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import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase_ = logging.get_logger(__name__)
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Dict:
lowerCAmelCase__ : Tuple = UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase , config=UpperCamelCase )
lowerCAmelCase__ : Any = downstream_dict['''projector.weight''']
lowerCAmelCase__ : List[Any] = downstream_dict['''projector.bias''']
lowerCAmelCase__ : Any = downstream_dict['''model.post_net.linear.weight''']
lowerCAmelCase__ : str = downstream_dict['''model.post_net.linear.bias''']
return model
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : Tuple = UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase , config=UpperCamelCase )
lowerCAmelCase__ : List[Any] = downstream_dict['''model.linear.weight''']
lowerCAmelCase__ : Union[str, Any] = downstream_dict['''model.linear.bias''']
return model
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> List[Any]:
lowerCAmelCase__ : int = UniSpeechSatForXVector.from_pretrained(UpperCamelCase , config=UpperCamelCase )
lowerCAmelCase__ : Dict = downstream_dict['''connector.weight''']
lowerCAmelCase__ : Optional[int] = downstream_dict['''connector.bias''']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
lowerCAmelCase__ : List[str] = downstream_dict[
F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
]
lowerCAmelCase__ : Any = downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
lowerCAmelCase__ : str = downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
lowerCAmelCase__ : List[Any] = downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
lowerCAmelCase__ : Tuple = downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
lowerCAmelCase__ : Optional[Any] = downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
lowerCAmelCase__ : Dict = downstream_dict['''objective.W''']
return model
@torch.no_grad()
def __lowerCAmelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Any:
lowerCAmelCase__ : str = torch.load(UpperCamelCase , map_location='''cpu''' )
lowerCAmelCase__ : Any = checkpoint['''Downstream''']
lowerCAmelCase__ : List[str] = UniSpeechSatConfig.from_pretrained(UpperCamelCase )
lowerCAmelCase__ : str = WavaVecaFeatureExtractor.from_pretrained(
UpperCamelCase , return_attention_mask=UpperCamelCase , do_normalize=UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = hf_config.architectures[0]
if arch.endswith('''ForSequenceClassification''' ):
lowerCAmelCase__ : Any = convert_classification(UpperCamelCase , UpperCamelCase , UpperCamelCase )
elif arch.endswith('''ForAudioFrameClassification''' ):
lowerCAmelCase__ : Dict = convert_diarization(UpperCamelCase , UpperCamelCase , UpperCamelCase )
elif arch.endswith('''ForXVector''' ):
lowerCAmelCase__ : Any = convert_xvector(UpperCamelCase , UpperCamelCase , UpperCamelCase )
else:
raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}""" )
if hf_config.use_weighted_layer_sum:
lowerCAmelCase__ : List[Any] = checkpoint['''Featurizer''']['''weights''']
hf_feature_extractor.save_pretrained(UpperCamelCase )
hf_model.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
)
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
lowerCAmelCase_ = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 678
|
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( _lowercase , unittest.TestCase ):
A__ = PegasusTokenizer
A__ = PegasusTokenizerFast
A__ = True
A__ = True
def __magic_name__( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ : Union[str, Any] = PegasusTokenizer(__UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __magic_name__( self ):
return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )
def __magic_name__( self , **__UpperCAmelCase ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def __magic_name__( self , __UpperCAmelCase ):
return ("This is a test", "This is a test")
def __magic_name__( self ):
lowerCAmelCase__ : Optional[Any] = '''</s>'''
lowerCAmelCase__ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase ) , __UpperCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase ) , __UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<pad>''' )
self.assertEqual(vocab_keys[1] , '''</s>''' )
self.assertEqual(vocab_keys[-1] , '''v''' )
self.assertEqual(len(__UpperCAmelCase ) , 1103 )
def __magic_name__( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1103 )
def __magic_name__( self ):
lowerCAmelCase__ : int = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ : Tuple = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ : int = (
'''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
''' </s> <pad> <pad> <pad>'''
)
lowerCAmelCase__ : Any = rust_tokenizer([raw_input_str] , return_tensors=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ).input_ids[0]
lowerCAmelCase__ : Dict = py_tokenizer([raw_input_str] , return_tensors=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ).input_ids[0]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : Any = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
lowerCAmelCase__ : List[str] = '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
lowerCAmelCase__ : Tuple = [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
lowerCAmelCase__ : Tuple = tokenizer([raw_input_str] , return_tensors=__UpperCAmelCase ).input_ids[0]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def __magic_name__( self ):
lowerCAmelCase__ : Dict = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 9_6103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1024
lowerCAmelCase__ : str = '''To ensure a smooth flow of bank resolutions.'''
lowerCAmelCase__ : int = [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
lowerCAmelCase__ : List[Any] = tokenizer([raw_input_str] , return_tensors=__UpperCAmelCase ).input_ids[0]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def __magic_name__( self ):
lowerCAmelCase__ : Optional[int] = ['''This is going to be way too long.''' * 150, '''short example''']
lowerCAmelCase__ : List[str] = ['''not super long but more than 5 tokens''', '''tiny''']
lowerCAmelCase__ : Tuple = self._large_tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors='''pt''' )
lowerCAmelCase__ : Optional[int] = self._large_tokenizer(
text_target=__UpperCAmelCase , max_length=5 , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 1024)
assert batch.attention_mask.shape == (2, 1024)
assert targets["input_ids"].shape == (2, 5)
assert len(__UpperCAmelCase ) == 2 # input_ids, attention_mask.
@slow
def __magic_name__( self ):
# fmt: off
lowerCAmelCase__ : Optional[int] = {'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__UpperCAmelCase , model_name='''google/bigbird-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , )
@require_sentencepiece
@require_tokenizers
class _lowerCAmelCase ( _lowercase , unittest.TestCase ):
A__ = PegasusTokenizer
A__ = PegasusTokenizerFast
A__ = True
A__ = True
def __magic_name__( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase__ : List[Any] = PegasusTokenizer(__UpperCAmelCase , offset=0 , mask_token_sent=__UpperCAmelCase , mask_token='''[MASK]''' )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def __magic_name__( self ):
return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )
def __magic_name__( self , **__UpperCAmelCase ):
return PegasusTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def __magic_name__( self , __UpperCAmelCase ):
return ("This is a test", "This is a test")
def __magic_name__( self ):
lowerCAmelCase__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ : int = self.tokenizer_class.from_pretrained(self.tmpdirname )
lowerCAmelCase__ : str = (
'''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
''' <pad> <pad> <pad>'''
)
lowerCAmelCase__ : Optional[Any] = rust_tokenizer([raw_input_str] , return_tensors=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ).input_ids[0]
lowerCAmelCase__ : int = py_tokenizer([raw_input_str] , return_tensors=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase ).input_ids[0]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
@require_torch
def __magic_name__( self ):
lowerCAmelCase__ : Optional[Any] = ['''This is going to be way too long.''' * 1000, '''short example''']
lowerCAmelCase__ : int = ['''not super long but more than 5 tokens''', '''tiny''']
lowerCAmelCase__ : Tuple = self._large_tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors='''pt''' )
lowerCAmelCase__ : Tuple = self._large_tokenizer(
text_target=__UpperCAmelCase , max_length=5 , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors='''pt''' )
assert batch.input_ids.shape == (2, 4096)
assert batch.attention_mask.shape == (2, 4096)
assert targets["input_ids"].shape == (2, 5)
assert len(__UpperCAmelCase ) == 2 # input_ids, attention_mask.
