCAmelCase , '''__init__.py''' )
__UpperCamelCase = parse_init(__UpperCAmelCase )
if objects is not None:
__UpperCamelCase = analyze_results(*__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
__UpperCamelCase = f"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append('''\n'''.join(__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > 0:
raise ValueError('''\n\n'''.join(__UpperCAmelCase ) )
def A_ ( ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase = []
for path, directories, files in os.walk(__UpperCAmelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(__UpperCAmelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__UpperCAmelCase ) / folder).glob('''*.py''' ) ) ) == 0:
continue
__UpperCamelCase = str((Path(__UpperCAmelCase ) / folder).relative_to(__UpperCAmelCase ) )
__UpperCamelCase = short_path.replace(os.path.sep , '''.''' )
submodules.append(__UpperCAmelCase )
for fname in files:
if fname == "__init__.py":
continue
__UpperCamelCase = str((Path(__UpperCAmelCase ) / fname).relative_to(__UpperCAmelCase ) )
__UpperCamelCase = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(__UpperCAmelCase )
return submodules
lowercase__ : Dict = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
'models.esm.openfold_utils',
]
def A_ ( ) -> Optional[int]:
'''simple docstring'''
from transformers.utils import direct_transformers_import
__UpperCamelCase = direct_transformers_import(__UpperCAmelCase )
__UpperCamelCase = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(__UpperCAmelCase , '''__init__.py''' ) , '''r''' ) as f:
__UpperCamelCase = f.read()
import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' , __UpperCAmelCase ) ) )
__UpperCamelCase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(__UpperCAmelCase ) > 0:
__UpperCamelCase = '''\n'''.join(f"- {module}" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed 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()
| 328
|
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
a : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 56
| 0
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=_lowerCamelCase ):
snake_case__ : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _A ( metaclass=_lowerCamelCase ):
snake_case__ : Optional[int] = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _A ( metaclass=_lowerCamelCase ):
snake_case__ : int = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _A ( metaclass=_lowerCamelCase ):
snake_case__ : Optional[Any] = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _A ( metaclass=_lowerCamelCase ):
snake_case__ : Any = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
class _A ( metaclass=_lowerCamelCase ):
snake_case__ : Dict = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(self , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
@classmethod
def A__ ( cls , *__lowerCAmelCase , **__lowerCAmelCase ):
"""simple docstring"""
requires_backends(cls , ["""torch""", """transformers""", """onnx"""] )
| 197
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
re.sub('''<n>''', '''''', __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 56
| 0
|
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ): # noqa: E741
while r - l > 1:
UpperCAmelCase = (l + r) // 2
if v[m] >= key:
UpperCAmelCase = m
else:
UpperCAmelCase = m # noqa: E741
return r
def _lowerCAmelCase ( lowercase_ ):
if len(__UpperCAmelCase ) == 0:
return 0
UpperCAmelCase = [0] * len(__UpperCAmelCase )
UpperCAmelCase = 1
UpperCAmelCase = v[0]
for i in range(1 , len(__UpperCAmelCase ) ):
if v[i] < tail[0]:
UpperCAmelCase = v[i]
elif v[i] > tail[length - 1]:
UpperCAmelCase = v[i]
length += 1
else:
UpperCAmelCase = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 78
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : str = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Optional[Any] = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
a : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 56
| 0
|
"""simple docstring"""
from __future__ import annotations
class A_ :
"""simple docstring"""
def __init__( self :int , lowerCamelCase_ :Tuple=None ):
"""simple docstring"""
lowerCamelCase__ : Any =data
lowerCamelCase__ : str =None
def __repr__( self :int ):
"""simple docstring"""
lowerCamelCase__ : Dict =[]
lowerCamelCase__ : Union[str, Any] =self
while temp:
string_rep.append(f"""{temp.data}""" )
lowerCamelCase__ : Tuple =temp.next
return "->".join(lowercase_ )
def lowerCAmelCase_ ( snake_case_ : Tuple ) ->str:
if not elements_list:
raise Exception('The Elements List is empty' )
lowerCamelCase__ : int =Node(elements_list[0] )
for i in range(1 , len(__UpperCAmelCase ) ):
lowerCamelCase__ : Union[str, Any] =Node(elements_list[i] )
lowerCamelCase__ : Any =current.next
return head
def lowerCAmelCase_ ( snake_case_ : str ) ->None:
if head_node is not None and isinstance(__UpperCAmelCase , __UpperCAmelCase ):
print_reverse(head_node.next )
print(head_node.data )
def lowerCAmelCase_ ( ) ->Union[str, Any]:
from doctest import testmod
testmod()
lowerCamelCase__ : Tuple =make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3] )
print('Linked List:' )
print(__UpperCAmelCase )
print('Elements in Reverse:' )
print_reverse(__UpperCAmelCase )
if __name__ == "__main__":
main()
| 126
|
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class a ( tf.keras.optimizers.schedules.LearningRateSchedule ):
def __init__( self : Optional[Any] , lowercase_ : float , lowercase_ : Callable , lowercase_ : int , lowercase_ : float = 1.0 , lowercase_ : str = None , ):
super().__init__()
snake_case_ = initial_learning_rate
snake_case_ = warmup_steps
snake_case_ = power
snake_case_ = decay_schedule_fn
snake_case_ = name
def __call__( self : Tuple , lowercase_ : str ):
with tf.name_scope(self.name or '''WarmUp''' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
snake_case_ = tf.cast(lowercase_ , tf.floataa )
snake_case_ = tf.cast(self.warmup_steps , tf.floataa )
snake_case_ = global_step_float / warmup_steps_float
snake_case_ = self.initial_learning_rate * tf.math.pow(lowercase_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=lowercase_ , )
def A_ ( self : Any ):
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, __UpperCAmelCase = 0.9, __UpperCAmelCase = 0.9_9_9, __UpperCAmelCase = 1e-8, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 0.0, __UpperCAmelCase = 1.0, __UpperCAmelCase = None, ) -> List[str]:
'''simple docstring'''
snake_case_ = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=__UpperCAmelCase, decay_steps=num_train_steps - num_warmup_steps, end_learning_rate=init_lr * min_lr_ratio, power=__UpperCAmelCase, )
if num_warmup_steps:
snake_case_ = WarmUp(
initial_learning_rate=__UpperCAmelCase, decay_schedule_fn=__UpperCAmelCase, warmup_steps=__UpperCAmelCase, )
if weight_decay_rate > 0.0:
snake_case_ = AdamWeightDecay(
learning_rate=__UpperCAmelCase, weight_decay_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''], include_in_weight_decay=__UpperCAmelCase, )
else:
snake_case_ = tf.keras.optimizers.Adam(
learning_rate=__UpperCAmelCase, beta_a=__UpperCAmelCase, beta_a=__UpperCAmelCase, epsilon=__UpperCAmelCase, clipnorm=__UpperCAmelCase, global_clipnorm=__UpperCAmelCase, )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class a ( _lowerCamelCase ):
def __init__( self : Dict , lowercase_ : Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001 , lowercase_ : float = 0.9 , lowercase_ : float = 0.999 , lowercase_ : float = 1e-7 , lowercase_ : bool = False , lowercase_ : float = 0.0 , lowercase_ : Optional[List[str]] = None , lowercase_ : Optional[List[str]] = None , lowercase_ : str = "AdamWeightDecay" , **lowercase_ : Optional[int] , ):
super().__init__(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
snake_case_ = weight_decay_rate
snake_case_ = include_in_weight_decay
snake_case_ = exclude_from_weight_decay
@classmethod
def A_ ( cls : Dict , lowercase_ : Union[str, Any] ):
snake_case_ = {'''WarmUp''': WarmUp}
return super(lowercase_ , cls ).from_config(lowercase_ , custom_objects=lowercase_ )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : Optional[int] ):
super(lowercase_ , self )._prepare_local(lowercase_ , lowercase_ , lowercase_ )
snake_case_ = tf.constant(
self.weight_decay_rate , name='''adam_weight_decay_rate''' )
def A_ ( self : str , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Any ):
snake_case_ = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] , use_locking=self._use_locking , )
return tf.no_op()
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : str=None , **lowercase_ : List[str] ):
snake_case_ ,snake_case_ = list(zip(*lowercase_ ) )
return super(lowercase_ , self ).apply_gradients(zip(lowercase_ , lowercase_ ) , name=lowercase_ , **lowercase_ )
def A_ ( self : List[Any] , lowercase_ : str , lowercase_ : str , lowercase_ : Any ):
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
snake_case_ = apply_state or {}
snake_case_ = apply_state.get((var_device, var_dtype) )
if coefficients is None:
snake_case_ = self._fallback_apply_state(lowercase_ , lowercase_ )
snake_case_ = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def A_ ( self : Union[str, Any] , lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : Optional[int]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_dense(lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ : int , lowercase_ : str , lowercase_ : List[Any]=None ):
snake_case_ ,snake_case_ = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
snake_case_ = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_sparse(lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = super().get_config()
config.update({'''weight_decay_rate''': self.weight_decay_rate} )
return config
def A_ ( self : Optional[int] , lowercase_ : int ):
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return False
return True
class a ( _lowerCamelCase ):
def __init__( self : List[Any] ):
snake_case_ = []
snake_case_ = None
@property
def A_ ( self : Union[str, Any] ):
if self._accum_steps is None:
snake_case_ = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def A_ ( self : Dict ):
if not self._gradients:
raise ValueError('''The accumulator should be called first to initialize the gradients''' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self : Any , lowercase_ : int ):
if not self._gradients:
snake_case_ = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(lowercase_ ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(lowercase_ ) != len(self._gradients ):
raise ValueError(F"Expected {len(self._gradients )} gradients, but got {len(lowercase_ )}" )
for accum_gradient, gradient in zip(self._gradients , lowercase_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(lowercase_ )
self._accum_steps.assign_add(1 )
def A_ ( self : Optional[int] ):
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(lowercase_ ) )
| 56
| 0
|
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
UpperCAmelCase_ = (720, 1280) # Height, Width
UpperCAmelCase_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
UpperCAmelCase_ = 1 / 100
UpperCAmelCase_ = ''
UpperCAmelCase_ = ''
UpperCAmelCase_ = ''
UpperCAmelCase_ = 250
def lowerCAmelCase_ ( ) -> None:
UpperCamelCase__ ,UpperCamelCase__ : Any = get_dataset(__UpperCAmelCase , __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
UpperCamelCase__ : Dict = random.sample(range(len(__UpperCAmelCase ) ) , 4 )
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Tuple = update_image_and_anno(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , filter_scale=__UpperCAmelCase , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
UpperCamelCase__ : Tuple = random_chars(32 )
UpperCamelCase__ : List[Any] = path.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
UpperCamelCase__ : Union[str, Any] = f"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(f"{file_root}.jpg" , __UpperCAmelCase , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
UpperCamelCase__ : Union[str, Any] = []
for anno in new_annos:
UpperCamelCase__ : Dict = anno[3] - anno[1]
UpperCamelCase__ : str = anno[4] - anno[2]
UpperCamelCase__ : str = anno[1] + width / 2
UpperCamelCase__ : Union[str, Any] = anno[2] + height / 2
UpperCamelCase__ : Optional[Any] = f"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(f"{file_root}.txt" , '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: List[Any] ) -> tuple[list, list]:
UpperCamelCase__ : Dict = []
UpperCamelCase__ : str = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase , '''*.txt''' ) ):
UpperCamelCase__ : Optional[Any] = label_file.split(os.sep )[-1].rsplit('''.''' , 1 )[0]
with open(__UpperCAmelCase ) as in_file:
UpperCamelCase__ : Optional[int] = in_file.readlines()
UpperCamelCase__ : int = os.path.join(__UpperCAmelCase , f"{label_name}.jpg" )
UpperCamelCase__ : Union[str, Any] = []
for obj_list in obj_lists:
UpperCamelCase__ : Optional[int] = obj_list.rstrip('''\n''' ).split(''' ''' )
UpperCamelCase__ : List[str] = float(obj[1] ) - float(obj[3] ) / 2
UpperCamelCase__ : str = float(obj[2] ) - float(obj[4] ) / 2
UpperCamelCase__ : Union[str, Any] = float(obj[1] ) + float(obj[3] ) / 2
UpperCamelCase__ : Optional[Any] = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: Optional[int] , __UpperCAmelCase: str , __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: int , __UpperCAmelCase: str = 0.0 , ) -> tuple[list, list, str]:
UpperCamelCase__ : Tuple = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
UpperCamelCase__ : int = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
UpperCamelCase__ : Dict = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
UpperCamelCase__ : str = int(scale_x * output_size[1] )
UpperCamelCase__ : List[str] = int(scale_y * output_size[0] )
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : Optional[int] = []
for i, index in enumerate(__UpperCAmelCase ):
UpperCamelCase__ : Tuple = all_img_list[index]
path_list.append(__UpperCAmelCase )
UpperCamelCase__ : List[str] = all_annos[index]
UpperCamelCase__ : Any = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
UpperCamelCase__ : Union[str, Any] = cva.resize(__UpperCAmelCase , (divid_point_x, divid_point_y) )
UpperCamelCase__ : Optional[int] = img
for bbox in img_annos:
UpperCamelCase__ : Optional[Any] = bbox[1] * scale_x
UpperCamelCase__ : Any = bbox[2] * scale_y
UpperCamelCase__ : Optional[Any] = bbox[3] * scale_x
UpperCamelCase__ : List[Any] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
UpperCamelCase__ : Any = cva.resize(__UpperCAmelCase , (output_size[1] - divid_point_x, divid_point_y) )
UpperCamelCase__ : List[str] = img
for bbox in img_annos:
UpperCamelCase__ : List[Any] = scale_x + bbox[1] * (1 - scale_x)
UpperCamelCase__ : List[str] = bbox[2] * scale_y
UpperCamelCase__ : Any = scale_x + bbox[3] * (1 - scale_x)
UpperCamelCase__ : str = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
UpperCamelCase__ : Optional[Any] = cva.resize(__UpperCAmelCase , (divid_point_x, output_size[0] - divid_point_y) )
UpperCamelCase__ : str = img
for bbox in img_annos:
UpperCamelCase__ : List[Any] = bbox[1] * scale_x
UpperCamelCase__ : List[str] = scale_y + bbox[2] * (1 - scale_y)
UpperCamelCase__ : List[Any] = bbox[3] * scale_x
UpperCamelCase__ : List[str] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
UpperCamelCase__ : Tuple = cva.resize(
__UpperCAmelCase , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
UpperCamelCase__ : Optional[int] = img
for bbox in img_annos:
UpperCamelCase__ : Any = scale_x + bbox[1] * (1 - scale_x)
UpperCamelCase__ : Any = scale_y + bbox[2] * (1 - scale_y)
UpperCamelCase__ : Optional[Any] = scale_x + bbox[3] * (1 - scale_x)
UpperCamelCase__ : Union[str, Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
UpperCamelCase__ : List[str] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> str:
assert number_char > 1, "The number of character should greater than 1"
UpperCamelCase__ : Any = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 201
|
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = "sample"
snake_case_ = 1e-2
@property
def A_ ( self : Dict ):
snake_case_ = 4
snake_case_ = 3
snake_case_ = (32, 32)
snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(lowercase_ )
return {"sample": image}
@property
def A_ ( self : List[Any] ):
return (3, 32, 32)
@property
def A_ ( self : Dict ):
return (3, 32, 32)
def A_ ( self : Union[str, Any] ):
snake_case_ = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
snake_case_ = self.dummy_input
return init_dict, inputs_dict
def A_ ( self : Any ):
pass
def A_ ( self : str ):
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def A_ ( self : Dict ):
# enable deterministic behavior for gradient checkpointing
snake_case_ ,snake_case_ = self.prepare_init_args_and_inputs_for_common()
snake_case_ = self.model_class(**lowercase_ )
model.to(lowercase_ )
assert not model.is_gradient_checkpointing and model.training
snake_case_ = model(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
snake_case_ = torch.randn_like(lowercase_ )
snake_case_ = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
snake_case_ = self.model_class(**lowercase_ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(lowercase_ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
snake_case_ = model_a(**lowercase_ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
snake_case_ = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
snake_case_ = dict(model.named_parameters() )
snake_case_ = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5e-5 ) )
def A_ ( self : Tuple ):
snake_case_ ,snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=lowercase_ )
self.assertIsNotNone(lowercase_ )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(lowercase_ )
snake_case_ = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def A_ ( self : Tuple ):
snake_case_ = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
snake_case_ = model.to(lowercase_ )
model.eval()
if torch_device == "mps":
snake_case_ = torch.manual_seed(0 )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(0 )
snake_case_ = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
snake_case_ = image.to(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , sample_posterior=lowercase_ , generator=lowercase_ ).sample
snake_case_ = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
snake_case_ = torch.tensor(
[
-4.0_078e-01,
-3.8_323e-04,
-1.2_681e-01,
-1.1_462e-01,
2.0_095e-01,
1.0_893e-01,
-8.8_247e-02,
-3.0_361e-01,
-9.8_644e-03,
] )
elif torch_device == "cpu":
snake_case_ = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
snake_case_ = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(lowercase_ , lowercase_ , rtol=1e-2 ) )
@slow
class a ( unittest.TestCase ):
def A_ ( self : Union[str, Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] ):
return F"gaussian_noise_s={seed}_shape={'_'.join([str(lowercase_ ) for s in shape] )}.npy"
def A_ ( self : Any ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Dict , lowercase_ : List[Any]=0 , lowercase_ : Union[str, Any]=(4, 3, 512, 512) , lowercase_ : Optional[Any]=False ):
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = torch.from_numpy(load_hf_numpy(self.get_file_format(lowercase_ , lowercase_ ) ) ).to(lowercase_ ).to(lowercase_ )
return image
def A_ ( self : Any , lowercase_ : Dict="CompVis/stable-diffusion-v1-4" , lowercase_ : List[str]=False ):
snake_case_ = '''fp16''' if fpaa else None
snake_case_ = torch.floataa if fpaa else torch.floataa
snake_case_ = AutoencoderKL.from_pretrained(
lowercase_ , subfolder='''vae''' , torch_dtype=lowercase_ , revision=lowercase_ , )
model.to(lowercase_ ).eval()
return model
def A_ ( self : Any , lowercase_ : int=0 ):
if torch_device == "mps":
return torch.manual_seed(lowercase_ )
return torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Optional[int] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , fpaa=lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ , generator=lowercase_ , sample_posterior=lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def A_ ( self : Tuple , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
with torch.no_grad():
snake_case_ = model(lowercase_ ).sample
assert sample.shape == image.shape
snake_case_ = sample[-1, -2:, -2:, :2].flatten().float().cpu()
snake_case_ = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(lowercase_ , lowercase_ , atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Dict , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def A_ ( self : Union[str, Any] , lowercase_ : Any , lowercase_ : Optional[Any] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
snake_case_ = sample[-1, -2:, :2, -2:].flatten().float().cpu()
snake_case_ = torch.tensor(lowercase_ )
assert torch_all_close(lowercase_ , lowercase_ , atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : List[str] ):
snake_case_ = self.get_sd_vae_model(fpaa=lowercase_ )
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) , fpaa=lowercase_ )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def A_ ( self : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ , shape=(3, 4, 64, 64) )
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
snake_case_ = model.decode(lowercase_ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(lowercase_ , lowercase_ , atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def A_ ( self : str , lowercase_ : Optional[int] , lowercase_ : Tuple ):
snake_case_ = self.get_sd_vae_model()
snake_case_ = self.get_sd_image(lowercase_ )
snake_case_ = self.get_generator(lowercase_ )
with torch.no_grad():
snake_case_ = model.encode(lowercase_ ).latent_dist
snake_case_ = dist.sample(generator=lowercase_ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
snake_case_ = sample[0, -1, -3:, -3:].flatten().cpu()
snake_case_ = torch.tensor(lowercase_ )
snake_case_ = 3e-3 if torch_device != '''mps''' else 1e-2
assert torch_all_close(lowercase_ , lowercase_ , atol=lowercase_ )
| 56
| 0
|
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
__lowerCamelCase = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("""""", """|""", """|"""),
datarow=DataRow("""""", """|""", """|"""),
padding=1,
with_header_hide=None,
)
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = {'type': 'section', 'text': {'type': 'plain_text', 'text': 'No failed tests! 🤗', 'emoji': True}}
__lowerCamelCase = [
{
'type': 'header',
'text': {
'type': 'plain_text',
'text': F'🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results',
'emoji': True,
},
}
]
__lowerCamelCase = 0
for log in Path().glob("""*.log"""):
__lowerCamelCase = 0
with open(log, """r""") as f:
for line in f:
__lowerCamelCase = json.loads(line)
if line.get("""nodeid""", """""") != "":
__lowerCamelCase = line['nodeid']
if line.get("""duration""", None) is not None:
__lowerCamelCase = F'{line["duration"]:.4f}'
if line.get("""outcome""", """""") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("""_""")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
__lowerCamelCase = []
log.unlink()
__lowerCamelCase = ''
__lowerCamelCase = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
__lowerCamelCase = []
__lowerCamelCase = {}
for test in failed_tests:
__lowerCamelCase = test[0].split("""::""")
__lowerCamelCase = data[0].split("""/""")[-1]
if data[0] not in filesafailed:
__lowerCamelCase = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
__lowerCamelCase = [test[0] for test in failed_table]
__lowerCamelCase = list(set(files))
# Count number of instances in failed_tests
__lowerCamelCase = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
__lowerCamelCase = tabulate(
table,
headers=["""Test Location""", """Num Failed"""],
tablefmt=hf_table_format,
stralign="""right""",
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 30_00:
__lowerCamelCase = 'Too many failed tests, please see the full report in the Action results.'
__lowerCamelCase = len(err) + 10
__lowerCamelCase = message[: 30_00 - offset] + F'\n...\n```\n{err}'
print(F'### {message}')
else:
__lowerCamelCase = 'No failed tests! 🤗'
print(F'## {message}')
payload.append(no_error_payload)
if os.environ.get("""TEST_TYPE""", """""") != "":
from slack_sdk import WebClient
__lowerCamelCase = WebClient(token=os.environ["""SLACK_API_TOKEN"""])
if message != "No failed tests! 🤗":
__lowerCamelCase = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': message,
},
}
payload.append(md_report)
__lowerCamelCase = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': '*For more details:*',
},
'accessory': {
'type': 'button',
'text': {
'type': 'plain_text',
'text': 'Check Action results',
'emoji': True,
},
'url': F'https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
payload.append(action_button)
__lowerCamelCase = {
'type': 'context',
'elements': [
{
'type': 'plain_text',
'text': F'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
}
],
}
payload.append(date_report)
__lowerCamelCase = client.chat_postMessage(channel="""#accelerate-ci-daily""", text=message, blocks=payload)
__lowerCamelCase = response.data['ts']
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
__lowerCamelCase = ''
for i, row in enumerate(test_failures):
if row[0] != test_class:
__lowerCamelCase = row[0]
else:
__lowerCamelCase = ''
__lowerCamelCase = {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': F'Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```',
},
}
client.chat_postMessage(
channel="""#accelerate-ci-daily""",
thread_ts=ts,
blocks=[payload],
)
| 59
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class a ( _lowerCamelCase ):
snake_case_ = 42
@flax_register_to_config
class a ( nn.Module , _lowerCamelCase , _lowerCamelCase ):
snake_case_ = 32
snake_case_ = 4
snake_case_ = 4
snake_case_ = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
snake_case_ = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
snake_case_ = False
snake_case_ = (320, 640, 1_280, 1_280)
snake_case_ = 2
snake_case_ = 8
snake_case_ = None
snake_case_ = 1_280
snake_case_ = 0.0
snake_case_ = False
snake_case_ = jnp.floataa
snake_case_ = True
snake_case_ = 0
snake_case_ = False
def A_ ( self : Optional[int] , lowercase_ : jax.random.KeyArray ):
# init input tensors
snake_case_ = (1, self.in_channels, self.sample_size, self.sample_size)
snake_case_ = jnp.zeros(lowercase_ , dtype=jnp.floataa )
snake_case_ = jnp.ones((1,) , dtype=jnp.intaa )
snake_case_ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
snake_case_ ,snake_case_ = jax.random.split(lowercase_ )
snake_case_ = {'''params''': params_rng, '''dropout''': dropout_rng}
return self.init(lowercase_ , lowercase_ , lowercase_ , lowercase_ )["params"]
def A_ ( self : List[str] ):
snake_case_ = self.block_out_channels
snake_case_ = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
'''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.''' )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
snake_case_ = self.num_attention_heads or self.attention_head_dim
# input
snake_case_ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
snake_case_ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
snake_case_ = FlaxTimestepEmbedding(lowercase_ , dtype=self.dtype )
snake_case_ = self.only_cross_attention
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = (num_attention_heads,) * len(self.down_block_types )
# down
snake_case_ = []
snake_case_ = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
snake_case_ = output_channel
snake_case_ = block_out_channels[i]
snake_case_ = i == len(lowercase_ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
snake_case_ = FlaxCrossAttnDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxDownBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(lowercase_ )
snake_case_ = down_blocks
# mid
snake_case_ = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
snake_case_ = []
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = list(reversed(lowercase_ ) )
snake_case_ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
snake_case_ = output_channel
snake_case_ = reversed_block_out_channels[i]
snake_case_ = reversed_block_out_channels[min(i + 1 , len(lowercase_ ) - 1 )]
snake_case_ = i == len(lowercase_ ) - 1
if up_block_type == "CrossAttnUpBlock2D":
snake_case_ = FlaxCrossAttnUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
snake_case_ = FlaxUpBlockaD(
in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(lowercase_ )
snake_case_ = output_channel
snake_case_ = up_blocks
# out
snake_case_ = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
snake_case_ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : int , lowercase_ : Any , lowercase_ : int=None , lowercase_ : Any=None , lowercase_ : bool = True , lowercase_ : bool = False , ):
# 1. time
if not isinstance(lowercase_ , jnp.ndarray ):
snake_case_ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(lowercase_ , jnp.ndarray ) and len(timesteps.shape ) == 0:
snake_case_ = timesteps.astype(dtype=jnp.floataa )
snake_case_ = jnp.expand_dims(lowercase_ , 0 )
snake_case_ = self.time_proj(lowercase_ )
snake_case_ = self.time_embedding(lowercase_ )
# 2. pre-process
snake_case_ = jnp.transpose(lowercase_ , (0, 2, 3, 1) )
snake_case_ = self.conv_in(lowercase_ )
# 3. down
snake_case_ = (sample,)
for down_block in self.down_blocks:
if isinstance(lowercase_ , lowercase_ ):
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
else:
snake_case_ ,snake_case_ = down_block(lowercase_ , lowercase_ , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
snake_case_ = ()
for down_block_res_sample, down_block_additional_residual in zip(
lowercase_ , lowercase_ ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
snake_case_ = new_down_block_res_samples
# 4. mid
snake_case_ = self.mid_block(lowercase_ , lowercase_ , lowercase_ , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
snake_case_ = down_block_res_samples[-(self.layers_per_block + 1) :]
snake_case_ = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(lowercase_ , lowercase_ ):
snake_case_ = up_block(
lowercase_ , temb=lowercase_ , encoder_hidden_states=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train , )
else:
snake_case_ = up_block(lowercase_ , temb=lowercase_ , res_hidden_states_tuple=lowercase_ , deterministic=not train )
# 6. post-process
snake_case_ = self.conv_norm_out(lowercase_ )
snake_case_ = nn.silu(lowercase_ )
snake_case_ = self.conv_out(lowercase_ )
snake_case_ = jnp.transpose(lowercase_ , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=lowercase_ )
| 56
| 0
|
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class UpperCAmelCase_ ( _lowerCamelCase ):
'''simple docstring'''
__A : Tuple = 42
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __A=3 , __A=3 , __A=("DownEncoderBlock2D",) , __A=(64,) , __A=2 , __A=32 , __A="silu" , __A=True , ):
"""simple docstring"""
super().__init__()
lowerCamelCase : str = layers_per_block
lowerCamelCase : List[Any] = torch.nn.Convad(
lowercase_ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
lowerCamelCase : int = None
lowerCamelCase : List[str] = nn.ModuleList([] )
# down
lowerCamelCase : str = block_out_channels[0]
for i, down_block_type in enumerate(lowercase_ ):
lowerCamelCase : Tuple = output_channel
lowerCamelCase : Union[str, Any] = block_out_channels[i]
lowerCamelCase : Optional[Any] = i == len(lowercase_ ) - 1
lowerCamelCase : Dict = get_down_block(
lowercase_ , num_layers=self.layers_per_block , in_channels=lowercase_ , out_channels=lowercase_ , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=lowercase_ , resnet_groups=lowercase_ , attention_head_dim=lowercase_ , temb_channels=lowercase_ , )
self.down_blocks.append(lowercase_ )