def __magic_name__( self ):
lowerCAmelCase__ : List[str] = (
'''This is an example string that is used to test the original TF implementation against the HF'''
''' implementation'''
)
lowerCAmelCase__ : Union[str, Any] = self._large_tokenizer(__UpperCAmelCase ).input_ids
self.assertListEqual(
__UpperCAmelCase , [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1] , )
| 678
| 1
|
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase : str = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowerCamelCase : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 713
|
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _UpperCamelCase (enum.Enum ):
snake_case_ = 0
snake_case_ = 1
snake_case_ = 2
@add_end_docstrings(a_ )
class _UpperCamelCase (a_ ):
snake_case_ = """
In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
begging for his blessing. <eod> </s> <eos>
"""
def __init__( self , *__UpperCamelCase , **__UpperCamelCase )-> Optional[int]:
super().__init__(*__UpperCamelCase , **__UpperCamelCase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
__lowerCAmelCase = None
if self.model.config.prefix is not None:
__lowerCAmelCase = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
__lowerCAmelCase = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._sanitize_parameters(prefix=__UpperCamelCase , **self._forward_params )
__lowerCAmelCase = {**self._preprocess_params, **preprocess_params}
__lowerCAmelCase = {**self._forward_params, **forward_params}
def __UpperCAmelCase ( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , )-> int:
__lowerCAmelCase = {}
if prefix is not None:
__lowerCAmelCase = prefix
if prefix:
__lowerCAmelCase = self.tokenizer(
__UpperCamelCase , padding=__UpperCamelCase , add_special_tokens=__UpperCamelCase , return_tensors=self.framework )
__lowerCAmelCase = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
F"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
" [None, 'hole']" )
__lowerCAmelCase = handle_long_generation
preprocess_params.update(__UpperCamelCase )
__lowerCAmelCase = generate_kwargs
__lowerCAmelCase = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
__lowerCAmelCase = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
__lowerCAmelCase = ReturnType.TENSORS
if return_type is not None:
__lowerCAmelCase = return_type
if clean_up_tokenization_spaces is not None:
__lowerCAmelCase = clean_up_tokenization_spaces
if stop_sequence is not None:
__lowerCAmelCase = self.tokenizer.encode(__UpperCamelCase , add_special_tokens=__UpperCamelCase )
if len(__UpperCamelCase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
__lowerCAmelCase = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __UpperCAmelCase ( self , *__UpperCamelCase , **__UpperCamelCase )-> List[str]:
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*__UpperCamelCase , **__UpperCamelCase )
def __call__( self , __UpperCamelCase , **__UpperCamelCase )-> List[Any]:
return super().__call__(__UpperCamelCase , **__UpperCamelCase )
def __UpperCAmelCase ( self , __UpperCamelCase , __UpperCamelCase="" , __UpperCamelCase=None , **__UpperCamelCase )-> int:
__lowerCAmelCase = self.tokenizer(
prefix + prompt_text , padding=__UpperCamelCase , add_special_tokens=__UpperCamelCase , return_tensors=self.framework )
__lowerCAmelCase = prompt_text
if handle_long_generation == "hole":
__lowerCAmelCase = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
__lowerCAmelCase = generate_kwargs["max_new_tokens"]
else:
__lowerCAmelCase = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
__lowerCAmelCase = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
__lowerCAmelCase = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
__lowerCAmelCase = inputs["attention_mask"][:, -keep_length:]
return inputs
def __UpperCAmelCase ( self , __UpperCamelCase , **__UpperCamelCase )-> Optional[Any]:
__lowerCAmelCase = model_inputs["input_ids"]
__lowerCAmelCase = model_inputs.get("attention_mask" , __UpperCamelCase )
# Allow empty prompts
if input_ids.shape[1] == 0:
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = 1
else:
__lowerCAmelCase = input_ids.shape[0]
__lowerCAmelCase = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
__lowerCAmelCase = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
__lowerCAmelCase = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
__lowerCAmelCase = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
__lowerCAmelCase = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
__lowerCAmelCase = self.model.generate(input_ids=__UpperCamelCase , attention_mask=__UpperCamelCase , **__UpperCamelCase )
__lowerCAmelCase = generated_sequence.shape[0]
if self.framework == "pt":
__lowerCAmelCase = generated_sequence.reshape(__UpperCamelCase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
__lowerCAmelCase = tf.reshape(__UpperCamelCase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def __UpperCAmelCase ( self , __UpperCamelCase , __UpperCamelCase=ReturnType.FULL_TEXT , __UpperCamelCase=True )-> Any:
__lowerCAmelCase = model_outputs["generated_sequence"][0]
__lowerCAmelCase = model_outputs["input_ids"]
__lowerCAmelCase = model_outputs["prompt_text"]
__lowerCAmelCase = generated_sequence.numpy().tolist()
__lowerCAmelCase = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
__lowerCAmelCase = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
__lowerCAmelCase = self.tokenizer.decode(
__UpperCamelCase , skip_special_tokens=__UpperCamelCase , clean_up_tokenization_spaces=__UpperCamelCase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
__lowerCAmelCase = 0
else:
__lowerCAmelCase = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=__UpperCamelCase , clean_up_tokenization_spaces=__UpperCamelCase , ) )
if return_type == ReturnType.FULL_TEXT:
__lowerCAmelCase = prompt_text + text[prompt_length:]
else:
__lowerCAmelCase = text[prompt_length:]
__lowerCAmelCase = {"generated_text": all_text}
records.append(__UpperCamelCase )
return records
| 290
| 0
|
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
A_ : str = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class __snake_case ( snake_case__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = ReformerTokenizer
lowerCamelCase__ = ReformerTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = True
def __UpperCamelCase ( self ):
super().setUp()
snake_case__ : Union[str, Any] = ReformerTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCamelCase ( self ):
snake_case__ : str = """<s>"""
snake_case__ : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self ):
snake_case__ : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , 1_0_0_0 )
def __UpperCamelCase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 1_0_0_0 )
def __UpperCamelCase ( self ):
if not self.test_rust_tokenizer:
return
snake_case__ : Optional[Any] = self.get_tokenizer()
snake_case__ : int = self.get_rust_tokenizer()
snake_case__ : Optional[int] = """I was born in 92000, and this is falsé."""
snake_case__ : str = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
snake_case__ : Any = rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
snake_case__ : Dict = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
snake_case__ : List[Any] = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
snake_case__ : Any = self.get_rust_tokenizer()
snake_case__ : Tuple = tokenizer.encode(__SCREAMING_SNAKE_CASE )
snake_case__ : str = rust_tokenizer.encode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE=1_5 ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
snake_case__ : Dict = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
# Simple input
snake_case__ : int = """This is a simple input"""
snake_case__ : Optional[Any] = ["""This is a simple input 1""", """This is a simple input 2"""]
snake_case__ : Optional[int] = ("""This is a simple input""", """This is a pair""")
snake_case__ : Optional[Any] = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Simple input
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Simple input
self.assertRaises(
__SCREAMING_SNAKE_CASE , tokenizer_r.batch_encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding="""max_length""" , )
# Pair input
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Pair input
self.assertRaises(__SCREAMING_SNAKE_CASE , tokenizer_r.encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding="""max_length""" )
# Pair input
self.assertRaises(
__SCREAMING_SNAKE_CASE , tokenizer_r.batch_encode_plus , __SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , padding="""max_length""" , )
def __UpperCamelCase ( self ):
pass
def __UpperCamelCase ( self ):
snake_case__ : int = ReformerTokenizer(__SCREAMING_SNAKE_CASE , keep_accents=__SCREAMING_SNAKE_CASE )
snake_case__ : str = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__SCREAMING_SNAKE_CASE , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] , )
snake_case__ : Union[str, Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
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""",
""".""",
] , )
snake_case__ : Tuple = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] , )
snake_case__ : Tuple = tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE )
self.assertListEqual(
__SCREAMING_SNAKE_CASE , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def __UpperCamelCase ( self ):
return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" )
@slow
def __UpperCamelCase ( self ):
snake_case__ : int = """Hello World!"""
snake_case__ : Union[str, Any] = [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@slow
def __UpperCamelCase ( self ):
snake_case__ : Union[str, Any] = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
snake_case__ : str = [
1_0_8,
2_6_5,
2_4,
1_1_1,
4,
2_5_8,
1_5_6,
3_5,
2_8,
2_7_5,
3,
2_5_9,
2_9_7,
2_6_0,
8_4,
4,
3_5,
1_1_0,
4_4,
8,
2_5_9,
9_1,
2_6_8,
2_1,
1_1,
2_0_9,
2_7_4,
1_0_9,
2_6_6,
2_7_7,
1_1_7,
8_6,
9_3,
3_1_5,
2_5_8,
2_7_8,
2_5_8,
2_7_7,
2_5_8,
0,
2_5_8,
2_8_8,
2_5_8,
3_1_9,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
0,
2_5_8,
2_8_7,
2_5_8,
3_1_5,
2_5_8,
2_8_9,
2_5_8,
2_7_8,
9_9,
2_6_9,
2_6_6,
2_6_2,
8,
2_5_9,
2_4_1,
4,
2_1_7,
2_3_0,
2_6_8,
2_6_6,
5_5,
1_6_8,
1_0_6,
7_5,