# mid
lowerCamelCase : Optional[int] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=lowercase_ , output_scale_factor=1 , resnet_time_scale_shift="default" , attention_head_dim=block_out_channels[-1] , resnet_groups=lowercase_ , temb_channels=lowercase_ , )
# out
lowerCamelCase : Dict = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=lowercase_ , eps=1e-6 )
lowerCamelCase : List[Any] = nn.SiLU()
lowerCamelCase : List[Any] = 2 * out_channels if double_z else out_channels
lowerCamelCase : Optional[Any] = nn.Convad(block_out_channels[-1] , lowercase_ , 3 , padding=1 )
lowerCamelCase : List[str] = False
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : str = x
lowerCamelCase : Union[str, Any] = self.conv_in(lowercase_ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__A ):
def custom_forward(*__A ):
return module(*lowercase_ )
return custom_forward
# down
if is_torch_version(">=" , "1.11.0" ):
for down_block in self.down_blocks:
lowerCamelCase : int = torch.utils.checkpoint.checkpoint(
create_custom_forward(lowercase_ ) , lowercase_ , use_reentrant=lowercase_ )
# middle
lowerCamelCase : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowercase_ , use_reentrant=lowercase_ )
else:
for down_block in self.down_blocks:
lowerCamelCase : Dict = torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase_ ) , lowercase_ )
# middle
lowerCamelCase : Optional[int] = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , lowercase_ )
else:
# down
for down_block in self.down_blocks:
lowerCamelCase : Optional[int] = down_block(lowercase_ )
# middle
lowerCamelCase : Any = self.mid_block(lowercase_ )
# post-process
lowerCamelCase : str = self.conv_norm_out(lowercase_ )
lowerCamelCase : Optional[Any] = self.conv_act(lowercase_ )
lowerCamelCase : List[Any] = self.conv_out(lowercase_ )
return sample
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __A=3 , __A=3 , __A=("UpDecoderBlock2D",) , __A=(64,) , __A=2 , __A=32 , __A="silu" , __A="group" , ):
"""simple docstring"""
super().__init__()
lowerCamelCase : Union[str, Any] = layers_per_block
lowerCamelCase : str = nn.Convad(
lowercase_ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
lowerCamelCase : Union[str, Any] = None
lowerCamelCase : Any = nn.ModuleList([] )
lowerCamelCase : Optional[Any] = in_channels if norm_type == "spatial" else None
# mid
lowerCamelCase : List[Any] = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=lowercase_ , output_scale_factor=1 , resnet_time_scale_shift="default" if norm_type == "group" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=lowercase_ , temb_channels=lowercase_ , )
# up
lowerCamelCase : Union[str, Any] = list(reversed(lowercase_ ) )
lowerCamelCase : Optional[Any] = reversed_block_out_channels[0]
for i, up_block_type in enumerate(lowercase_ ):
lowerCamelCase : Optional[Any] = output_channel
lowerCamelCase : List[str] = reversed_block_out_channels[i]
lowerCamelCase : Optional[int] = i == len(lowercase_ ) - 1
lowerCamelCase : Dict = get_up_block(
lowercase_ , num_layers=self.layers_per_block + 1 , in_channels=lowercase_ , out_channels=lowercase_ , prev_output_channel=lowercase_ , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=lowercase_ , resnet_groups=lowercase_ , attention_head_dim=lowercase_ , temb_channels=lowercase_ , resnet_time_scale_shift=lowercase_ , )
self.up_blocks.append(lowercase_ )
lowerCamelCase : List[Any] = output_channel
# out
if norm_type == "spatial":
lowerCamelCase : Optional[int] = SpatialNorm(block_out_channels[0] , lowercase_ )
else:
lowerCamelCase : Dict = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=lowercase_ , eps=1e-6 )
lowerCamelCase : Any = nn.SiLU()
lowerCamelCase : Union[str, Any] = nn.Convad(block_out_channels[0] , lowercase_ , 3 , padding=1 )
lowerCamelCase : Optional[Any] = False
def _snake_case ( self , __A , __A=None ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = z
lowerCamelCase : Optional[Any] = self.conv_in(lowercase_ )
lowerCamelCase : Optional[Any] = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__A ):
def custom_forward(*__A ):
return module(*lowercase_ )
return custom_forward
if is_torch_version(">=" , "1.11.0" ):
# middle
lowerCamelCase : int = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowercase_ , lowercase_ , use_reentrant=lowercase_ )
lowerCamelCase : Optional[Any] = sample.to(lowercase_ )
# up
for up_block in self.up_blocks:
lowerCamelCase : str = torch.utils.checkpoint.checkpoint(
create_custom_forward(lowercase_ ) , lowercase_ , lowercase_ , use_reentrant=lowercase_ )
else:
# middle
lowerCamelCase : Optional[Any] = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , lowercase_ , lowercase_ )
lowerCamelCase : Optional[int] = sample.to(lowercase_ )
# up
for up_block in self.up_blocks:
lowerCamelCase : int = torch.utils.checkpoint.checkpoint(create_custom_forward(lowercase_ ) , lowercase_ , lowercase_ )
else:
# middle
lowerCamelCase : str = self.mid_block(lowercase_ , lowercase_ )
lowerCamelCase : str = sample.to(lowercase_ )
# up
for up_block in self.up_blocks:
lowerCamelCase : Any = up_block(lowercase_ , lowercase_ )
# post-process
if latent_embeds is None:
lowerCamelCase : Optional[Any] = self.conv_norm_out(lowercase_ )
else:
lowerCamelCase : Optional[Any] = self.conv_norm_out(lowercase_ , lowercase_ )
lowerCamelCase : str = self.conv_act(lowercase_ )
lowerCamelCase : List[Any] = self.conv_out(lowercase_ )
return sample
class UpperCAmelCase_ ( nn.Module ):
'''simple docstring'''
def __init__( self , __A , __A , __A , __A=None , __A="random" , __A=False , __A=True ):
"""simple docstring"""
super().__init__()
lowerCamelCase : Any = n_e
lowerCamelCase : int = vq_embed_dim
lowerCamelCase : Tuple = beta
lowerCamelCase : List[str] = legacy
lowerCamelCase : List[Any] = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
lowerCamelCase : Optional[Any] = remap
if self.remap is not None:
self.register_buffer("used" , torch.tensor(np.load(self.remap ) ) )
lowerCamelCase : Dict = self.used.shape[0]
lowerCamelCase : List[str] = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
lowerCamelCase : List[str] = self.re_embed
lowerCamelCase : Tuple = self.re_embed + 1
print(
F"""Remapping {self.n_e} indices to {self.re_embed} indices. """
F"""Using {self.unknown_index} for unknown indices.""" )
else:
lowerCamelCase : Any = n_e
lowerCamelCase : Optional[int] = sane_index_shape
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = inds.shape
assert len(lowercase_ ) > 1
lowerCamelCase : Dict = inds.reshape(ishape[0] , -1 )
lowerCamelCase : Tuple = self.used.to(lowercase_ )
lowerCamelCase : Union[str, Any] = (inds[:, :, None] == used[None, None, ...]).long()
lowerCamelCase : Tuple = match.argmax(-1 )
lowerCamelCase : Any = match.sum(2 ) < 1
if self.unknown_index == "random":
lowerCamelCase : Dict = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
lowerCamelCase : Tuple = self.unknown_index
return new.reshape(lowercase_ )
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : int = inds.shape
assert len(lowercase_ ) > 1
lowerCamelCase : int = inds.reshape(ishape[0] , -1 )
lowerCamelCase : int = self.used.to(lowercase_ )
if self.re_embed > self.used.shape[0]: # extra token
lowerCamelCase : Any = 0 # simply set to zero
lowerCamelCase : Dict = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , lowercase_ )
return back.reshape(lowercase_ )
def _snake_case ( self , __A ):
"""simple docstring"""
lowerCamelCase : List[str] = z.permute(0 , 2 , 3 , 1 ).contiguous()
lowerCamelCase : Tuple = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
lowerCamelCase : int = torch.argmin(torch.cdist(lowercase_ , self.embedding.weight ) , dim=1 )
lowerCamelCase : Any = self.embedding(lowercase_ ).view(z.shape )
lowerCamelCase : List[Any] = None
lowerCamelCase : Any = None
# compute loss for embedding
if not self.legacy:
lowerCamelCase : Tuple = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
lowerCamelCase : Tuple = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
lowerCamelCase : List[str] = z + (z_q - z).detach()
# reshape back to match original input shape
lowerCamelCase : Optional[int] = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
lowerCamelCase : Any = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
lowerCamelCase : Union[str, Any] = self.remap_to_used(lowercase_ )
lowerCamelCase : Any = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
lowerCamelCase : List[Any] = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def _snake_case ( self , __A , __A ):
"""simple docstring"""
if self.remap is not None:
lowerCamelCase : List[Any] = indices.reshape(shape[0] , -1 ) # add batch axis
lowerCamelCase : Any = self.unmap_to_all(lowercase_ )
lowerCamelCase : Union[str, Any] = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
lowerCamelCase : Optional[Any] = self.embedding(lowercase_ )
if shape is not None:
lowerCamelCase : Optional[int] = z_q.view(lowercase_ )
# reshape back to match original input shape
lowerCamelCase : str = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class UpperCAmelCase_ ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self , __A , __A=False ):
"""simple docstring"""
lowerCamelCase : str = parameters
lowerCamelCase , lowerCamelCase : int = torch.chunk(lowercase_ , 2 , dim=1 )
lowerCamelCase : str = torch.clamp(self.logvar , -30.0 , 20.0 )
lowerCamelCase : Optional[int] = deterministic
lowerCamelCase : Optional[Any] = torch.exp(0.5 * self.logvar )
lowerCamelCase : Optional[Any] = torch.exp(self.logvar )
if self.deterministic:
lowerCamelCase : int = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def _snake_case ( self , __A = None ):
"""simple docstring"""
lowerCamelCase : Optional[int] = randn_tensor(
self.mean.shape , generator=lowercase_ , device=self.parameters.device , dtype=self.parameters.dtype )
lowerCamelCase : Optional[Any] = self.mean + self.std * sample
return x
def _snake_case ( self , __A=None ):
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def _snake_case ( self , __A , __A=[1, 2, 3] ):
"""simple docstring"""
if self.deterministic:
return torch.Tensor([0.0] )
lowerCamelCase : List[Any] = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=lowercase_ )
def _snake_case ( self ):
"""simple docstring"""
return self.mean
| 283
|
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a : Dict = (720, 1280) # Height, Width
a : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a : Dict = 1 / 100
a : str = ''
a : Any = ''
a : Optional[int] = ''
a : List[str] = 250
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dataset(__UpperCAmelCase, __UpperCAmelCase )
for index in range(__UpperCAmelCase ):
snake_case_ = random.sample(range(len(__UpperCAmelCase ) ), 4 )
snake_case_ ,snake_case_ ,snake_case_ = update_image_and_anno(
__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, filter_scale=__UpperCAmelCase, )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ = random_chars(32 )
snake_case_ = path.split(os.sep )[-1].rsplit('''.''', 1 )[0]
snake_case_ = F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(F"{file_root}.jpg", __UpperCAmelCase, [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
snake_case_ = []
for anno in new_annos:
snake_case_ = anno[3] - anno[1]
snake_case_ = anno[4] - anno[2]
snake_case_ = anno[1] + width / 2
snake_case_ = anno[2] + height / 2
snake_case_ = F"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(__UpperCAmelCase )
with open(F"{file_root}.txt", '''w''' ) as outfile:
outfile.write('''\n'''.join(line for line in annos_list ) )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> tuple[list, list]:
'''simple docstring'''
snake_case_ = []
snake_case_ = []
for label_file in glob.glob(os.path.join(__UpperCAmelCase, '''*.txt''' ) ):
snake_case_ = label_file.split(os.sep )[-1].rsplit('''.''', 1 )[0]
with open(__UpperCAmelCase ) as in_file:
snake_case_ = in_file.readlines()
snake_case_ = os.path.join(__UpperCAmelCase, F"{label_name}.jpg" )
snake_case_ = []
for obj_list in obj_lists:
snake_case_ = obj_list.rstrip('''\n''' ).split(''' ''' )
snake_case_ = float(obj[1] ) - float(obj[3] ) / 2
snake_case_ = float(obj[2] ) - float(obj[4] ) / 2
snake_case_ = float(obj[1] ) + float(obj[3] ) / 2
snake_case_ = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__UpperCAmelCase )
labels.append(__UpperCAmelCase )
return img_paths, labels
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.0, ) -> tuple[list, list, str]:
'''simple docstring'''
snake_case_ = np.zeros([output_size[0], output_size[1], 3], dtype=np.uinta )
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ = int(scale_x * output_size[1] )
snake_case_ = int(scale_y * output_size[0] )
snake_case_ = []
snake_case_ = []
for i, index in enumerate(__UpperCAmelCase ):
snake_case_ = all_img_list[index]
path_list.append(__UpperCAmelCase )
snake_case_ = all_annos[index]
snake_case_ = cva.imread(__UpperCAmelCase )
if i == 0: # top-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = bbox[2] * scale_y
snake_case_ = bbox[3] * scale_x
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case_ = cva.resize(__UpperCAmelCase, (output_size[1] - divid_point_x, divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = bbox[2] * scale_y
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case_ = cva.resize(__UpperCAmelCase, (divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = bbox[1] * scale_x
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = bbox[3] * scale_x
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case_ = cva.resize(
__UpperCAmelCase, (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case_ = img
for bbox in img_annos:
snake_case_ = scale_x + bbox[1] * (1 - scale_x)
snake_case_ = scale_y + bbox[2] * (1 - scale_y)
snake_case_ = scale_x + bbox[3] * (1 - scale_x)
snake_case_ = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case_ = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case_ = ascii_lowercase + digits
return "".join(random.choice(__UpperCAmelCase ) for _ in range(__UpperCAmelCase ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 56
| 0
|
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class snake_case_ ( _lowerCamelCase ):
'''simple docstring'''
def snake_case__( self : str ) ->Optional[int]:
snake_case_ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowercase_ , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(lowercase_ , '''num_heads''' ) )
class snake_case_ :
'''simple docstring'''
def __init__( self : int , _UpperCamelCase : Any , _UpperCamelCase : Dict=1_3 , _UpperCamelCase : Optional[int]=6_4 , _UpperCamelCase : Optional[int]=3 , _UpperCamelCase : Union[str, Any]=[1_6, 4_8, 9_6] , _UpperCamelCase : List[str]=[1, 3, 6] , _UpperCamelCase : Optional[Any]=[1, 2, 1_0] , _UpperCamelCase : List[Any]=[7, 3, 3] , _UpperCamelCase : List[Any]=[4, 2, 2] , _UpperCamelCase : Union[str, Any]=[2, 1, 1] , _UpperCamelCase : Tuple=[2, 2, 2] , _UpperCamelCase : Union[str, Any]=[False, False, True] , _UpperCamelCase : str=[0.0, 0.0, 0.0] , _UpperCamelCase : Optional[Any]=0.02 , _UpperCamelCase : Optional[Any]=1e-12 , _UpperCamelCase : int=True , _UpperCamelCase : Union[str, Any]=True , _UpperCamelCase : Any=2 , ) ->List[str]:
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = patch_sizes
snake_case_ = patch_stride
snake_case_ = patch_padding
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = num_labels
snake_case_ = num_channels
snake_case_ = embed_dim
snake_case_ = num_heads
snake_case_ = stride_kv
snake_case_ = depth
snake_case_ = cls_token
snake_case_ = attention_drop_rate
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
def snake_case__( self : List[Any] ) ->int:
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
# create a random int32 tensor of given shape
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels
def snake_case__( self : Tuple ) ->Optional[Any]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def snake_case__( self : int , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any , _UpperCamelCase : Any ) ->List[str]:
snake_case_ = TFCvtModel(config=lowercase_ )
snake_case_ = model(lowercase_ , training=lowercase_ )
snake_case_ = (self.image_size, self.image_size)
snake_case_, snake_case_ = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
snake_case_ = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
snake_case_ = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def snake_case__( self : Any , _UpperCamelCase : Dict , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Optional[Any] ) ->Optional[Any]:
snake_case_ = self.num_labels
snake_case_ = TFCvtForImageClassification(lowercase_ )
snake_case_ = model(lowercase_ , labels=lowercase_ , training=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__( self : str ) ->List[str]:
snake_case_ = self.prepare_config_and_inputs()
snake_case_, snake_case_, snake_case_ = config_and_inputs
snake_case_ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class snake_case_ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE : List[Any] = (
{"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE : Tuple = False
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Optional[Any] = False
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : int = False
def snake_case__( self : Dict ) ->Optional[Any]:
snake_case_ = TFCvtModelTester(self )
snake_case_ = TFCvtConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=3_7 )
def snake_case__( self : List[Any] ) ->Tuple:
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason='''Cvt does not output attentions''' )
def snake_case__( self : Any ) ->Optional[int]:
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def snake_case__( self : Any ) ->List[Any]:
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def snake_case__( self : int ) ->List[str]:
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
def snake_case__( self : List[Any] ) ->List[Any]:
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def snake_case__( self : Union[str, Any] ) ->str:
super().test_keras_fit()
@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
def snake_case__( self : Any ) ->Optional[Any]:
snake_case_ = tf.keras.mixed_precision.Policy('''mixed_float16''' )
tf.keras.mixed_precision.set_global_policy(lowercase_ )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy('''float32''' )
def snake_case__( self : Tuple ) ->Tuple:
snake_case_, snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(lowercase_ )
snake_case_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase_ )
def snake_case__( self : List[str] ) ->Dict:
def check_hidden_states_output(_UpperCamelCase : Any , _UpperCamelCase : Tuple , _UpperCamelCase : int ):
snake_case_ = model_class(lowercase_ )
snake_case_ = model(**self._prepare_for_class(lowercase_ , lowercase_ ) )
snake_case_ = outputs.hidden_states
snake_case_ = len(self.model_tester.depth )
self.assertEqual(len(lowercase_ ) , lowercase_ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
snake_case_, snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(lowercase_ , lowercase_ , lowercase_ )
def snake_case__( self : str ) ->int:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
def snake_case__( self : List[str] ) ->List[str]:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def snake_case__( self : Optional[int] ) ->Tuple:
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = TFCvtModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def __SCREAMING_SNAKE_CASE ():
snake_case_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def snake_case__( self : Any ) ->str:
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def snake_case__( self : Optional[Any] ) ->List[str]:
snake_case_ = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = image_processor(images=lowercase_ , return_tensors='''tf''' )
# forward pass
snake_case_ = model(**lowercase_ )
# verify the logits
snake_case_ = tf.TensorShape((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , lowercase_ )
snake_case_ = tf.constant([0.9285, 0.9015, -0.3150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , lowercase_ , atol=1e-4 ) )
| 8
|
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class a :
@staticmethod
def A_ ( *lowercase_ : int , **lowercase_ : str ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class a ( unittest.TestCase ):
snake_case_ = MODEL_FOR_OBJECT_DETECTION_MAPPING
def A_ ( self : Any , lowercase_ : List[Any] , lowercase_ : Optional[int] , lowercase_ : List[str] ):
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , image_processor=lowercase_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def A_ ( self : Dict , lowercase_ : List[Any] , lowercase_ : int ):
snake_case_ = object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' , threshold=0.0 )
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
import datasets
snake_case_ = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
snake_case_ = [
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
]
snake_case_ = object_detector(lowercase_ , threshold=0.0 )
self.assertEqual(len(lowercase_ ) , len(lowercase_ ) )
for outputs in batch_outputs:
self.assertGreater(len(lowercase_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
lowercase_ , {
'''score''': ANY(lowercase_ ),
'''label''': ANY(lowercase_ ),
'''box''': {'''xmin''': ANY(lowercase_ ), '''ymin''': ANY(lowercase_ ), '''xmax''': ANY(lowercase_ ), '''ymax''': ANY(lowercase_ )},
} , )
@require_tf
@unittest.skip('''Object detection not implemented in TF''' )
def A_ ( self : int ):
pass
@require_torch
def A_ ( self : Tuple ):
snake_case_ = '''hf-internal-testing/tiny-detr-mobilenetsv3'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=0.0 )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
[
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
{'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
],
] , )
@require_torch
@slow
def A_ ( self : Optional[int] ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = AutoModelForObjectDetection.from_pretrained(lowercase_ )
snake_case_ = AutoFeatureExtractor.from_pretrained(lowercase_ )
snake_case_ = ObjectDetectionPipeline(model=lowercase_ , feature_extractor=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : Tuple ):
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
snake_case_ = object_detector(
[
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
] )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
[
{'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
{'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
{'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
],
] , )
@require_torch
@slow
def A_ ( self : str ):
snake_case_ = 0.9985
snake_case_ = '''facebook/detr-resnet-50'''
snake_case_ = pipeline('''object-detection''' , model=lowercase_ )
snake_case_ = object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' , threshold=lowercase_ )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
{'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
] , )
@require_torch
@require_pytesseract
@slow
def A_ ( self : Dict ):
snake_case_ = '''Narsil/layoutlmv3-finetuned-funsd'''
snake_case_ = 0.9993
snake_case_ = pipeline('''object-detection''' , model=lowercase_ , threshold=lowercase_ )
snake_case_ = object_detector(
'''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png''' )
self.assertEqual(
nested_simplify(lowercase_ , decimals=4 ) , [
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
{'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
] , )
| 56
| 0
|
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class lowercase :
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=False , _a=True , _a=99 , _a=64 , _a=5 , _a=4 , _a=64 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=3 , _a=4 , _a=None , ) -> Optional[Any]:
_A : Optional[int] = parent
_A : Any = batch_size
_A : str = seq_length
_A : Optional[int] = is_training
_A : Tuple = use_input_mask
_A : List[str] = use_token_type_ids
_A : Optional[int] = use_labels
_A : int = vocab_size
_A : Dict = hidden_size
_A : Tuple = num_hidden_layers
_A : Union[str, Any] = num_attention_heads
_A : Union[str, Any] = intermediate_size
_A : str = hidden_act
_A : Optional[Any] = hidden_dropout_prob
_A : Optional[Any] = attention_probs_dropout_prob
_A : Optional[Any] = max_position_embeddings
_A : Dict = type_vocab_size
_A : Tuple = type_sequence_label_size
_A : int = initializer_range
_A : Union[str, Any] = num_labels
_A : Optional[Any] = num_choices
_A : int = scope
def a__ ( self ) -> Optional[int]:
return MPNetConfig.from_pretrained("""microsoft/mpnet-base""" )
def a__ ( self ) -> Any:
_A : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_A : List[Any] = None
if self.use_input_mask:
_A : Any = random_attention_mask([self.batch_size, self.seq_length] )
_A : Dict = None
_A : Union[str, Any] = None
_A : int = None
if self.use_labels:
_A : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_A : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_A : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
_A : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ ( self ) -> str:
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def a__ ( self , _a , _a , _a , _a , _a , _a ) -> Union[str, Any]:
_A : List[Any] = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
_A : Dict = model(lowercase_ , lowercase_ )
_A : Optional[Any] = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def a__ ( self , _a , _a , _a , _a , _a , _a ) -> Optional[int]:
_A : int = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
_A : Optional[Any] = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 , _a , _a , _a , _a , _a , _a ) -> int:
_A : str = self.num_labels
_A : Optional[Any] = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
_A : Tuple = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a__ ( self , _a , _a , _a , _a , _a , _a ) -> Optional[Any]:
_A : Any = self.num_choices
_A : str = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
_A : int = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A : str = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_A : Optional[int] = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a__ ( self , _a , _a , _a , _a , _a , _a ) -> Any:
_A : int = self.num_labels
_A : str = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
_A : List[Any] = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a__ ( self ) -> Optional[int]:
_A : Dict = self.prepare_config_and_inputs()
((_A) , (_A) , (_A) , (_A) , (_A) , (_A)) : Optional[Any] = config_and_inputs
_A : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class lowercase ( _lowerCamelCase,_lowerCamelCase,unittest.TestCase ):
_a = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
_a = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
_a = False
_a = True
def a__ ( self ) -> List[str]:
_A : str = MPNetModelTester(self )
_A : Any = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def a__ ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def a__ ( self ) -> Dict:
_A : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def a__ ( self ) -> Optional[int]:
_A : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def a__ ( self ) -> Union[str, Any]:
_A : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def a__ ( self ) -> Dict:
_A : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def a__ ( self ) -> int:
_A : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class lowercase ( unittest.TestCase ):
@slow
def a__ ( self ) -> Union[str, Any]:
_A : Optional[int] = MPNetModel.from_pretrained("""microsoft/mpnet-base""" )
_A : Optional[Any] = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
_A : Optional[int] = model(lowercase_ )[0]
_A : Optional[int] = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowercase_ )
_A : str = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1e-4 ) )
| 26
|
'''simple docstring'''
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class a :
def __init__( self : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Any=13 , lowercase_ : Optional[Any]=7 , lowercase_ : Optional[Any]=True , lowercase_ : Dict=True , lowercase_ : Tuple=False , lowercase_ : Optional[Any]=True , lowercase_ : Any=99 , lowercase_ : Union[str, Any]=64 , lowercase_ : str=5 , lowercase_ : int=4 , lowercase_ : List[Any]=64 , lowercase_ : Dict="gelu" , lowercase_ : Optional[int]=0.1 , lowercase_ : Optional[Any]=0.1 , lowercase_ : Tuple=512 , lowercase_ : List[Any]=16 , lowercase_ : str=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[Any]=3 , lowercase_ : Optional[Any]=4 , lowercase_ : List[Any]=None , ):
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
snake_case_ = use_input_mask
snake_case_ = use_token_type_ids
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_act
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = type_sequence_label_size
snake_case_ = initializer_range
snake_case_ = num_labels
snake_case_ = num_choices
snake_case_ = scope
def A_ ( self : List[str] ):
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def A_ ( self : str ):
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_ = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A_ ( self : Tuple ):
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def A_ ( self : Any , lowercase_ : Dict , lowercase_ : str , lowercase_ : Optional[Any] , lowercase_ : Union[str, Any] , lowercase_ : Dict , lowercase_ : Optional[int] ):
snake_case_ = MPNetModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , lowercase_ )
snake_case_ = model(lowercase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def A_ ( self : str , lowercase_ : Optional[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : Tuple , lowercase_ : Optional[int] ):
snake_case_ = MPNetForQuestionAnswering(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , start_positions=lowercase_ , end_positions=lowercase_ , )
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 : Tuple , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : str , lowercase_ : Optional[int] , lowercase_ : Optional[Any] , lowercase_ : Any ):
snake_case_ = self.num_labels
snake_case_ = MPNetForSequenceClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A_ ( self : Any , lowercase_ : Any , lowercase_ : str , lowercase_ : Tuple , lowercase_ : List[str] , lowercase_ : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = self.num_choices
snake_case_ = MPNetForMultipleChoice(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ = model(
lowercase_ , attention_mask=lowercase_ , labels=lowercase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Union[str, Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : int , lowercase_ : List[str] ):
snake_case_ = self.num_labels
snake_case_ = MPNetForTokenClassification(config=lowercase_ )
model.to(lowercase_ )
model.eval()
snake_case_ = model(lowercase_ , attention_mask=lowercase_ , labels=lowercase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : Union[str, Any] ):
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_torch
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
snake_case_ = (
{
"feature-extraction": MPNetModel,
"fill-mask": MPNetForMaskedLM,
"question-answering": MPNetForQuestionAnswering,
"text-classification": MPNetForSequenceClassification,
"token-classification": MPNetForTokenClassification,
"zero-shot": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = True
def A_ ( self : Tuple ):
snake_case_ = MPNetModelTester(self )
snake_case_ = ConfigTester(self , config_class=lowercase_ , hidden_size=37 )
def A_ ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_ )
def A_ ( self : List[Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_ )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_ )
def A_ ( self : Tuple ):
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_ )
@require_torch
class a ( unittest.TestCase ):
@slow
def A_ ( self : List[Any] ):
snake_case_ = MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
snake_case_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
snake_case_ = model(lowercase_ )[0]
snake_case_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , lowercase_ )
snake_case_ = torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowercase_ , atol=1e-4 ) )
| 56
| 0
|
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase_ = get_logger(__name__)
class __lowerCAmelCase ( enum.Enum ):
'''simple docstring'''
__UpperCAmelCase : str = 'all_checks'
__UpperCAmelCase : Optional[Any] = 'basic_checks'
__UpperCAmelCase : Optional[int] = 'no_checks'
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
def lowercase ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Union[str, Any]=None ) -> Dict:
if expected_checksums is None:
logger.info('''Unable to verify checksums.''' )
return
if len(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) )
if len(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) > 0:
raise UnexpectedDownloadedFile(str(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) )
__a = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
__a = ''' for ''' + verification_name if verification_name is not None else ''''''
if len(__UpperCAmelCase ) > 0:
raise NonMatchingChecksumError(
f'''Checksums didn\'t match{for_verification_name}:\n'''
f'''{bad_urls}\n'''
'''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error''' )
logger.info('''All the checksums matched successfully''' + for_verification_name )
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
class __lowerCAmelCase ( _lowerCamelCase ):
'''simple docstring'''
pass
def lowercase ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str ) -> Tuple:
if expected_splits is None:
logger.info('''Unable to verify splits sizes.''' )
return
if len(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) > 0:
raise ExpectedMoreSplits(str(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) )
if len(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) > 0:
raise UnexpectedSplits(str(set(__UpperCAmelCase ) - set(__UpperCAmelCase ) ) )
__a = [
{'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(__UpperCAmelCase ) > 0:
raise NonMatchingSplitsSizesError(str(__UpperCAmelCase ) )
logger.info('''All the splits matched successfully.''' )
def lowercase ( lowerCAmelCase__ : Tuple , lowerCAmelCase__ : List[Any] = True ) -> dict:
if record_checksum:
__a = shaaaa()
with open(__UpperCAmelCase , '''rb''' ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , b'''''' ):
m.update(__UpperCAmelCase )
__a = m.hexdigest()
else:
__a = None
return {"num_bytes": os.path.getsize(__UpperCAmelCase ), "checksum": checksum}
def lowercase ( lowerCAmelCase__ : List[str] ) -> Tuple:
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 45
|
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class a ( _lowerCamelCase ):
def A_ ( self : str ):
snake_case_ = tempfile.mkdtemp()
snake_case_ = 8
# DPR tok
snake_case_ = [
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
snake_case_ = os.path.join(self.tmpdirname , '''dpr_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , DPR_VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
# BART tok
snake_case_ = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
snake_case_ = dict(zip(lowercase_ , range(len(lowercase_ ) ) ) )
snake_case_ = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
snake_case_ = {'''unk_token''': '''<unk>'''}
snake_case_ = os.path.join(self.tmpdirname , '''bart_tokenizer''' )
os.makedirs(lowercase_ , exist_ok=lowercase_ )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''vocab_file'''] )
snake_case_ = os.path.join(lowercase_ , BART_VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(lowercase_ ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(lowercase_ ) )
def A_ ( self : Union[str, Any] ):
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : Union[str, Any] ):
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''dpr_tokenizer''' ) )
def A_ ( self : int ):
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''bart_tokenizer''' ) )
def A_ ( self : str ):
shutil.rmtree(self.tmpdirname )
def A_ ( self : str ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def A_ ( self : str ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
return retriever
def A_ ( self : str , lowercase_ : bool ):
snake_case_ = self.get_dummy_dataset()
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''custom''' , )
if from_disk:
snake_case_ = os.path.join(self.tmpdirname , '''dataset''' )
snake_case_ = os.path.join(self.tmpdirname , '''index.faiss''' )
dataset.get_index('''embeddings''' ).save(os.path.join(self.tmpdirname , '''index.faiss''' ) )
dataset.drop_index('''embeddings''' )
dataset.save_to_disk(os.path.join(self.tmpdirname , '''dataset''' ) )
del dataset
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
else:
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , lowercase_ ) , )
return retriever
def A_ ( self : Tuple ):
snake_case_ = Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''text''': ['''foo''', '''bar'''],
'''title''': ['''Foo''', '''Bar'''],
'''embeddings''': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index('''embeddings''' , string_factory='''Flat''' , metric_type=faiss.METRIC_INNER_PRODUCT )
snake_case_ = os.path.join(self.tmpdirname , '''hf_bert_base.hnswSQ8_correct_phi_128.c_index''' )
dataset.save_faiss_index('''embeddings''' , index_file_name + '''.index.dpr''' )
pickle.dump(dataset['''id'''] , open(index_file_name + '''.index_meta.dpr''' , '''wb''' ) )
snake_case_ = os.path.join(self.tmpdirname , '''psgs_w100.tsv.pkl''' )
snake_case_ = {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
pickle.dump(lowercase_ , open(lowercase_ , '''wb''' ) )
snake_case_ = RagConfig(
retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='''legacy''' , index_path=self.tmpdirname , )
snake_case_ = RagRetriever(
lowercase_ , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def A_ ( self : Optional[Any] ):
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : str ):
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch('''transformers.models.rag.retrieval_rag.load_dataset''' ) as mock_load_dataset:
snake_case_ = self.get_dummy_dataset()
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : int ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : str ):
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''embeddings''', '''id''', '''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''id'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''id'''][0] , '''1''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''id'''][0] , '''0''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : Any ):
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
def A_ ( self : Any ):
snake_case_ = 1
snake_case_ = self.get_dummy_legacy_index_retriever()
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ ,snake_case_ ,snake_case_ = retriever.retrieve(lowercase_ , n_docs=lowercase_ )
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowercase_ ) , 2 )
self.assertEqual(sorted(doc_dicts[0] ) , ['''text''', '''title'''] )
self.assertEqual(len(doc_dicts[0]['''text'''] ) , lowercase_ )
self.assertEqual(doc_dicts[0]['''text'''][0] , '''bar''' ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]['''text'''][0] , '''foo''' ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() , [[1], [0]] )
def A_ ( self : int ):
snake_case_ = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowercase_ )
snake_case_ = RagRetriever.from_pretrained(lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever.retrieve(lowercase_ , n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : List[str] ):
import torch
snake_case_ = 1
snake_case_ = self.get_dummy_canonical_hf_index_retriever()
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
snake_case_ ,snake_case_ ,snake_case_ = (
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , lowercase_ )
self.assertIsInstance(lowercase_ , np.ndarray )
snake_case_ = retriever(
lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ , return_tensors='''pt''' , )
snake_case_ ,snake_case_ ,snake_case_ ,snake_case_ = ( # noqa: F841
out['''context_input_ids'''],
out['''context_attention_mask'''],
out['''retrieved_doc_embeds'''],
out['''doc_ids'''],
)
self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
self.assertIsInstance(lowercase_ , torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def A_ ( self : Tuple ):
snake_case_ = self.get_dpr_ctx_encoder_tokenizer()
snake_case_ = 1
snake_case_ = self.get_dummy_custom_hf_index_retriever(from_disk=lowercase_ )
retriever.set_ctx_encoder_tokenizer(lowercase_ )
snake_case_ = [[5, 7], [10, 11]]
snake_case_ = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa )
snake_case_ = retriever(lowercase_ , lowercase_ , prefix=retriever.config.generator.prefix , n_docs=lowercase_ )
self.assertEqual(
len(lowercase_ ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''') ) , lowercase_ ) # check for doc token related keys in dictionary.
| 56
| 0
|
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class A_ ( _lowerCamelCase , unittest.TestCase ):
_UpperCAmelCase : Dict = RoFormerTokenizer
_UpperCAmelCase : Optional[int] = RoFormerTokenizerFast
_UpperCAmelCase : Tuple = True
_UpperCAmelCase : Union[str, Any] = True
def lowerCAmelCase ( self : Tuple):
super().setUp()
def lowerCAmelCase ( self : List[str] ,**SCREAMING_SNAKE_CASE__ : str):
return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' ,**lowercase_)
def lowerCAmelCase ( self : int ,**SCREAMING_SNAKE_CASE__ : int):
return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' ,**lowercase_)
def lowerCAmelCase ( self : Optional[int]):
__lowerCamelCase : int = '永和服装饰品有限公司,今天天气非常好'
__lowerCamelCase : List[str] = '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
return input_text, output_text
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : Any = self.get_tokenizer()
__lowerCamelCase , __lowerCamelCase : Tuple = self.get_chinese_input_output_texts()
__lowerCamelCase : Optional[int] = tokenizer.tokenize(lowercase_)
self.assertListEqual(lowercase_ ,output_text.split())
__lowerCamelCase : List[str] = tokens + [tokenizer.unk_token]
__lowerCamelCase : List[Any] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_) ,lowercase_)
def lowerCAmelCase ( self : int):
__lowerCamelCase : int = self.get_rust_tokenizer()
__lowerCamelCase , __lowerCamelCase : int = self.get_chinese_input_output_texts()
__lowerCamelCase : List[str] = tokenizer.tokenize(lowercase_)
self.assertListEqual(lowercase_ ,output_text.split())
__lowerCamelCase : Optional[Any] = tokens + [tokenizer.unk_token]
__lowerCamelCase : List[str] = [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_) ,lowercase_)
def lowerCAmelCase ( self : int):
pass
def lowerCAmelCase ( self : List[Any]):
pass
def lowerCAmelCase ( self : Optional[int]):
pass
| 73
|
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
a : Dict = None
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
a : str = {
'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',
},
'tokenizer_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
},
}
# TODO(PVP) - this should be removed in Transformers v5
a : List[Any] = {
't5-small': 512,
't5-base': 512,
't5-large': 512,
't5-3b': 512,
't5-11b': 512,
}
class a ( _lowerCamelCase ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ["input_ids", "attention_mask"]
snake_case_ = TaTokenizer
snake_case_ = []
def __init__( self : List[Any] , lowercase_ : int=None , lowercase_ : Dict=None , lowercase_ : Dict="</s>" , lowercase_ : List[Any]="<unk>" , lowercase_ : int="<pad>" , lowercase_ : int=100 , lowercase_ : List[Any]=None , **lowercase_ : List[str] , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
snake_case_ = [F"<extra_id_{i}>" for i in range(lowercase_ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
snake_case_ = len(set(filter(lambda lowercase_ : bool('''extra_id_''' in str(lowercase_ ) ) , lowercase_ ) ) )
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''' )
super().__init__(
lowercase_ , tokenizer_file=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , pad_token=lowercase_ , extra_ids=lowercase_ , additional_special_tokens=lowercase_ , **lowercase_ , )
snake_case_ = vocab_file
snake_case_ = False if not self.vocab_file else True
snake_case_ = extra_ids
@staticmethod
def A_ ( lowercase_ : Optional[int] , lowercase_ : List[str] , lowercase_ : int ):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
snake_case_ = TaTokenizerFast.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.''' , lowercase_ , )
return max_model_length
def A_ ( self : Optional[Any] , lowercase_ : str , lowercase_ : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(lowercase_ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
snake_case_ = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
copyfile(self.vocab_file , lowercase_ )
logger.info(F"Copy vocab file to {out_vocab_file}" )
return (out_vocab_file,)
def A_ ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
snake_case_ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def A_ ( self : int , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None ):
snake_case_ = [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 A_ ( self : Dict ):
return list(
set(filter(lambda lowercase_ : bool(re.search(R'''<extra_id_\d+>''' , lowercase_ ) ) is not None , self.additional_special_tokens ) ) )
def A_ ( self : Any ):
return [self.convert_tokens_to_ids(lowercase_ ) for token in self.get_sentinel_tokens()]
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import re
def A_ ( snake_case : Optional[int] ) -> bool:
'''simple docstring'''
__UpperCamelCase = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase , __UpperCAmelCase ) )
if __name__ == "__main__":
lowercase__ : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
| 328
|
'''simple docstring'''
from __future__ import annotations
import math
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(__UpperCAmelCase ) == 0:
raise ValueError('''Scores cannot be empty''' )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
return min(
minimax(depth + 1, node_index * 2, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), minimax(depth + 1, node_index * 2 + 1, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ), )
def __magic_name__ ( ) -> None:
'''simple docstring'''
snake_case_ = [90, 23, 6, 33, 21, 65, 123, 3_4423]
snake_case_ = math.log(len(__UpperCAmelCase ), 2 )
print('''Optimal value : ''', end='''''' )
print(minimax(0, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 56
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|
"""simple docstring"""
from maths.prime_check import is_prime
def UpperCAmelCase__ ( lowerCAmelCase__ :Any ) -> int:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
lowercase = f'Input value of [number={number}] must be an integer'
raise TypeError(__UpperCAmelCase )
if is_prime(__UpperCAmelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 197
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
elif "subsample" in key:
snake_case_ = s_dict.pop(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ ,snake_case_ = emb.weight.shape
snake_case_ = nn.Linear(__UpperCAmelCase, __UpperCAmelCase, bias=__UpperCAmelCase )
snake_case_ = emb.weight.data
return lin_layer
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
snake_case_ = torch.load(__UpperCAmelCase, map_location='''cpu''' )
snake_case_ = mam_aaa['''args''']
snake_case_ = mam_aaa['''model''']
snake_case_ = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(__UpperCAmelCase )
rename_keys(__UpperCAmelCase )
snake_case_ = state_dict['''decoder.embed_tokens.weight'''].shape[0]
snake_case_ = args.share_decoder_input_output_embed