1_9_3,
2_6_6,
2_2_3,
2_7,
4_9,
2_6,
2_8_2,
2_5,
2_6_4,
2_9_9,
1_9,
2_6,
0,
2_5_8,
2_7_7,
1_1_7,
8_6,
9_3,
1_7_6,
1_8_3,
2_7_0,
1_1,
2_6_2,
4_2,
6_1,
2_6_5,
]
self.assertListEqual(__SCREAMING_SNAKE_CASE , self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE ) )
@require_torch
@slow
def __UpperCamelCase ( self ):
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
snake_case__ : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:1_0]
snake_case__ : str = """ """.join(__SCREAMING_SNAKE_CASE )
snake_case__ : Any = self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
snake_case__ : Dict = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="""pt""" )
snake_case__ : str = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
snake_case__ : Any = encoded_sequence["""input_ids"""].shape
snake_case__ : Optional[Any] = ReformerModel(__SCREAMING_SNAKE_CASE )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**__SCREAMING_SNAKE_CASE )
model(**__SCREAMING_SNAKE_CASE )
@slow
def __UpperCamelCase ( self ):
snake_case__ : Optional[Any] = {"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
snake_case__ : Dict = [
"""This is a very simple sentence.""",
"""The quick brown fox jumps over the lazy dog.""",
]
self.tokenizer_integration_test_util(
expected_encoding=__SCREAMING_SNAKE_CASE , model_name="""google/reformer-crime-and-punishment""" , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""" , padding=__SCREAMING_SNAKE_CASE , sequences=__SCREAMING_SNAKE_CASE , )
| 38
|
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.g4dn.xlarge""",
"""results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
},
{
"""framework""": """tensorflow""",
"""script""": """run_tf.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.g4dn.xlarge""",
"""results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
},
] )
class __UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) -> int:
"""simple docstring"""
if self.framework == "pytorch":
subprocess.run(
f'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split() , encoding="utf-8" , check=SCREAMING_SNAKE_CASE , )
assert hasattr(self , "env" )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE=1 ) -> Tuple:
"""simple docstring"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f'''{self.env.base_job_name}-single''' , instance_count=SCREAMING_SNAKE_CASE , instance_type=self.instance_type , debugger_hook_config=SCREAMING_SNAKE_CASE , hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version="py36" , )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
TrainingJobAnalytics(SCREAMING_SNAKE_CASE ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''' )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
# create estimator
UpperCamelCase = self.create_estimator()
# run training
estimator.fit()
# result dataframe
UpperCamelCase = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCamelCase = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
UpperCamelCase = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCamelCase = (
Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds" , 999999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy )
assert all(t <= self.results["eval_loss"] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f'''{estimator.latest_training_job.name}.json''' , "w" ) as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} , SCREAMING_SNAKE_CASE )
| 606
| 0
|
class __A :
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = name
lowerCamelCase__ = value
lowerCamelCase__ = weight
def __repr__( self ):
'''simple docstring'''
return F'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.value
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.name
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.weight
def __lowerCamelCase ( self ):
'''simple docstring'''
return self.value / self.weight
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
menu.append(Things(name[i] ,value[i] ,weight[i] ) )
return menu
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = sorted(SCREAMING_SNAKE_CASE_ ,key=SCREAMING_SNAKE_CASE_ ,reverse=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = []
lowerCamelCase__ , lowerCamelCase__ = 0.0, 0.0
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def lowerCAmelCase__() -> Tuple:
'''simple docstring'''
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 714
|
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Tuple:
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
return _map_with_joblib(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = num_proc if num_proc <= len(__snake_case ) else len(__snake_case )
lowerCamelCase__ = [] # We organize the splits ourselve (contiguous splits)
for index in range(__snake_case ):
lowerCamelCase__ = len(__snake_case ) // num_proc
lowerCamelCase__ = len(__snake_case ) % num_proc
lowerCamelCase__ = div * index + min(__snake_case ,__snake_case )
lowerCamelCase__ = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(__snake_case ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F'Error dividing inputs iterable among processes. '
F'Total number of objects {len(__snake_case )}, '
F'length: {sum(len(i[1] ) for i in split_kwds )}' )
logger.info(
F'Spawning {num_proc} processes for {len(__snake_case )} objects in slices of {[len(i[1] ) for i in split_kwds]}' )
lowerCamelCase__ , lowerCamelCase__ = None, None
if not disable_tqdm:
lowerCamelCase__ , lowerCamelCase__ = (RLock(),), tqdm.set_lock
with Pool(__snake_case ,initargs=__snake_case ,initializer=__snake_case ) as pool:
lowerCamelCase__ = pool.map(__snake_case ,__snake_case )
logger.info(F'Finished {num_proc} processes' )
lowerCamelCase__ = [obj for proc_res in mapped for obj in proc_res]
logger.info(F'Unpacked {len(__snake_case )} objects' )
return mapped
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> List[str]:
'''simple docstring'''
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=__snake_case ):
return joblib.Parallel()(
joblib.delayed(__snake_case )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
lowerCamelCase__ = None
| 29
| 0
|
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( lowerCamelCase__ : Any, lowerCamelCase__ : Optional[int], lowerCamelCase__ : int, lowerCamelCase__ : Union[str, Any]="attention" ) -> str:
_SCREAMING_SNAKE_CASE : int = params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
_SCREAMING_SNAKE_CASE : str = params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
_SCREAMING_SNAKE_CASE : int = params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
_SCREAMING_SNAKE_CASE : Any = params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
return k, o, q, v
def _lowerCAmelCase ( lowerCamelCase__ : int, lowerCamelCase__ : Optional[int], lowerCamelCase__ : List[Any], lowerCamelCase__ : Optional[int]=False ) -> List[Any]:
if split_mlp_wi:
_SCREAMING_SNAKE_CASE : Optional[Any] = params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
_SCREAMING_SNAKE_CASE : Dict = params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
_SCREAMING_SNAKE_CASE : Union[str, Any] = (wi_a, wi_a)
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']
_SCREAMING_SNAKE_CASE : int = params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
return wi, wo
def _lowerCAmelCase ( lowerCamelCase__ : Union[str, Any], lowerCamelCase__ : List[str], lowerCamelCase__ : List[Any], lowerCamelCase__ : Dict ) -> Optional[int]:
return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']
def _lowerCAmelCase ( lowerCamelCase__ : Dict, *, lowerCamelCase__ : List[str], lowerCamelCase__ : Union[str, Any] ) -> str:
_SCREAMING_SNAKE_CASE : Union[str, Any] = traverse_util.flatten_dict(variables["target"] )
_SCREAMING_SNAKE_CASE : int = {"/".join(lowerCamelCase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_SCREAMING_SNAKE_CASE : Tuple = "encoder/layers_0/mlp/wi_0/kernel" in old
print("Split MLP:", lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = collections.OrderedDict()
# Shared embeddings.
_SCREAMING_SNAKE_CASE : int = old["token_embedder/embedding"]
# Encoder.
for i in range(lowerCamelCase__ ):
# Block i, layer 0 (Self Attention).
_SCREAMING_SNAKE_CASE : Tuple = tax_layer_norm_lookup(lowerCamelCase__, lowerCamelCase__, "encoder", "pre_attention_layer_norm" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = tax_attention_lookup(lowerCamelCase__, lowerCamelCase__, "encoder", "attention" )
_SCREAMING_SNAKE_CASE : Any = layer_norm
_SCREAMING_SNAKE_CASE : Dict = k.T
_SCREAMING_SNAKE_CASE : List[str] = o.T
_SCREAMING_SNAKE_CASE : Dict = q.T
_SCREAMING_SNAKE_CASE : List[str] = v.T
# Block i, layer 1 (MLP).
_SCREAMING_SNAKE_CASE : Dict = tax_layer_norm_lookup(lowerCamelCase__, lowerCamelCase__, "encoder", "pre_mlp_layer_norm" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Tuple = tax_mlp_lookup(lowerCamelCase__, lowerCamelCase__, "encoder", lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm
if split_mlp_wi:
_SCREAMING_SNAKE_CASE : str = wi[0].T
_SCREAMING_SNAKE_CASE : Optional[int] = wi[1].T
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = wi.T
_SCREAMING_SNAKE_CASE : Any = wo.T
_SCREAMING_SNAKE_CASE : Optional[int] = old[
"encoder/relpos_bias/rel_embedding"
].T
_SCREAMING_SNAKE_CASE : str = old["encoder/encoder_norm/scale"]
if not is_encoder_only:
# Decoder.
for i in range(lowerCamelCase__ ):
# Block i, layer 0 (Self Attention).
_SCREAMING_SNAKE_CASE : Union[str, Any] = tax_layer_norm_lookup(lowerCamelCase__, lowerCamelCase__, "decoder", "pre_self_attention_layer_norm" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = tax_attention_lookup(lowerCamelCase__, lowerCamelCase__, "decoder", "self_attention" )
_SCREAMING_SNAKE_CASE : Optional[int] = layer_norm
_SCREAMING_SNAKE_CASE : List[Any] = k.T
_SCREAMING_SNAKE_CASE : Union[str, Any] = o.T
_SCREAMING_SNAKE_CASE : Any = q.T
_SCREAMING_SNAKE_CASE : Tuple = v.T
# Block i, layer 1 (Cross Attention).
_SCREAMING_SNAKE_CASE : List[Any] = tax_layer_norm_lookup(lowerCamelCase__, lowerCamelCase__, "decoder", "pre_cross_attention_layer_norm" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = tax_attention_lookup(lowerCamelCase__, lowerCamelCase__, "decoder", "encoder_decoder_attention" )