snake_case_ = [int(__UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
snake_case_ = SpeechaTextConfig(
vocab_size=__UpperCAmelCase, max_source_positions=args.max_source_positions, max_target_positions=args.max_target_positions, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', num_conv_layers=len(__UpperCAmelCase ), conv_channels=args.conv_channels, conv_kernel_sizes=__UpperCAmelCase, input_feat_per_channel=args.input_feat_per_channel, input_channels=args.input_channels, tie_word_embeddings=__UpperCAmelCase, num_beams=5, max_length=200, use_cache=__UpperCAmelCase, decoder_start_token_id=2, early_stopping=__UpperCAmelCase, )
snake_case_ = SpeechaTextForConditionalGeneration(__UpperCAmelCase )
snake_case_ ,snake_case_ = model.model.load_state_dict(__UpperCAmelCase, strict=__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0 and not set(__UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
F" but all the following weights are missing {missing}" )
if tie_embeds:
snake_case_ = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
snake_case_ = lm_head_weights
model.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
a : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
a : List[Any] = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
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"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case_ = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2StructConfig',
'Pix2StructTextConfig',
'Pix2StructVisionConfig',
],
'processing_pix2struct': ['Pix2StructProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['Pix2StructImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
'Pix2StructPreTrainedModel',
'Pix2StructForConditionalGeneration',
'Pix2StructVisionModel',
'Pix2StructTextModel',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 78
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
snake_case_ = ["transformers", "torch", "note_seq"]
def __init__( self : Union[str, Any] , *lowercase_ : Optional[int] , **lowercase_ : int ):
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : List[Any] , *lowercase_ : Optional[Any] , **lowercase_ : str ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def A_ ( cls : Tuple , *lowercase_ : Union[str, Any] , **lowercase_ : List[Any] ):
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
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|
"""simple docstring"""
from __future__ import annotations
class A_ :
"""simple docstring"""
def __init__( self :Optional[int] , lowerCamelCase_ :str , lowerCamelCase_ :str ):
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : Any =text, pattern
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] =len(lowercase_ ), len(lowercase_ )
def UpperCAmelCase__ ( self :Union[str, Any] , lowerCamelCase_ :str ):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def UpperCAmelCase__ ( self :List[Any] , lowerCamelCase_ :int ):
"""simple docstring"""
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def UpperCAmelCase__ ( self :Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] =[]
for i in range(self.textLen - self.patLen + 1 ):
lowerCamelCase__ : int =self.mismatch_in_text(lowercase_ )
if mismatch_index == -1:
positions.append(lowercase_ )
else:
lowerCamelCase__ : str =self.match_in_pattern(self.text[mismatch_index] )
lowerCamelCase__ : Any =(
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
lowerCAmelCase = 'ABAABA'
lowerCAmelCase = 'AB'
lowerCAmelCase = BoyerMooreSearch(text, pattern)
lowerCAmelCase = bms.bad_character_heuristic()
if len(positions) == 0:
print("""No match found""")
else:
print("""Pattern found in following positions: """)
print(positions)
| 126
|
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a : int = abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
config.addinivalue_line(
'''markers''', '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested''' )
config.addinivalue_line(
'''markers''', '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested''' )
config.addinivalue_line('''markers''', '''is_pipeline_test: mark test to run only when pipelines are tested''' )
config.addinivalue_line('''markers''', '''is_staging_test: mark test to run only in the staging environment''' )
config.addinivalue_line('''markers''', '''accelerate_tests: mark test that require accelerate''' )
config.addinivalue_line('''markers''', '''tool_tests: mark the tool tests that are run on their specific schedule''' )
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
from transformers.testing_utils import pytest_addoption_shared
pytest_addoption_shared(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
from transformers.testing_utils import pytest_terminal_summary_main
snake_case_ = terminalreporter.config.getoption('''--make-reports''' )
if make_reports:
pytest_terminal_summary_main(__UpperCAmelCase, id=__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
if exitstatus == 5:
snake_case_ = 0
# Doctest custom flag to ignore output.
a : Union[str, Any] = doctest.register_optionflag('IGNORE_RESULT')
a : Optional[int] = doctest.OutputChecker
class a ( _lowerCamelCase ):
def A_ ( self : List[Any] , lowercase_ : int , lowercase_ : Tuple , lowercase_ : Optional[int] ):
if IGNORE_RESULT & optionflags:
return True
return OutputChecker.check_output(self , lowercase_ , lowercase_ , lowercase_ )
a : List[Any] = CustomOutputChecker
a : Optional[int] = HfDoctestModule
a : Tuple = HfDocTestParser
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|
UpperCAmelCase_ = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
UpperCAmelCase_ = ['a', 'b', 'c', 'd', 'e']
def lowerCAmelCase_ ( __UpperCAmelCase: str , __UpperCAmelCase: Dict , __UpperCAmelCase: List[str] ) -> Any:
UpperCamelCase__ : str = start
# add current to visited
visited.append(__UpperCAmelCase )
UpperCamelCase__ : Optional[Any] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
UpperCamelCase__ : Optional[int] = topological_sort(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# if all neighbors visited add current to sort
sort.append(__UpperCAmelCase )
# if all vertices haven't been visited select a new one to visit
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
for vertice in vertices:
if vertice not in visited:
UpperCamelCase__ : Optional[int] = topological_sort(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# return sort
return sort
if __name__ == "__main__":
UpperCAmelCase_ = topological_sort('a', [], [])
print(sort)
| 201
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
a : Dict = logging.get_logger(__name__)
a : List[str] = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class a ( _lowerCamelCase ):
snake_case_ = "marian"
snake_case_ = ["past_key_values"]
snake_case_ = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : List[Any] , lowercase_ : Optional[Any]=5_8101 , lowercase_ : Dict=None , lowercase_ : List[str]=1024 , lowercase_ : Optional[Any]=12 , lowercase_ : int=4096 , lowercase_ : Any=16 , lowercase_ : Optional[int]=12 , lowercase_ : str=4096 , lowercase_ : Union[str, Any]=16 , lowercase_ : Dict=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Optional[Any]=True , lowercase_ : Union[str, Any]=True , lowercase_ : int="gelu" , lowercase_ : Dict=1024 , lowercase_ : int=0.1 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.0 , lowercase_ : Tuple=0.02 , lowercase_ : int=5_8100 , lowercase_ : Optional[Any]=False , lowercase_ : Any=5_8100 , lowercase_ : Optional[int]=0 , lowercase_ : Dict=0 , lowercase_ : List[str]=True , **lowercase_ : Any , ):
snake_case_ = vocab_size
snake_case_ = decoder_vocab_size or vocab_size
snake_case_ = max_position_embeddings
snake_case_ = d_model
snake_case_ = encoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class a ( _lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def A_ ( self : Union[str, Any] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ = {0: '''batch'''}
snake_case_ = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
snake_case_ = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
else:
snake_case_ = OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def A_ ( self : Dict ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super().outputs
else:
snake_case_ = super(lowercase_ , self ).outputs
if self.use_past:
snake_case_ ,snake_case_ = self.num_layers
for i in range(lowercase_ ):
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
snake_case_ = {0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def A_ ( self : Dict , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
snake_case_ = seq_length if not self.use_past else 1
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
snake_case_ = {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
snake_case_ = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
snake_case_ = common_inputs['''decoder_input_ids'''].shape[1]
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = decoder_seq_length + 3
snake_case_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ = torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
snake_case_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ ,snake_case_ = self.num_layers
snake_case_ = min(lowercase_ , lowercase_ )
snake_case_ = max(lowercase_ , lowercase_ ) - min_num_layers
snake_case_ = '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
snake_case_ = encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def A_ ( self : Union[str, Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
snake_case_ ,snake_case_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
snake_case_ = seqlen + 2
snake_case_ ,snake_case_ = self.num_layers
snake_case_ ,snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = common_inputs['''attention_mask'''].dtype
snake_case_ = torch.cat(
[common_inputs['''attention_mask'''], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
snake_case_ = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def A_ ( self : List[str] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ = tokenizer.num_special_tokens_to_add(lowercase_ )
snake_case_ = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
snake_case_ = [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def A_ ( self : Any , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
snake_case_ = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def A_ ( self : Dict , lowercase_ : List[str] , lowercase_ : List[str] , lowercase_ : int , lowercase_ : List[str] ):
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
snake_case_ = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
@property
def A_ ( self : List[str] ):
return 1e-4
| 56
| 0
|
__lowerCamelCase = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
__lowerCamelCase = [{'type': 'code', 'content': INSTALL_CONTENT}]
__lowerCamelCase = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 59
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
snake_case_ = CycleDiffusionPipeline
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
snake_case_ = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def A_ ( self : Tuple ):
torch.manual_seed(0 )
snake_case_ = 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 , )
snake_case_ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , )
torch.manual_seed(0 )
snake_case_ = 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 , )
torch.manual_seed(0 )
snake_case_ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case_ = CLIPTextModel(lowercase_ )
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 A_ ( self : Any , lowercase_ : int , lowercase_ : Optional[Any]=0 ):
snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ )
snake_case_ = image / 2 + 0.5
if str(lowercase_ ).startswith('''mps''' ):
snake_case_ = torch.manual_seed(lowercase_ )
else:
snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
snake_case_ = {
'''prompt''': '''An astronaut riding an elephant''',
'''source_prompt''': '''An astronaut riding a horse''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''eta''': 0.1,
'''strength''': 0.8,
'''guidance_scale''': 3,
'''source_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def A_ ( self : Union[str, Any] ):
snake_case_ = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case_ = self.get_dummy_components()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def A_ ( self : Union[str, Any] ):
snake_case_ = self.get_dummy_components()
for name, module in components.items():
if hasattr(lowercase_ , '''half''' ):
snake_case_ = module.half()
snake_case_ = CycleDiffusionPipeline(**lowercase_ )
snake_case_ = pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
snake_case_ = self.get_dummy_inputs(lowercase_ )
snake_case_ = pipe(**lowercase_ )
snake_case_ = output.images
snake_case_ = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
snake_case_ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def A_ ( self : Optional[int] ):
return super().test_save_load_local()
@unittest.skip('''non-deterministic pipeline''' )
def A_ ( self : List[Any] ):
return super().test_inference_batch_single_identical()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_save_load_optional_components()
@skip_mps
def A_ ( self : Union[str, Any] ):
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def A_ ( self : List[Any] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A_ ( self : Union[str, Any] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(
lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision='''fp16''' )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def A_ ( self : List[str] ):
snake_case_ = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/cycle-diffusion/black_colored_car.png''' )
snake_case_ = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' )
snake_case_ = init_image.resize((512, 512) )
snake_case_ = '''CompVis/stable-diffusion-v1-4'''
snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' )
snake_case_ = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ )
pipe.to(lowercase_ )
pipe.set_progress_bar_config(disable=lowercase_ )
pipe.enable_attention_slicing()
snake_case_ = '''A black colored car'''
snake_case_ = '''A blue colored car'''
snake_case_ = torch.manual_seed(0 )
snake_case_ = pipe(
prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , )
snake_case_ = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 56
| 0
|
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase_ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
'''simple docstring'''
def __init__( self , __A , __A , __A , __A = 1.0 , __A = None , ):
"""simple docstring"""
super().__init__()
lowerCamelCase : Optional[Any] = initial_learning_rate
lowerCamelCase : List[Any] = warmup_steps
lowerCamelCase : Union[str, Any] = power
lowerCamelCase : Optional[int] = decay_schedule_fn
lowerCamelCase : Optional[Any] = name
def __call__( self , __A ):
"""simple docstring"""
with tf.name_scope(self.name or "WarmUp" ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
lowerCamelCase : str = tf.cast(lowercase_ , tf.floataa )
lowerCamelCase : List[str] = tf.cast(self.warmup_steps , tf.floataa )
lowerCamelCase : Any = global_step_float / warmup_steps_float
lowerCamelCase : Optional[int] = self.initial_learning_rate * tf.math.pow(lowercase_ , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=lowercase_ , )
def _snake_case ( self ):
"""simple docstring"""
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 0.9 , SCREAMING_SNAKE_CASE_ = 0.999 , SCREAMING_SNAKE_CASE_ = 1E-8 , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 1.0 , SCREAMING_SNAKE_CASE_ = None , ):
'''simple docstring'''
lowerCamelCase : Union[str, Any] = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=__UpperCAmelCase , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=__UpperCAmelCase , )
if num_warmup_steps:
lowerCamelCase : Union[str, Any] = WarmUp(
initial_learning_rate=__UpperCAmelCase , decay_schedule_fn=__UpperCAmelCase , warmup_steps=__UpperCAmelCase , )
if weight_decay_rate > 0.0:
lowerCamelCase : Optional[int] = AdamWeightDecay(
learning_rate=__UpperCAmelCase , weight_decay_rate=__UpperCAmelCase , beta_a=__UpperCAmelCase , beta_a=__UpperCAmelCase , epsilon=__UpperCAmelCase , clipnorm=__UpperCAmelCase , global_clipnorm=__UpperCAmelCase , exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"] , include_in_weight_decay=__UpperCAmelCase , )
else:
lowerCamelCase : str = tf.keras.optimizers.Adam(
learning_rate=__UpperCAmelCase , beta_a=__UpperCAmelCase , beta_a=__UpperCAmelCase , epsilon=__UpperCAmelCase , clipnorm=__UpperCAmelCase , global_clipnorm=__UpperCAmelCase , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class UpperCAmelCase_ ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self , __A = 0.001 , __A = 0.9 , __A = 0.999 , __A = 1e-7 , __A = False , __A = 0.0 , __A = None , __A = None , __A = "AdamWeightDecay" , **__A , ):
"""simple docstring"""
super().__init__(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
lowerCamelCase : Any = weight_decay_rate
lowerCamelCase : Optional[int] = include_in_weight_decay
lowerCamelCase : Union[str, Any] = exclude_from_weight_decay
@classmethod
def _snake_case ( cls , __A ):
"""simple docstring"""
lowerCamelCase : Optional[int] = {"WarmUp": WarmUp}
return super(lowercase_ , cls ).from_config(lowercase_ , custom_objects=lowercase_ )
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
super(lowercase_ , self )._prepare_local(lowercase_ , lowercase_ , lowercase_ )
lowerCamelCase : List[Any] = tf.constant(
self.weight_decay_rate , name="adam_weight_decay_rate" )
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
lowerCamelCase : Dict = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"] , use_locking=self._use_locking , )
return tf.no_op()
def _snake_case ( self , __A , __A=None , **__A ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : int = list(zip(*lowercase_ ) )
return super(lowercase_ , self ).apply_gradients(zip(lowercase_ , lowercase_ ) , name=lowercase_ , **lowercase_ )
def _snake_case ( self , __A , __A , __A ):
"""simple docstring"""
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
lowerCamelCase : str = apply_state or {}
lowerCamelCase : Optional[Any] = apply_state.get((var_device, var_dtype) )
if coefficients is None:
lowerCamelCase : Optional[int] = self._fallback_apply_state(lowercase_ , lowercase_ )
lowerCamelCase : Optional[int] = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def _snake_case ( self , __A , __A , __A=None ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : Optional[Any] = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
lowerCamelCase : str = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_dense(lowercase_ , lowercase_ , **lowercase_ )
def _snake_case ( self , __A , __A , __A , __A=None ):
"""simple docstring"""
lowerCamelCase , lowerCamelCase : Any = self._get_lr(var.device , var.dtype.base_dtype , lowercase_ )
lowerCamelCase : Optional[Any] = self._decay_weights_op(lowercase_ , lowercase_ , lowercase_ )
with tf.control_dependencies([decay] ):
return super(lowercase_ , self )._resource_apply_sparse(lowercase_ , lowercase_ , lowercase_ , **lowercase_ )
def _snake_case ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = super().get_config()
config.update({"weight_decay_rate": self.weight_decay_rate} )
return config
def _snake_case ( self , __A ):
"""simple docstring"""
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(lowercase_ , lowercase_ ) is not None:
return False
return True
class UpperCAmelCase_ ( _lowerCamelCase ):
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = []
lowerCamelCase : int = None
@property
def _snake_case ( self ):
"""simple docstring"""
if self._accum_steps is None:
lowerCamelCase : int = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def _snake_case ( self ):
"""simple docstring"""
if not self._gradients:
raise ValueError("The accumulator should be called first to initialize the gradients" )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , __A ):
"""simple docstring"""
if not self._gradients:
lowerCamelCase : List[Any] = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(lowercase_ ) , trainable=lowercase_ , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(lowercase_ ) != len(self._gradients ):
raise ValueError(F"""Expected {len(self._gradients )} gradients, but got {len(lowercase_ )}""" )
for accum_gradient, gradient in zip(self._gradients , lowercase_ ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(lowercase_ )
self._accum_steps.assign_add(1 )
def _snake_case ( self ):
"""simple docstring"""
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(lowercase_ ) )
| 283
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : str = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class a ( _lowerCamelCase ):
snake_case_ = "big_bird"
def __init__( self : Union[str, Any] , lowercase_ : List[Any]=5_0358 , lowercase_ : Tuple=768 , lowercase_ : Dict=12 , lowercase_ : str=12 , lowercase_ : Tuple=3072 , lowercase_ : Any="gelu_new" , lowercase_ : Optional[Any]=0.1 , lowercase_ : List[Any]=0.1 , lowercase_ : List[Any]=4096 , lowercase_ : List[Any]=2 , lowercase_ : List[str]=0.02 , lowercase_ : Optional[int]=1e-12 , lowercase_ : Tuple=True , lowercase_ : Tuple=0 , lowercase_ : str=1 , lowercase_ : Union[str, Any]=2 , lowercase_ : Optional[Any]=66 , lowercase_ : Optional[int]="block_sparse" , lowercase_ : Any=True , lowercase_ : List[str]=False , lowercase_ : Any=64 , lowercase_ : Tuple=3 , lowercase_ : Tuple=None , **lowercase_ : Tuple , ):
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , sep_token_id=lowercase_ , **lowercase_ , )
snake_case_ = vocab_size
snake_case_ = max_position_embeddings
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_ = type_vocab_size
snake_case_ = layer_norm_eps
snake_case_ = use_cache
snake_case_ = rescale_embeddings
snake_case_ = attention_type
snake_case_ = use_bias
snake_case_ = block_size
snake_case_ = num_random_blocks
snake_case_ = classifier_dropout
class a ( _lowerCamelCase ):
@property
def A_ ( self : str ):
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
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|
class snake_case_ :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : Union[str, Any] ) ->List[Any]:
# we need a list not a string, so do something to change the type
snake_case_ = arr.split(''',''' )
def snake_case__( self : Union[str, Any] ) ->Optional[Any]:
snake_case_ = [int(self.array[0] )] * len(self.array )
snake_case_ = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
snake_case_ = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
snake_case_ = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
lowerCAmelCase_ = input('''please input some numbers:''')
lowerCAmelCase_ = SubArray(whole_array)
lowerCAmelCase_ = array.solve_sub_array()
print(('''the results is:''', re))
| 8
|
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> str:
'''simple docstring'''
assert isinstance(__UpperCAmelCase, __UpperCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''', [False, True] )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
snake_case_ = SqlDatasetReader(
'''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase, keep_in_memory=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
@require_sqlalchemy
@pytest.mark.parametrize(
'''features''', [
None,
{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
{'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
{'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
{'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
], )
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
snake_case_ = features.copy() if features else default_expected_features
snake_case_ = (
Features({feature: Value(__UpperCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, features=__UpperCAmelCase, cache_dir=__UpperCAmelCase ).read()
_check_sql_dataset(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(__UpperCAmelCase ) ) as con:
snake_case_ = con.cursor()
cur.execute('''SELECT * FROM dataset''' )
for row in cur:
yield row
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=1 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Any:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=2 ).write()
snake_case_ = iter_sql_file(__UpperCAmelCase )
snake_case_ = iter_sql_file(__UpperCAmelCase )
for rowa, rowa in zip(__UpperCAmelCase, __UpperCAmelCase ):
assert rowa == rowa
@require_sqlalchemy
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
snake_case_ = tmp_path / '''cache'''
snake_case_ = os.path.join(__UpperCAmelCase, '''tmp.sql''' )
snake_case_ = SqlDatasetReader('''dataset''', '''sqlite:///''' + sqlite_path, cache_dir=__UpperCAmelCase ).read()
with pytest.raises(__UpperCAmelCase ):
SqlDatasetWriter(__UpperCAmelCase, '''dataset''', '''sqlite:///''' + output_sqlite_path, num_proc=0 ).write()
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|
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class lowercase ( _lowerCamelCase ):
_a = "marian"
_a = ["past_key_values"]
_a = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self , _a=5_8101 , _a=None , _a=1024 , _a=12 , _a=4096 , _a=16 , _a=12 , _a=4096 , _a=16 , _a=0.0 , _a=0.0 , _a=True , _a=True , _a="gelu" , _a=1024 , _a=0.1 , _a=0.0 , _a=0.0 , _a=0.02 , _a=5_8100 , _a=False , _a=5_8100 , _a=0 , _a=0 , _a=True , **_a , ) -> int:
_A : Any = vocab_size
_A : int = decoder_vocab_size or vocab_size
_A : Optional[int] = max_position_embeddings
_A : Tuple = d_model
_A : Any = encoder_ffn_dim
_A : List[str] = encoder_layers
_A : Union[str, Any] = encoder_attention_heads
_A : Tuple = decoder_ffn_dim
_A : Optional[int] = decoder_layers
_A : Any = decoder_attention_heads
_A : Dict = dropout
_A : Union[str, Any] = attention_dropout
_A : Optional[Any] = activation_dropout
_A : List[str] = activation_function
_A : int = init_std
_A : Union[str, Any] = encoder_layerdrop
_A : Dict = decoder_layerdrop
_A : List[Any] = use_cache
_A : Optional[Any] = encoder_layers
_A : Dict = scale_embedding # scale factor will be sqrt(d_model) if True
_A : Union[str, Any] = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class lowercase ( _lowerCamelCase ):
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def a__ ( self ) -> Union[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_A : Dict = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
_A : List[Any] = {0: """batch"""}
_A : Dict = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
_A : str = {0: """batch""", 1: """decoder_sequence"""}
_A : int = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
_A : List[Any] = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
_A , _A : List[str] = self.num_layers
for i in range(lowercase_ ):
_A : List[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
_A : int = {0: """batch""", 2: """past_sequence + sequence"""}
else:
_A : int = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def a__ ( self ) -> Union[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_A : Optional[Any] = super().outputs
else:
_A : List[Any] = super(lowercase_ , self ).outputs
if self.use_past:
_A , _A : List[str] = self.num_layers
for i in range(lowercase_ ):
_A : List[Any] = {0: """batch""", 2: """past_sequence + sequence"""}
_A : int = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def a__ ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , ) -> Dict:
_A : Tuple = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
_A : List[str] = seq_length if not self.use_past else 1
_A : Optional[Any] = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
_A : List[Any] = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
_A : str = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_A , _A : str = common_inputs["""input_ids"""].shape
_A : List[str] = common_inputs["""decoder_input_ids"""].shape[1]
_A , _A : Union[str, Any] = self.num_attention_heads
_A : List[Any] = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_A : str = decoder_seq_length + 3
_A : str = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_A : Tuple = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
_A : List[str] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_A , _A : Optional[Any] = self.num_layers
_A : List[str] = min(lowercase_ , lowercase_ )
_A : int = max(lowercase_ , lowercase_ ) - min_num_layers
_A : str = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
_A : List[str] = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def a__ ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , ) -> int:
_A : str = self._generate_dummy_inputs_for_encoder_and_decoder(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
_A , _A : Optional[int] = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
_A : Any = seqlen + 2
_A , _A : List[str] = self.num_layers
_A , _A : int = self.num_attention_heads
_A : Dict = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_A : Union[str, Any] = common_inputs["""attention_mask"""].dtype
_A : List[Any] = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
_A : List[Any] = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def a__ ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , ) -> Dict:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_A : Any = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_A : Dict = tokenizer.num_special_tokens_to_add(lowercase_ )
_A : str = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
_A : Tuple = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
_A : Optional[Any] = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def a__ ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , ) -> Any:
if self.task in ["default", "seq2seq-lm"]:
_A : List[str] = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
_A : Union[str, Any] = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def a__ ( self , _a , _a , _a , _a ) -> Tuple:
if self.task in ["default", "seq2seq-lm"]:
_A : List[str] = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
_A : Any = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
@property
def a__ ( self ) -> str:
return 1e-4
| 26
|
'''simple docstring'''
from collections import defaultdict
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
snake_case_ = 1
snake_case_ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCAmelCase )
if ret % 2 == 0:
cuts.append(__UpperCAmelCase )
return ret
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
dfs(1 )
if __name__ == "__main__":
a ,a : Dict = 10, 9
a : Dict = defaultdict(list)
a : dict[int, bool] = {}
a : list[int] = []
a : Tuple = 0
a : str = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 56
| 0
|
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple ) -> str:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''iterations must be defined as integers''' )
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or not number >= 1:
raise ValueError(
'''starting number must be
and integer and be more than 0''' )
if not iterations >= 1:
raise ValueError('''Iterations must be done more than 0 times to play FizzBuzz''' )
__a = ''''''
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(__UpperCAmelCase )
# print(out)
number += 1
out += " "
return out
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45
|
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x_na or function(__UpperCAmelCase ) == function(__UpperCAmelCase ):
raise ZeroDivisionError('''float division by zero, could not find root''' )
snake_case_ = x_na - (
function(__UpperCAmelCase ) / ((function(__UpperCAmelCase ) - function(__UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
snake_case_ = x_na
snake_case_ = x_na
def __magic_name__ ( __UpperCAmelCase ) -> float:
'''simple docstring'''
return math.pow(__UpperCAmelCase, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 56