_SCREAMING_SNAKE_CASE : Dict = layer_norm
_SCREAMING_SNAKE_CASE : Union[str, Any] = k.T
_SCREAMING_SNAKE_CASE : Optional[int] = o.T
_SCREAMING_SNAKE_CASE : List[Any] = q.T
_SCREAMING_SNAKE_CASE : List[Any] = v.T
# Block i, layer 2 (MLP).
_SCREAMING_SNAKE_CASE : int = tax_layer_norm_lookup(lowerCamelCase__, lowerCamelCase__, "decoder", "pre_mlp_layer_norm" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = tax_mlp_lookup(lowerCamelCase__, lowerCamelCase__, "decoder", lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : List[str] = layer_norm
if split_mlp_wi:
_SCREAMING_SNAKE_CASE : Tuple = wi[0].T
_SCREAMING_SNAKE_CASE : Any = wi[1].T
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = wi.T
_SCREAMING_SNAKE_CASE : Any = wo.T
_SCREAMING_SNAKE_CASE : int = old["decoder/decoder_norm/scale"]
_SCREAMING_SNAKE_CASE : str = old[
"decoder/relpos_bias/rel_embedding"
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_SCREAMING_SNAKE_CASE : Any = old["decoder/logits_dense/kernel"].T
return new
def _lowerCAmelCase ( lowerCamelCase__ : Optional[int], lowerCamelCase__ : Tuple ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : Optional[Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_SCREAMING_SNAKE_CASE : Union[str, Any] = state_dict["shared.weight"]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_SCREAMING_SNAKE_CASE : Any = state_dict["shared.weight"]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("Using shared word embeddings as lm_head." )
_SCREAMING_SNAKE_CASE : Dict = state_dict["shared.weight"]
return state_dict
def _lowerCAmelCase ( lowerCamelCase__ : List[Any], lowerCamelCase__ : int, lowerCamelCase__ : Optional[Any], lowerCamelCase__ : List[str] ) -> int:
_SCREAMING_SNAKE_CASE : List[Any] = checkpoints.load_tax_checkpoint(lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : List[str] = convert_tax_to_pytorch(lowerCamelCase__, num_layers=config.num_layers, is_encoder_only=lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : Optional[Any] = make_state_dict(lowerCamelCase__, lowerCamelCase__ )
model.load_state_dict(lowerCamelCase__, strict=lowerCamelCase__ )
def _lowerCAmelCase ( lowerCamelCase__ : Optional[int], lowerCamelCase__ : Tuple, lowerCamelCase__ : Tuple, lowerCamelCase__ : Union[str, Any] = False ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE : Optional[Any] = TaConfig.from_json_file(lowerCamelCase__ )
print(f'''Building PyTorch model from configuration: {config}''' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_SCREAMING_SNAKE_CASE : Tuple = TaEncoderModel(lowerCamelCase__ )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = TaForConditionalGeneration(lowerCamelCase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
model.save_pretrained(lowerCamelCase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowerCamelCase__ )
print("Done" )
if __name__ == "__main__":
lowercase_ : Optional[int] = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
lowercase_ : Union[str, Any] = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 572
|
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__magic_name__ =argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument(
'''--original_config_file''',
type=str,
required=True,
help='''The YAML config file corresponding to the original architecture.''',
)
parser.add_argument(
'''--num_in_channels''',
default=None,
type=int,
help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
)
parser.add_argument(
'''--image_size''',
default=512,
type=int,
help=(
'''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
''' Base. Use 768 for Stable Diffusion v2.'''
),
)
parser.add_argument(
'''--extract_ema''',
action='''store_true''',
help=(
'''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
),
)
parser.add_argument(
'''--upcast_attention''',
action='''store_true''',
help=(
'''Whether the attention computation should always be upcasted. This is necessary when running stable'''
''' diffusion 2.1.'''
),
)
parser.add_argument(
'''--from_safetensors''',
action='''store_true''',
help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
)
parser.add_argument(
'''--to_safetensors''',
action='''store_true''',
help='''Whether to store pipeline in safetensors format or not.''',
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
def __UpperCamelCase ( A ):
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(f"could not parse string as bool {string}" )
parser.add_argument(
'''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
)
parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)
__magic_name__ =parser.parse_args()
__magic_name__ =download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 415
| 0
|
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
lowerCAmelCase_ = NewType('''DataClass''', Any)
lowerCAmelCase_ = NewType('''DataClassType''', Any)
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> Any:
if isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise ArgumentTypeError(
F"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).""" )
def lowerCamelCase_(__SCREAMING_SNAKE_CASE )-> Callable[[str], Any]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = {str(__SCREAMING_SNAKE_CASE ): choice for choice in choices}
return lambda __SCREAMING_SNAKE_CASE : str_to_choice.get(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCamelCase_(*,
__SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = dataclasses.MISSING , __SCREAMING_SNAKE_CASE = dataclasses.MISSING , __SCREAMING_SNAKE_CASE = None , **__SCREAMING_SNAKE_CASE , )-> dataclasses.Field:
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_SCREAMING_SNAKE_CASE : Optional[int] = {}
if aliases is not None:
_SCREAMING_SNAKE_CASE : Dict = aliases
if help is not None:
_SCREAMING_SNAKE_CASE : Optional[int] = help
return dataclasses.field(metadata=__SCREAMING_SNAKE_CASE , default=__SCREAMING_SNAKE_CASE , default_factory=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
class _snake_case ( __snake_case ):
"""simple docstring"""
a = 42
def __init__( self : Union[str, Any] , _A : Union[DataClassType, Iterable[DataClassType]] , **_A : str):
"""simple docstring"""
if "formatter_class" not in kwargs:
_SCREAMING_SNAKE_CASE : Optional[int] = ArgumentDefaultsHelpFormatter
super().__init__(**_A)
if dataclasses.is_dataclass(_A):
_SCREAMING_SNAKE_CASE : List[str] = [dataclass_types]
_SCREAMING_SNAKE_CASE : Optional[Any] = list(_A)
for dtype in self.dataclass_types:
self._add_dataclass_arguments(_A)
@staticmethod
def _lowerCAmelCase ( _A : ArgumentParser , _A : dataclasses.Field):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Any = f"""--{field.name}"""
_SCREAMING_SNAKE_CASE : int = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , _A):
raise RuntimeError(
"""Unresolved type detected, which should have been done with the help of """
"""`typing.get_type_hints` method by default""")
_SCREAMING_SNAKE_CASE : Optional[int] = kwargs.pop("""aliases""" , [])
if isinstance(_A , _A):
_SCREAMING_SNAKE_CASE : int = [aliases]
_SCREAMING_SNAKE_CASE : Tuple = getattr(field.type , """__origin__""" , field.type)
if origin_type is Union or (hasattr(_A , """UnionType""") and isinstance(_A , types.UnionType)):
if str not in field.type.__args__ and (
len(field.type.__args__) != 2 or type(_A) not in field.type.__args__
):
raise ValueError(
"""Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
""" the argument parser only supports one type per argument."""
f""" Problem encountered in field '{field.name}'.""")
if type(_A) not in field.type.__args__:
# filter `str` in Union
_SCREAMING_SNAKE_CASE : Union[str, Any] = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_SCREAMING_SNAKE_CASE : Optional[Any] = getattr(field.type , """__origin__""" , field.type)
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_SCREAMING_SNAKE_CASE : int = (
field.type.__args__[0] if isinstance(_A , field.type.__args__[1]) else field.type.__args__[1]
)
_SCREAMING_SNAKE_CASE : Optional[Any] = getattr(field.type , """__origin__""" , field.type)
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_SCREAMING_SNAKE_CASE : List[Any] = {}
if origin_type is Literal or (isinstance(field.type , _A) and issubclass(field.type , _A)):
if origin_type is Literal:
_SCREAMING_SNAKE_CASE : Optional[Any] = field.type.__args__
else:
_SCREAMING_SNAKE_CASE : int = [x.value for x in field.type]
_SCREAMING_SNAKE_CASE : List[str] = make_choice_type_function(kwargs["""choices"""])
if field.default is not dataclasses.MISSING:
_SCREAMING_SNAKE_CASE : Any = field.default
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_SCREAMING_SNAKE_CASE : int = copy(_A)
# Hack because type=bool in argparse does not behave as we want.
_SCREAMING_SNAKE_CASE : Optional[Any] = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_SCREAMING_SNAKE_CASE : Dict = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_SCREAMING_SNAKE_CASE : str = default
# This tells argparse we accept 0 or 1 value after --field_name
_SCREAMING_SNAKE_CASE : str = """?"""
# This is the value that will get picked if we do --field_name (without value)
_SCREAMING_SNAKE_CASE : List[Any] = True
elif isclass(_A) and issubclass(_A , _A):
_SCREAMING_SNAKE_CASE : Tuple = field.type.__args__[0]
_SCREAMING_SNAKE_CASE : Optional[int] = """+"""
if field.default_factory is not dataclasses.MISSING:
_SCREAMING_SNAKE_CASE : Union[str, Any] = field.default_factory()
elif field.default is dataclasses.MISSING:
_SCREAMING_SNAKE_CASE : Optional[Any] = True
else:
_SCREAMING_SNAKE_CASE : List[str] = field.type
if field.default is not dataclasses.MISSING:
_SCREAMING_SNAKE_CASE : int = field.default
elif field.default_factory is not dataclasses.MISSING:
_SCREAMING_SNAKE_CASE : Union[str, Any] = field.default_factory()
else:
_SCREAMING_SNAKE_CASE : Any = True
parser.add_argument(_A , *_A , **_A)
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_SCREAMING_SNAKE_CASE : Union[str, Any] = False
parser.add_argument(f"""--no_{field.name}""" , action="""store_false""" , dest=field.name , **_A)
def _lowerCAmelCase ( self : Optional[Any] , _A : DataClassType):
"""simple docstring"""
if hasattr(_A , """_argument_group_name"""):
_SCREAMING_SNAKE_CASE : List[str] = self.add_argument_group(dtype._argument_group_name)
else:
_SCREAMING_SNAKE_CASE : str = self
try:
_SCREAMING_SNAKE_CASE : Dict[str, type] = get_type_hints(_A)
except NameError:
raise RuntimeError(
f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
"""removing line of `from __future__ import annotations` which opts in Postponed """
"""Evaluation of Annotations (PEP 563)""")
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 1_0) and "unsupported operand type(s) for |" in str(_A):
_SCREAMING_SNAKE_CASE : List[str] = """.""".join(map(_A , sys.version_info[:3]))
raise RuntimeError(
f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
"""line of `from __future__ import annotations` which opts in union types as """
"""`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
"""support Python versions that lower than 3.10, you need to use """
"""`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
"""`X | None`.""") from ex
raise
for field in dataclasses.fields(_A):
if not field.init:
continue
_SCREAMING_SNAKE_CASE : Any = type_hints[field.name]
self._parse_dataclass_field(_A , _A)
def _lowerCAmelCase ( self : Any , _A : Union[str, Any]=None , _A : Union[str, Any]=False , _A : Union[str, Any]=True , _A : Union[str, Any]=None , _A : int=None , ):
"""simple docstring"""
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
_SCREAMING_SNAKE_CASE : List[str] = []
if args_filename:
args_files.append(Path(_A))
elif look_for_args_file and len(sys.argv):
args_files.append(Path(sys.argv[0]).with_suffix(""".args"""))
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_SCREAMING_SNAKE_CASE : int = ArgumentParser()
args_file_parser.add_argument(_A , type=_A , action="""append""")
# Use only remaining args for further parsing (remove the args_file_flag)
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = args_file_parser.parse_known_args(args=_A)
_SCREAMING_SNAKE_CASE : List[str] = vars(_A).get(args_file_flag.lstrip("""-""") , _A)
if cmd_args_file_paths:
args_files.extend([Path(_A) for p in cmd_args_file_paths])
_SCREAMING_SNAKE_CASE : Optional[Any] = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_SCREAMING_SNAKE_CASE : str = file_args + args if args is not None else file_args + sys.argv[1:]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : int = self.parse_known_args(args=_A)
_SCREAMING_SNAKE_CASE : Union[str, Any] = []
for dtype in self.dataclass_types:
_SCREAMING_SNAKE_CASE : Dict = {f.name for f in dataclasses.fields(_A) if f.init}
_SCREAMING_SNAKE_CASE : List[str] = {k: v for k, v in vars(_A).items() if k in keys}
for k in keys:
delattr(_A , _A)
_SCREAMING_SNAKE_CASE : Optional[Any] = dtype(**_A)
outputs.append(_A)
if len(namespace.__dict__) > 0:
# additional namespace.
outputs.append(_A)
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}""")
return (*outputs,)
def _lowerCAmelCase ( self : List[Any] , _A : Dict[str, Any] , _A : bool = False):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : str = set(args.keys())
_SCREAMING_SNAKE_CASE : Dict = []
for dtype in self.dataclass_types:
_SCREAMING_SNAKE_CASE : Union[str, Any] = {f.name for f in dataclasses.fields(_A) if f.init}
_SCREAMING_SNAKE_CASE : Optional[Any] = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys())
_SCREAMING_SNAKE_CASE : Optional[int] = dtype(**_A)
outputs.append(_A)
if not allow_extra_keys and unused_keys:
raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(_A)}""")
return tuple(_A)
def _lowerCAmelCase ( self : Optional[int] , _A : str , _A : bool = False):
"""simple docstring"""
with open(Path(_A) , encoding="""utf-8""") as open_json_file:
_SCREAMING_SNAKE_CASE : List[str] = json.loads(open_json_file.read())
_SCREAMING_SNAKE_CASE : List[Any] = self.parse_dict(_A , allow_extra_keys=_A)
return tuple(_A)
def _lowerCAmelCase ( self : Dict , _A : str , _A : bool = False):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Optional[Any] = self.parse_dict(yaml.safe_load(Path(_A).read_text()) , allow_extra_keys=_A)
return tuple(_A)
| 635
|
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _snake_case ( __snake_case ):
"""simple docstring"""
a = "sew"
def __init__( self : List[Any] , _A : Tuple=3_2 , _A : str=7_6_8 , _A : Dict=1_2 , _A : Tuple=1_2 , _A : Optional[Any]=3_0_7_2 , _A : List[str]=2 , _A : Dict="gelu" , _A : Union[str, Any]=0.1 , _A : Optional[int]=0.1 , _A : Optional[int]=0.1 , _A : Optional[int]=0.0 , _A : str=0.1 , _A : Tuple=0.1 , _A : Optional[int]=0.02 , _A : Dict=1e-5 , _A : str="group" , _A : Tuple="gelu" , _A : Union[str, Any]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , _A : Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _A : Any=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _A : Tuple=False , _A : Tuple=1_2_8 , _A : int=1_6 , _A : Union[str, Any]=True , _A : Optional[Any]=0.05 , _A : List[Any]=1_0 , _A : Union[str, Any]=2 , _A : Tuple=0.0 , _A : Union[str, Any]=1_0 , _A : Optional[int]=0 , _A : Union[str, Any]="mean" , _A : Optional[int]=False , _A : List[Any]=False , _A : int=2_5_6 , _A : str=0 , _A : Optional[int]=1 , _A : List[Any]=2 , **_A : Dict , ):
"""simple docstring"""
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A)
_SCREAMING_SNAKE_CASE : str = hidden_size
_SCREAMING_SNAKE_CASE : Optional[int] = feat_extract_norm
_SCREAMING_SNAKE_CASE : Optional[int] = feat_extract_activation
_SCREAMING_SNAKE_CASE : Dict = list(_A)
_SCREAMING_SNAKE_CASE : int = list(_A)
_SCREAMING_SNAKE_CASE : int = list(_A)
_SCREAMING_SNAKE_CASE : str = conv_bias
_SCREAMING_SNAKE_CASE : Tuple = num_conv_pos_embeddings
_SCREAMING_SNAKE_CASE : List[str] = num_conv_pos_embedding_groups
_SCREAMING_SNAKE_CASE : Tuple = len(self.conv_dim)
_SCREAMING_SNAKE_CASE : Tuple = num_hidden_layers
_SCREAMING_SNAKE_CASE : List[str] = intermediate_size
_SCREAMING_SNAKE_CASE : str = squeeze_factor
_SCREAMING_SNAKE_CASE : Dict = hidden_act
_SCREAMING_SNAKE_CASE : str = num_attention_heads
_SCREAMING_SNAKE_CASE : Dict = hidden_dropout
_SCREAMING_SNAKE_CASE : Tuple = attention_dropout
_SCREAMING_SNAKE_CASE : int = activation_dropout
_SCREAMING_SNAKE_CASE : Any = feat_proj_dropout
_SCREAMING_SNAKE_CASE : str = final_dropout
_SCREAMING_SNAKE_CASE : Union[str, Any] = layerdrop
_SCREAMING_SNAKE_CASE : Any = layer_norm_eps
_SCREAMING_SNAKE_CASE : int = initializer_range
_SCREAMING_SNAKE_CASE : List[Any] = vocab_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect."""
"""It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
f"""but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)"""
f"""= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_SCREAMING_SNAKE_CASE : List[Any] = apply_spec_augment
_SCREAMING_SNAKE_CASE : List[Any] = mask_time_prob
_SCREAMING_SNAKE_CASE : List[str] = mask_time_length
_SCREAMING_SNAKE_CASE : List[Any] = mask_time_min_masks
_SCREAMING_SNAKE_CASE : List[Any] = mask_feature_prob
_SCREAMING_SNAKE_CASE : int = mask_feature_length
_SCREAMING_SNAKE_CASE : List[Any] = mask_feature_min_masks
# ctc loss
_SCREAMING_SNAKE_CASE : int = ctc_loss_reduction
_SCREAMING_SNAKE_CASE : Optional[int] = ctc_zero_infinity
# sequence classification
_SCREAMING_SNAKE_CASE : Dict = use_weighted_layer_sum
_SCREAMING_SNAKE_CASE : List[str] = classifier_proj_size
@property
def _lowerCAmelCase ( self : Any):
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1)
| 635
| 1
|
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _A ( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = IFImgaImgSuperResolutionPipeline
lowerCamelCase : Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
lowerCamelCase : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'} )
lowerCamelCase : Tuple = PipelineTesterMixin.required_optional_params - {'latents'}
def _a ( self : Any ) -> List[Any]:
return self._get_superresolution_dummy_components()
def _a ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=0 ) -> int:
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__UpperCAmelCase =torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__UpperCAmelCase =torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =floats_tensor((1, 3, 32, 32) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =floats_tensor((1, 3, 16, 16) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase ={
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""original_image""": original_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def _a ( self : Optional[Any] ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
def _a ( self : str ) -> str:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def _a ( self : Optional[Any] ) -> str:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def _a ( self : Any ) -> Optional[int]:
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def _a ( self : List[Any] ) -> Tuple:
self._test_save_load_local()
def _a ( self : List[Any] ) -> Union[str, Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
| 68
|
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_UpperCamelCase : List[Any] = re.compile(R'^(?P<major>\d+)' R'\.(?P<minor>\d+)' R'\.(?P<patch>\d+)$')
@total_ordering
@dataclass
class _lowercase:
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = None
def snake_case ( self: Tuple ):
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = _str_to_version_tuple(self.version_str )
def __repr__( self: Any ):
return f"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""
@property
def snake_case ( self: int ):
return self.major, self.minor, self.patch
def snake_case ( self: Union[str, Any] ,a: Tuple ):
if isinstance(a ,a ):
return Version(a )
elif isinstance(a ,a ):
return other
raise TypeError(f"""{other} (type {type(a )}) cannot be compared to version.""" )
def __eq__( self: Dict ,a: str ):
try:
__UpperCAmelCase = self._validate_operand(a )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self: Optional[Any] ,a: List[Any] ):
__UpperCAmelCase = self._validate_operand(a )
return self.tuple < other.tuple
def __hash__( self: List[str] ):
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def snake_case ( cls: Optional[Any] ,a: int ):
__UpperCAmelCase = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def snake_case ( self: Optional[Any] ):
return self.version_str
def __snake_case ( lowerCAmelCase : Dict ):
__UpperCAmelCase = _VERSION_REG.match(lowerCAmelCase )
if not res:
raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits.""" )
return tuple(int(lowerCAmelCase ) for v in [res.group('major' ), res.group('minor' ), res.group('patch' )] )
def __snake_case ( lowerCAmelCase : List[str] ):
return ".".join(str(lowerCAmelCase ) for v in version_tuple )
| 396
| 0
|
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowercase ( __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
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
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] ):
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
a__ = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
@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 __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any ):