| 0
|
a ={
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
'2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
'8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
'?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
'(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
a ={value: key for key, value in MORSE_CODE_DICT.items()}
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
__lowerCamelCase : Optional[int] = 'Morse code here!'
print(__UpperCAmelCase )
__lowerCamelCase : Tuple = encrypt(__UpperCAmelCase )
print(__UpperCAmelCase )
__lowerCamelCase : List[str] = decrypt(__UpperCAmelCase )
print(__UpperCAmelCase )
if __name__ == "__main__":
main()
| 73
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Any = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = DPTConfig()
if "large" in checkpoint_url:
snake_case_ = 1024
snake_case_ = 4096
snake_case_ = 24
snake_case_ = 16
snake_case_ = [5, 11, 17, 23]
snake_case_ = [256, 512, 1024, 1024]
snake_case_ = (1, 384, 384)
if "ade" in checkpoint_url:
snake_case_ = True
snake_case_ = 150
snake_case_ = '''huggingface/label-files'''
snake_case_ = '''ade20k-id2label.json'''
snake_case_ = json.load(open(cached_download(hf_hub_url(__UpperCAmelCase, __UpperCAmelCase, repo_type='''dataset''' ) ), '''r''' ) )
snake_case_ = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
snake_case_ = idalabel
snake_case_ = {v: k for k, v in idalabel.items()}
snake_case_ = [1, 150, 480, 480]
return config, expected_shape
def __magic_name__ ( __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase, __UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case_ = name.replace('''pretrained.model''', '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case_ = name.replace('''pretrained.model''', '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case_ = name.replace('''patch_embed''', '''patch_embeddings''' )
if "pos_embed" in name:
snake_case_ = name.replace('''pos_embed''', '''position_embeddings''' )
if "attn.proj" in name:
snake_case_ = name.replace('''attn.proj''', '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case_ = name.replace('''proj''', '''projection''' )
if "blocks" in name:
snake_case_ = name.replace('''blocks''', '''layer''' )
if "mlp.fc1" in name:
snake_case_ = name.replace('''mlp.fc1''', '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case_ = name.replace('''mlp.fc2''', '''output.dense''' )
if "norm1" in name:
snake_case_ = name.replace('''norm1''', '''layernorm_before''' )
if "norm2" in name:
snake_case_ = name.replace('''norm2''', '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case_ = name.replace('''scratch.output_conv''', '''head''' )
if "scratch" in name:
snake_case_ = name.replace('''scratch''', '''neck''' )
if "layer1_rn" in name:
snake_case_ = name.replace('''layer1_rn''', '''convs.0''' )
if "layer2_rn" in name:
snake_case_ = name.replace('''layer2_rn''', '''convs.1''' )
if "layer3_rn" in name:
snake_case_ = name.replace('''layer3_rn''', '''convs.2''' )
if "layer4_rn" in name:
snake_case_ = name.replace('''layer4_rn''', '''convs.3''' )
if "refinenet" in name:
snake_case_ = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case_ = name.replace(F"refinenet{layer_idx}", F"fusion_stage.layers.{abs(layer_idx-4 )}" )
if "out_conv" in name:
snake_case_ = name.replace('''out_conv''', '''projection''' )
if "resConfUnit1" in name:
snake_case_ = name.replace('''resConfUnit1''', '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case_ = name.replace('''resConfUnit2''', '''residual_layer2''' )
if "conv1" in name:
snake_case_ = name.replace('''conv1''', '''convolution1''' )
if "conv2" in name:
snake_case_ = name.replace('''conv2''', '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.0.project.0''', '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.0.project.0''', '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.0.project.0''', '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.0.project.0''', '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.3''', '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess1.4''', '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.3''', '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess2.4''', '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess3.3''', '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.3''', '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case_ = name.replace('''pretrained.act_postprocess4.4''', '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case_ = name.replace('''pretrained''', '''dpt''' )
if "bn" in name:
snake_case_ = name.replace('''bn''', '''batch_norm''' )
if "head" in name:
snake_case_ = name.replace('''head''', '''head.head''' )
if "encoder.norm" in name:
snake_case_ = name.replace('''encoder.norm''', '''layernorm''' )
if "auxlayer" in name:
snake_case_ = name.replace('''auxlayer''', '''auxiliary_head.head''' )
return name
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Dict:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
snake_case_ = state_dict.pop(F"dpt.encoder.layer.{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 __magic_name__ ( ) -> Any:
'''simple docstring'''
snake_case_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case_ = Image.open(requests.get(__UpperCAmelCase, stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ ,snake_case_ = get_dpt_config(__UpperCAmelCase )
# load original state_dict from URL
snake_case_ = torch.hub.load_state_dict_from_url(__UpperCAmelCase, map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(__UpperCAmelCase )
# rename keys
for key in state_dict.copy().keys():
snake_case_ = state_dict.pop(__UpperCAmelCase )
snake_case_ = val
# read in qkv matrices
read_in_q_k_v(__UpperCAmelCase, __UpperCAmelCase )
# load HuggingFace model
snake_case_ = DPTForSemanticSegmentation(__UpperCAmelCase ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(__UpperCAmelCase )
model.load_state_dict(__UpperCAmelCase )
model.eval()
# Check outputs on an image
snake_case_ = 480 if '''ade''' in checkpoint_url else 384
snake_case_ = DPTImageProcessor(size=__UpperCAmelCase )
snake_case_ = prepare_img()
snake_case_ = image_processor(__UpperCAmelCase, return_tensors='''pt''' )
# forward pass
snake_case_ = model(**__UpperCAmelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCAmelCase ).predicted_depth
# Assert logits
snake_case_ = torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]] )
if "ade" in checkpoint_url:
snake_case_ = torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]] )
assert outputs.shape == torch.Size(__UpperCAmelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3], __UpperCAmelCase, atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3], __UpperCAmelCase )
)
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCAmelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCAmelCase )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add model''', use_temp_dir=__UpperCAmelCase, )
image_processor.push_to_hub(
repo_path_or_name=Path(__UpperCAmelCase, __UpperCAmelCase ), organization='''nielsr''', commit_message='''Add image processor''', use_temp_dir=__UpperCAmelCase, )
if __name__ == "__main__":
a : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
type=str,
help='URL of the original DPT checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
parser.add_argument(
'--model_name',
default='dpt-large',
type=str,
help='Name of the model, in case you\'re pushing to the hub.',
)
a : List[Any] = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
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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"
)
lowercase__ : Dict = None
lowercase__ : Optional[int] = {
'7B': 1_1_0_0_8,
'13B': 1_3_8_2_4,
'30B': 1_7_9_2_0,
'65B': 2_2_0_1_6,
'70B': 2_8_6_7_2,
}
lowercase__ : Union[str, Any] = {
'7B': 1,
'7Bf': 1,
'13B': 2,
'13Bf': 2,
'30B': 4,
'65B': 8,
'70B': 8,
'70Bf': 8,
}
def A_ ( snake_case : Optional[Any] , snake_case : Dict=1 , snake_case : Union[str, Any]=256 ) -> Dict:
'''simple docstring'''
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def A_ ( snake_case : int ) -> Optional[int]:
'''simple docstring'''
with open(__UpperCAmelCase , '''r''' ) as f:
return json.load(__UpperCAmelCase )
def A_ ( snake_case : Dict , snake_case : List[Any] ) -> Any:
'''simple docstring'''
with open(__UpperCAmelCase , '''w''' ) as f:
json.dump(__UpperCAmelCase , __UpperCAmelCase )
def A_ ( snake_case : List[Any] , snake_case : Optional[int] , snake_case : List[Any] , snake_case : Tuple=True ) -> Optional[int]:
'''simple docstring'''
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__UpperCamelCase = os.path.join(__UpperCAmelCase , '''tmp''' )
os.makedirs(__UpperCAmelCase , exist_ok=__UpperCAmelCase )
__UpperCamelCase = read_json(os.path.join(__UpperCAmelCase , '''params.json''' ) )
__UpperCamelCase = NUM_SHARDS[model_size]
__UpperCamelCase = params['''n_layers''']
__UpperCamelCase = params['''n_heads''']
__UpperCamelCase = n_heads // num_shards
__UpperCamelCase = params['''dim''']
__UpperCamelCase = dim // n_heads
__UpperCamelCase = 10000.0
__UpperCamelCase = 1.0 / (base ** (torch.arange(0 , __UpperCAmelCase , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
__UpperCamelCase = params['''n_kv_heads'''] # for GQA / MQA
__UpperCamelCase = n_heads_per_shard // num_key_value_heads
__UpperCamelCase = dim // num_key_value_heads
else: # compatibility with other checkpoints
__UpperCamelCase = n_heads
__UpperCamelCase = n_heads_per_shard
__UpperCamelCase = dim
# permute for sliced rotary
def permute(snake_case : Optional[Any] , snake_case : List[Any]=n_heads , snake_case : List[Any]=dim , snake_case : Union[str, Any]=dim ):
return w.view(__UpperCAmelCase , dima // n_heads // 2 , 2 , __UpperCAmelCase ).transpose(1 , 2 ).reshape(__UpperCAmelCase , __UpperCAmelCase )
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.)
__UpperCamelCase = torch.load(os.path.join(__UpperCAmelCase , '''consolidated.00.pth''' ) , map_location='''cpu''' )
else:
# Sharded
__UpperCamelCase = [
torch.load(os.path.join(__UpperCAmelCase , f"consolidated.{i:02d}.pth" ) , map_location='''cpu''' )
for i in range(__UpperCAmelCase )
]
__UpperCamelCase = 0
__UpperCamelCase = {'''weight_map''': {}}
for layer_i in range(__UpperCAmelCase ):
__UpperCamelCase = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
if model_size == "7B":
# Unsharded
__UpperCamelCase = {
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.
__UpperCamelCase = {
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(),
}
__UpperCamelCase = permute(
torch.cat(
[
loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for i in range(__UpperCAmelCase )
] , dim=0 , ).reshape(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCamelCase = permute(
torch.cat(
[
loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for i in range(__UpperCAmelCase )
] , dim=0 , ).reshape(__UpperCAmelCase , __UpperCAmelCase ) , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , )
__UpperCamelCase = torch.cat(
[
loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
for i in range(__UpperCAmelCase )
] , dim=0 , ).reshape(__UpperCAmelCase , __UpperCAmelCase )
__UpperCamelCase = torch.cat(
[loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(__UpperCAmelCase )] , dim=1 )
__UpperCamelCase = torch.cat(
[loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(__UpperCAmelCase )] , dim=0 )
__UpperCamelCase = torch.cat(
[loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(__UpperCAmelCase )] , dim=1 )
__UpperCamelCase = torch.cat(
[loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(__UpperCAmelCase )] , dim=0 )
__UpperCamelCase = inv_freq
for k, v in state_dict.items():
__UpperCamelCase = filename
param_count += v.numel()
torch.save(__UpperCAmelCase , os.path.join(__UpperCAmelCase , __UpperCAmelCase ) )
__UpperCamelCase = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
if model_size == "7B":
# Unsharded
__UpperCamelCase = {
'''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
'''model.norm.weight''': loaded['''norm.weight'''],
'''lm_head.weight''': loaded['''output.weight'''],
}
else:
__UpperCamelCase = {
'''model.norm.weight''': loaded[0]['''norm.weight'''],
'''model.embed_tokens.weight''': torch.cat(
[loaded[i]['''tok_embeddings.weight'''] for i in range(__UpperCAmelCase )] , dim=1 ),
'''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(__UpperCAmelCase )] , dim=0 ),
}
for k, v in state_dict.items():
__UpperCamelCase = filename
param_count += v.numel()
torch.save(__UpperCAmelCase , os.path.join(__UpperCAmelCase , __UpperCAmelCase ) )
# Write configs
__UpperCamelCase = {'''total_size''': param_count * 2}
write_json(__UpperCAmelCase , os.path.join(__UpperCAmelCase , '''pytorch_model.bin.index.json''' ) )
__UpperCamelCase = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
__UpperCamelCase = params['''multiple_of'''] if '''multiple_of''' in params else 256
__UpperCamelCase = LlamaConfig(
hidden_size=__UpperCAmelCase , intermediate_size=compute_intermediate_size(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) , num_attention_heads=params['''n_heads'''] , num_hidden_layers=params['''n_layers'''] , rms_norm_eps=params['''norm_eps'''] , num_key_value_heads=__UpperCAmelCase , )
config.save_pretrained(__UpperCAmelCase )
# 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.''' )
__UpperCamelCase = LlamaForCausalLM.from_pretrained(__UpperCAmelCase , torch_dtype=torch.floataa , low_cpu_mem_usage=__UpperCAmelCase )
# Avoid saving this as part of the config.
del model.config._name_or_path
print('''Saving in the Transformers format.''' )
model.save_pretrained(__UpperCAmelCase , safe_serialization=__UpperCAmelCase )
shutil.rmtree(__UpperCAmelCase )
def A_ ( snake_case : str , snake_case : str ) -> str:
'''simple docstring'''
__UpperCamelCase = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}." )
__UpperCamelCase = tokenizer_class(__UpperCAmelCase )
tokenizer.save_pretrained(__UpperCAmelCase )
def A_ ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase = 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=__UpperCAmelCase , help='''Whether or not to save using `safetensors`.''' )
__UpperCamelCase = 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 , )
__UpperCamelCase = os.path.join(args.input_dir , '''tokenizer.model''' )
write_tokenizer(args.output_dir , __UpperCAmelCase )
if __name__ == "__main__":
main()
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|
'''simple docstring'''
import re
def __magic_name__ ( __UpperCAmelCase ) -> bool:
'''simple docstring'''
snake_case_ = re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(__UpperCAmelCase, __UpperCAmelCase ) )
if __name__ == "__main__":
a : Any = '0094702343221'
print(is_sri_lankan_phone_number(phone))
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|
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCAmelCase__ ( ) -> List[Any]:
'''simple docstring'''
lowercase = HfArgumentParser(__UpperCAmelCase )
lowercase = parser.parse_args_into_dataclasses()[0]
lowercase = TensorFlowBenchmark(args=__UpperCAmelCase )
try:
lowercase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase = """Arg --no_{0} is no longer used, please use --no-{0} instead."""
lowercase = """ """.join(str(__UpperCAmelCase ).split(""" """ )[:-1] )
lowercase = """"""
lowercase = eval(str(__UpperCAmelCase ).split(""" """ )[-1] )
lowercase = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
lowercase = full_error_msg + begin_error_msg + str(__UpperCAmelCase )
raise ValueError(__UpperCAmelCase )
benchmark.run()
if __name__ == "__main__":
main()
| 197
|
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
a : Union[str, Any] = True
except (ImportError, ModuleNotFoundError):
a : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
re.sub('''<n>''', '''''', __UpperCAmelCase ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(__UpperCAmelCase ) )
| 56
| 0
|
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