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a__ = features.copy() if features else default_expected_features
a__ = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
a__ = ParquetDatasetReader(__lowerCAmelCase , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] ):
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a__ = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase , split=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize('path_type' , [str, list] )
def __lowercase ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str ):
if issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a__ = parquet_path
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
a__ = [parquet_path]
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a__ = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any]=("train",) ):
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
for split in splits:
a__ = dataset_dict[split]
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
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple ):
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
a__ = ParquetDatasetReader(
{'train': parquet_path} , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_parquet_datasetdict(__lowerCAmelCase , __lowerCAmelCase )
@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 __lowercase ( __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[int] ):
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a__ = features.copy() if features else default_expected_features
a__ = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
a__ = ParquetDatasetReader({'train': parquet_path} , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_datasetdict(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize('split' , [None, NamedSplit('train' ), 'train', 'test'] )
def __lowercase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] ):
if split:
a__ = {split: parquet_path}
else:
a__ = 'train'
a__ = {'train': parquet_path, 'test': parquet_path}
a__ = tmp_path / 'cache'
a__ = {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'}
a__ = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_datasetdict(__lowerCAmelCase , __lowerCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] ):
a__ = ParquetDatasetWriter(__lowerCAmelCase , tmp_path / 'foo.parquet' )
assert writer.write() > 0
a__ = pq.ParquetFile(tmp_path / 'foo.parquet' )
a__ = pf.read()
assert dataset.data.table == output_table
def __lowercase ( __lowerCAmelCase : Any , __lowerCAmelCase : Optional[Any] ):
a__ = str(shared_datadir / 'test_image_rgb.jpg' )
a__ = {'image': [image_path]}
a__ = Features({'image': Image()} )
a__ = Dataset.from_dict(__lowerCAmelCase , features=__lowerCAmelCase )
a__ = ParquetDatasetWriter(__lowerCAmelCase , tmp_path / 'foo.parquet' )
assert writer.write() > 0
a__ = Dataset.from_parquet(str(tmp_path / 'foo.parquet' ) )
assert dataset.features == reloaded_dataset.features
a__ = ParquetDatasetReader(str(tmp_path / 'foo.parquet' ) , streaming=__lowerCAmelCase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
'feature, expected' , [
(Features({'foo': Value('int32' )} ), None),
(Features({'image': Image(), 'foo': Value('int32' )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({'nested': Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def __lowercase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple ):
assert get_writer_batch_size(__lowerCAmelCase ) == expected
| 657
|
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
snake_case : str = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S 9D TH AD''',
'''KS 8D 4D 9S 4S''', # pair
'''8C 4S KH JS 4D''', # pair
'''QH 8H KD JH 8S''', # pair
'''KC 4H KS 2H 8D''', # pair
'''KD 4S KC 3H 8S''', # pair
'''AH 8S AS KC JH''', # pair
'''3H 4C 4H 3S 2H''', # 2 pairs
'''5S 5D 2C KH KH''', # 2 pairs
'''3C KH 5D 5S KH''', # 2 pairs
'''AS 3C KH AD KH''', # 2 pairs
'''7C 7S 3S 7H 5S''', # 3 of a kind
'''7C 7S KH 2H 7H''', # 3 of a kind
'''AC KH QH AH AS''', # 3 of a kind
'''2H 4D 3C AS 5S''', # straight (low ace)
'''3C 5C 4C 2C 6H''', # straight
'''6S 8S 7S 5H 9H''', # straight
'''JS QS 9H TS KH''', # straight
'''QC KH TS JS AH''', # straight (high ace)
'''8C 9C 5C 3C TC''', # flush
'''3S 8S 9S 5S KS''', # flush
'''4C 5C 9C 8C KC''', # flush
'''JH 8H AH KH QH''', # flush
'''3D 2H 3H 2C 2D''', # full house
'''2H 2C 3S 3H 3D''', # full house
'''KH KC 3S 3H 3D''', # full house
'''JC 6H JS JD JH''', # 4 of a kind
'''JC 7H JS JD JH''', # 4 of a kind
'''JC KH JS JD JH''', # 4 of a kind
'''2S AS 4S 5S 3S''', # straight flush (low ace)
'''2D 6D 3D 4D 5D''', # straight flush
'''5C 6C 3C 7C 4C''', # straight flush
'''JH 9H TH KH QH''', # straight flush
'''JH AH TH KH QH''', # royal flush (high ace straight flush)
)
snake_case : str = (
('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)
snake_case : str = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', True),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', False),
('''AS 3S 4S 8S 2S''', True),
)
snake_case : Tuple = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', False),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', True),
)
snake_case : str = (
('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 14]),
('''2H 5D 3C AS 5S''', False, [14, 5, 5, 3, 2]),
('''JH QD KC AS TS''', False, [14, 13, 12, 11, 10]),
('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)
snake_case : Tuple = (
('''JH AH TH KH QH''', 0),
('''JH 9H TH KH QH''', 0),
('''JC KH JS JD JH''', 7),
('''KH KC 3S 3H 3D''', 6),
('''8C 9C 5C 3C TC''', 0),
('''JS QS 9H TS KH''', 0),
('''7C 7S KH 2H 7H''', 3),
('''3C KH 5D 5S KH''', 2),
('''QH 8H KD JH 8S''', 1),
('''2D 6D 9D TH 7D''', 0),
)
snake_case : int = (
('''JH AH TH KH QH''', 23),
('''JH 9H TH KH QH''', 22),
('''JC KH JS JD JH''', 21),
('''KH KC 3S 3H 3D''', 20),
('''8C 9C 5C 3C TC''', 19),
('''JS QS 9H TS KH''', 18),
('''7C 7S KH 2H 7H''', 17),
('''3C KH 5D 5S KH''', 16),
('''QH 8H KD JH 8S''', 15),
('''2D 6D 9D TH 7D''', 14),
)
def __lowercase ( ):
a__ , a__ = randrange(len(__lowerCAmelCase ) ), randrange(len(__lowerCAmelCase ) )
a__ = ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
a__ , a__ = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def __lowercase ( __lowerCAmelCase : int = 1_0_0 ):
return (generate_random_hand() for _ in range(__lowerCAmelCase ))
@pytest.mark.parametrize('hand, expected' , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] ):
assert PokerHand(__lowerCAmelCase )._is_flush() == expected
@pytest.mark.parametrize('hand, expected' , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Tuple , __lowerCAmelCase : Any ):
assert PokerHand(__lowerCAmelCase )._is_straight() == expected
@pytest.mark.parametrize('hand, expected, card_values' , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict ):
a__ = PokerHand(__lowerCAmelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('hand, expected' , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Any , __lowerCAmelCase : Tuple ):
assert PokerHand(__lowerCAmelCase )._is_same_kind() == expected
@pytest.mark.parametrize('hand, expected' , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ):
assert PokerHand(__lowerCAmelCase )._hand_type == expected
@pytest.mark.parametrize('hand, other, expected' , __lowerCAmelCase )
def __lowercase ( __lowerCAmelCase : List[str] , __lowerCAmelCase : Any , __lowerCAmelCase : List[Any] ):
assert PokerHand(__lowerCAmelCase ).compare_with(PokerHand(__lowerCAmelCase ) ) == expected
@pytest.mark.parametrize('hand, other, expected' , generate_random_hands() )
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple ):
assert PokerHand(__lowerCAmelCase ).compare_with(PokerHand(__lowerCAmelCase ) ) == expected
def __lowercase ( ):
a__ = [PokerHand(__lowerCAmelCase ) for hand in SORTED_HANDS]
a__ = poker_hands.copy()
shuffle(__lowerCAmelCase )
a__ = chain(sorted(__lowerCAmelCase ) )
for index, hand in enumerate(__lowerCAmelCase ):
assert hand == poker_hands[index]
def __lowercase ( ):
# Test that five high straights are compared correctly.
a__ = [PokerHand('2D AC 3H 4H 5S' ), PokerHand('2S 3H 4H 5S 6C' )]
pokerhands.sort(reverse=__lowerCAmelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def __lowercase ( ):
# Multiple calls to five_high_straight function should still return True
# and shouldn't mutate the list in every call other than the first.
a__ = PokerHand('2C 4S AS 3D 5C' )
a__ = True
a__ = [5, 4, 3, 2, 1_4]
for _ in range(1_0 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def __lowercase ( ):
# Problem number 54 from Project Euler
# Testing from poker_hands.txt file
a__ = 0
a__ = os.path.abspath(os.path.dirname(__lowerCAmelCase ) )
a__ = os.path.join(__lowerCAmelCase , 'poker_hands.txt' )
with open(__lowerCAmelCase ) as file_hand:
for line in file_hand:
a__ = line[:1_4].strip()
a__ = line[1_5:].strip()
a__ , a__ = PokerHand(__lowerCAmelCase ), PokerHand(__lowerCAmelCase )
a__ = player.compare_with(__lowerCAmelCase )
if output == "Win":
answer += 1
assert answer == 3_7_6
| 657
| 1
|
'''simple docstring'''
class lowercase_ :
"""simple docstring"""
def __init__( self : Optional[int] ):
__lowercase = 0
__lowercase = 0
__lowercase = {}
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : str ):
if vertex not in self.adjacency:
__lowercase = {}
self.num_vertices += 1
def SCREAMING_SNAKE_CASE ( self : List[Any] ,lowercase__ : Optional[Any] ,lowercase__ : List[str] ,lowercase__ : Any ):
self.add_vertex(lowercase__ )
self.add_vertex(lowercase__ )
if head == tail:
return
__lowercase = weight
__lowercase = weight
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.get_edges()
for edge in edges:
__lowercase , __lowercase , __lowercase = edge
edges.remove((tail, head, weight) )
for i in range(len(lowercase__ ) ):
__lowercase = list(edges[i] )
edges.sort(key=lambda lowercase__ : e[2] )
for i in range(len(lowercase__ ) - 1 ):
if edges[i][2] >= edges[i + 1][2]:
__lowercase = edges[i][2] + 1
for edge in edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = weight
__lowercase = weight
def __str__( self : Union[str, Any] ):
__lowercase = ''''''
for tail in self.adjacency:
for head in self.adjacency[tail]:
__lowercase = self.adjacency[head][tail]
string += F"{head} -> {tail} == {weight}\n"
return string.rstrip('''\n''' )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = []
for tail in self.adjacency:
for head in self.adjacency[tail]:
output.append((tail, head, self.adjacency[head][tail]) )
return output
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
return self.adjacency.keys()
@staticmethod
def SCREAMING_SNAKE_CASE ( lowercase__ : str=None ,lowercase__ : Any=None ):
__lowercase = Graph()
if vertices is None:
__lowercase = []
if edges is None:
__lowercase = []
for vertex in vertices:
g.add_vertex(lowercase__ )
for edge in edges:
g.add_edge(*lowercase__ )
return g
class lowercase_ :
"""simple docstring"""
def __init__( self : List[str] ):
__lowercase = {}
__lowercase = {}
def __len__( self : Dict ):
return len(self.parent )
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : Any ):
if item in self.parent:
return self.find(lowercase__ )
__lowercase = item
__lowercase = 0
return item
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : Dict ):
if item not in self.parent:
return self.make_set(lowercase__ )
if item != self.parent[item]:
__lowercase = self.find(self.parent[item] )
return self.parent[item]
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : Tuple ,lowercase__ : Dict ):
__lowercase = self.find(lowercase__ )
__lowercase = self.find(lowercase__ )
if roota == roota:
return roota
if self.rank[roota] > self.rank[roota]:
__lowercase = roota
return roota
if self.rank[roota] < self.rank[roota]:
__lowercase = roota
return roota
if self.rank[roota] == self.rank[roota]:
self.rank[roota] += 1
__lowercase = roota
return roota
return None
@staticmethod
def SCREAMING_SNAKE_CASE ( lowercase__ : Optional[int] ):
__lowercase = graph.num_vertices
__lowercase = Graph.UnionFind()
__lowercase = []
while num_components > 1:
__lowercase = {}
for vertex in graph.get_vertices():
__lowercase = -1
__lowercase = graph.get_edges()
for edge in edges:
__lowercase , __lowercase , __lowercase = edge
edges.remove((tail, head, weight) )
for edge in edges:
__lowercase , __lowercase , __lowercase = edge
__lowercase = union_find.find(lowercase__ )
__lowercase = union_find.find(lowercase__ )
if seta != seta:
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__lowercase = [head, tail, weight]
if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
__lowercase = [head, tail, weight]
for vertex in cheap_edge:
if cheap_edge[vertex] != -1:
__lowercase , __lowercase , __lowercase = cheap_edge[vertex]
if union_find.find(lowercase__ ) != union_find.find(lowercase__ ):
union_find.union(lowercase__ ,lowercase__ )
mst_edges.append(cheap_edge[vertex] )
__lowercase = num_components - 1
__lowercase = Graph.build(edges=lowercase__ )
return mst
| 41
|
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
NezhaModel,
)
from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class lowercase_ :
"""simple docstring"""
def __init__( self : Dict ,lowercase__ : Dict ,lowercase__ : int=1_3 ,lowercase__ : List[str]=7 ,lowercase__ : int=True ,lowercase__ : int=True ,lowercase__ : Union[str, Any]=True ,lowercase__ : List[Any]=True ,lowercase__ : str=9_9 ,lowercase__ : Optional[Any]=3_2 ,lowercase__ : Union[str, Any]=5 ,lowercase__ : List[Any]=4 ,lowercase__ : str=3_7 ,lowercase__ : Tuple="gelu" ,lowercase__ : List[Any]=0.1 ,lowercase__ : Dict=0.1 ,lowercase__ : int=1_2_8 ,lowercase__ : Dict=3_2 ,lowercase__ : Dict=1_6 ,lowercase__ : Any=2 ,lowercase__ : int=0.0_2 ,lowercase__ : List[str]=3 ,lowercase__ : Dict=4 ,lowercase__ : Optional[int]=None ,):
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
if self.use_token_type_ids:
__lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
__lowercase = ids_tensor([self.batch_size] ,self.num_choices )
__lowercase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return NezhaConfig(
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=lowercase__ ,initializer_range=self.initializer_range ,)
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = self.prepare_config_and_inputs()
__lowercase = True
__lowercase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowercase = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : Union[str, Any] ,lowercase__ : List[str] ,lowercase__ : List[str] ,lowercase__ : List[str] ,lowercase__ : Tuple ,lowercase__ : Tuple ,lowercase__ : str ):
__lowercase = NezhaModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ )
__lowercase = model(lowercase__ ,token_type_ids=lowercase__ )
__lowercase = 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 SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : Dict ,lowercase__ : str ,lowercase__ : Optional[Any] ,lowercase__ : Optional[Any] ,lowercase__ : List[str] ,lowercase__ : Tuple ,lowercase__ : Tuple ,lowercase__ : Optional[int] ,lowercase__ : List[Any] ,):
__lowercase = True
__lowercase = NezhaModel(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(
lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,encoder_hidden_states=lowercase__ ,encoder_attention_mask=lowercase__ ,)
__lowercase = model(
lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,encoder_hidden_states=lowercase__ ,)
__lowercase = model(lowercase__ ,attention_mask=lowercase__ ,token_type_ids=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 SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : List[str] ,lowercase__ : Dict ,lowercase__ : Tuple ,lowercase__ : Optional[Any] ,lowercase__ : List[Any] ,lowercase__ : List[Any] ,lowercase__ : Optional[Any] ):
__lowercase = NezhaForMaskedLM(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,labels=lowercase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : List[str] ,lowercase__ : Dict ,lowercase__ : Any ,lowercase__ : int ,lowercase__ : Union[str, Any] ,lowercase__ : Optional[int] ,lowercase__ : Any ):
__lowercase = NezhaForNextSentencePrediction(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(
lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,labels=lowercase__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, 2) )
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : str ,lowercase__ : Dict ,lowercase__ : Tuple ,lowercase__ : Dict ,lowercase__ : Tuple ,lowercase__ : int ,lowercase__ : int ):
__lowercase = NezhaForPreTraining(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(
lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,labels=lowercase__ ,next_sentence_label=lowercase__ ,)
self.parent.assertEqual(result.prediction_logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape ,(self.batch_size, 2) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : Union[str, Any] ,lowercase__ : Optional[Any] ,lowercase__ : Tuple ,lowercase__ : List[str] ,lowercase__ : Dict ,lowercase__ : Optional[int] ,lowercase__ : Union[str, Any] ):
__lowercase = NezhaForQuestionAnswering(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(
lowercase__ ,attention_mask=lowercase__ ,token_type_ids=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 SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Tuple ,lowercase__ : str ,lowercase__ : List[str] ,lowercase__ : Dict ,lowercase__ : Any ,lowercase__ : Optional[int] ,lowercase__ : int ):
__lowercase = self.num_labels
__lowercase = NezhaForSequenceClassification(lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,labels=lowercase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : Union[str, Any] ,lowercase__ : List[str] ,lowercase__ : int ,lowercase__ : List[Any] ,lowercase__ : List[Any] ,lowercase__ : Any ,lowercase__ : Optional[Any] ):
__lowercase = self.num_labels
__lowercase = NezhaForTokenClassification(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = model(lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,labels=lowercase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : List[Any] ,lowercase__ : List[Any] ,lowercase__ : Optional[Any] ,lowercase__ : List[str] ,lowercase__ : Dict ,lowercase__ : List[Any] ,lowercase__ : str ):
__lowercase = self.num_choices
__lowercase = NezhaForMultipleChoice(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__lowercase = token_type_ids.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 ,self.num_choices ,-1 ).contiguous()
__lowercase = model(
lowercase__ ,attention_mask=lowercase__ ,token_type_ids=lowercase__ ,labels=lowercase__ ,)
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.prepare_config_and_inputs()
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = config_and_inputs
__lowercase = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowercase_ (lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = (
(
NezhaModel,
NezhaForMaskedLM,
NezhaForMultipleChoice,
NezhaForNextSentencePrediction,
NezhaForPreTraining,
NezhaForQuestionAnswering,
NezhaForSequenceClassification,
NezhaForTokenClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE : Tuple = (
{
'feature-extraction': NezhaModel,
'fill-mask': NezhaForMaskedLM,
'question-answering': NezhaForQuestionAnswering,
'text-classification': NezhaForSequenceClassification,
'token-classification': NezhaForTokenClassification,
'zero-shot': NezhaForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE : List[str] = True
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : List[str] ,lowercase__ : str ,lowercase__ : Any=False ):
__lowercase = super()._prepare_for_class(lowercase__ ,lowercase__ ,return_labels=lowercase__ )
if return_labels:
if model_class in get_values(lowercase__ ):
__lowercase = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) ,dtype=torch.long ,device=lowercase__ )
__lowercase = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=lowercase__ )
return inputs_dict
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = NezhaModelTester(self )
__lowercase = ConfigTester(self ,config_class=lowercase__ ,hidden_size=3_7 )
def SCREAMING_SNAKE_CASE ( self : int ):
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Any ):
# This regression test was failing with PyTorch < 1.3
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
__lowercase = None
self.model_tester.create_and_check_model_as_decoder(
lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,)
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowercase__ )
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowercase__ )
@slow
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = NezhaModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
@slow
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# NezhaForMultipleChoice behaves incorrectly in JIT environments.
if model_class == NezhaForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=lowercase__ )
__lowercase = self._prepare_for_class(lowercase__ ,lowercase__ )
__lowercase = torch.jit.trace(
lowercase__ ,(inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(lowercase__ ,os.path.join(lowercase__ ,'''bert.pt''' ) )
__lowercase = torch.jit.load(os.path.join(lowercase__ ,'''bert.pt''' ) ,map_location=lowercase__ )
loaded(inputs_dict['''input_ids'''].to(lowercase__ ) ,inputs_dict['''attention_mask'''].to(lowercase__ ) )
@require_torch
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
@slow
def SCREAMING_SNAKE_CASE ( self : int ):
__lowercase = NezhaModel.from_pretrained('''sijunhe/nezha-cn-base''' )
__lowercase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(lowercase__ ,attention_mask=lowercase__ )[0]
__lowercase = torch.Size((1, 6, 7_6_8) )
self.assertEqual(output.shape ,lowercase__ )
__lowercase = torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,lowercase__ ,atol=1e-4 ) )
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base''' )
__lowercase = torch.tensor([[0, 1, 2, 3, 4, 5]] )
__lowercase = torch.tensor([[1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(lowercase__ ,attention_mask=lowercase__ )[0]
__lowercase = torch.Size((1, 6, 2_1_1_2_8) )
self.assertEqual(output.shape ,lowercase__ )
__lowercase = torch.tensor(
[[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,lowercase__ ,atol=1e-4 ) )
| 41
| 1
|
def __UpperCAmelCase ( __a : int = 50 ) -> int:
"""simple docstring"""
_a : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 578
|
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'''
)
a__ = None
a__ = {
'''7B''': 11008,
'''13B''': 13824,
'''30B''': 17920,
'''65B''': 22016,
'''70B''': 28672,
}
a__ = {
'''7B''': 1,
'''7Bf''': 1,
'''13B''': 2,
'''13Bf''': 2,
'''30B''': 4,
'''65B''': 8,
'''70B''': 8,
'''70Bf''': 8,
}
def __UpperCAmelCase ( __a : str ,__a : Optional[int]=1 ,__a : Any=256 ) -> Optional[Any]:
"""simple docstring"""
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def __UpperCAmelCase ( __a : Any ) -> Any:
"""simple docstring"""
with open(__a ,'''r''' ) as f:
return json.load(__a )
def __UpperCAmelCase ( __a : List[Any] ,__a : Any ) -> int:
"""simple docstring"""
with open(__a ,'''w''' ) as f:
json.dump(__a ,__a )
def __UpperCAmelCase ( __a : Optional[int] ,__a : Optional[Any] ,__a : int ,__a : Any=True ) -> Union[str, Any]:
"""simple docstring"""
os.makedirs(__a ,exist_ok=__a )
_a : Optional[Any] = os.path.join(__a ,'''tmp''' )
os.makedirs(__a ,exist_ok=__a )
_a : Any = read_json(os.path.join(__a ,'''params.json''' ) )
_a : Optional[int] = NUM_SHARDS[model_size]
_a : Optional[int] = params['''n_layers''']
_a : List[str] = params['''n_heads''']
_a : Union[str, Any] = n_heads // num_shards
_a : str = params['''dim''']
_a : Optional[Any] = dim // n_heads
_a : str = 1_00_00.0
_a : Union[str, Any] = 1.0 / (base ** (torch.arange(0 ,__a ,2 ).float() / dims_per_head))
if "n_kv_heads" in params:
_a : str = params['''n_kv_heads'''] # for GQA / MQA
_a : Union[str, Any] = n_heads_per_shard // num_key_value_heads
_a : List[str] = dim // num_key_value_heads
else: # compatibility with other checkpoints
_a : Optional[Any] = n_heads
_a : Union[str, Any] = n_heads_per_shard
_a : List[Any] = dim
# permute for sliced rotary
def permute(__a : Optional[Any] ,__a : Dict=n_heads ,__a : Dict=dim ,__a : Tuple=dim ):
return w.view(__a ,dima // n_heads // 2 ,2 ,__a ).transpose(1 ,2 ).reshape(__a ,__a )
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.)
_a : Any = torch.load(os.path.join(__a ,'''consolidated.00.pth''' ) ,map_location='''cpu''' )
else:
# Sharded
_a : Tuple = [
torch.load(os.path.join(__a ,F"""consolidated.{i:02d}.pth""" ) ,map_location='''cpu''' )
for i in range(__a )
]
_a : List[Any] = 0
_a : Optional[int] = {'''weight_map''': {}}
for layer_i in range(__a ):
_a : Any = F"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
_a : List[str] = {
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.
_a : int = {
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(),
}
_a : Optional[int] = permute(
torch.cat(
[
loaded[i][F"""layers.{layer_i}.attention.wq.weight"""].view(__a ,__a ,__a )
for i in range(__a )
] ,dim=0 ,).reshape(__a ,__a ) )
_a : int = permute(
torch.cat(
[
loaded[i][F"""layers.{layer_i}.attention.wk.weight"""].view(
__a ,__a ,__a )
for i in range(__a )
] ,dim=0 ,).reshape(__a ,__a ) ,__a ,__a ,__a ,)
_a : List[Any] = torch.cat(
[
loaded[i][F"""layers.{layer_i}.attention.wv.weight"""].view(
__a ,__a ,__a )
for i in range(__a )
] ,dim=0 ,).reshape(__a ,__a )
_a : Dict = torch.cat(
[loaded[i][F"""layers.{layer_i}.attention.wo.weight"""] for i in range(__a )] ,dim=1 )
_a : Optional[Any] = torch.cat(
[loaded[i][F"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(__a )] ,dim=0 )
_a : str = torch.cat(
[loaded[i][F"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(__a )] ,dim=1 )
_a : Dict = torch.cat(
[loaded[i][F"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(__a )] ,dim=0 )
_a : Any = inv_freq
for k, v in state_dict.items():
_a : Optional[int] = filename
param_count += v.numel()
torch.save(__a ,os.path.join(__a ,__a ) )
_a : int = F"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
if model_size == "7B":
# Unsharded
_a : int = {
'''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
'''model.norm.weight''': loaded['''norm.weight'''],
'''lm_head.weight''': loaded['''output.weight'''],
}
else:
_a : List[str] = {
'''model.norm.weight''': loaded[0]['''norm.weight'''],
'''model.embed_tokens.weight''': torch.cat(
[loaded[i]['''tok_embeddings.weight'''] for i in range(__a )] ,dim=1 ),
'''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(__a )] ,dim=0 ),
}
for k, v in state_dict.items():
_a : Any = filename
param_count += v.numel()
torch.save(__a ,os.path.join(__a ,__a ) )
# Write configs
_a : Tuple = {'''total_size''': param_count * 2}
write_json(__a ,os.path.join(__a ,'''pytorch_model.bin.index.json''' ) )
_a : Any = params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
_a : int = params['''multiple_of'''] if '''multiple_of''' in params else 256
_a : Union[str, Any] = LlamaConfig(
hidden_size=__a ,intermediate_size=compute_intermediate_size(__a ,__a ,__a ) ,num_attention_heads=params['''n_heads'''] ,num_hidden_layers=params['''n_layers'''] ,rms_norm_eps=params['''norm_eps'''] ,num_key_value_heads=__a ,)
config.save_pretrained(__a )
# 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.''' )
_a : Optional[Any] = LlamaForCausalLM.from_pretrained(__a ,torch_dtype=torch.floataa ,low_cpu_mem_usage=__a )
# Avoid saving this as part of the config.
del model.config._name_or_path
print('''Saving in the Transformers format.''' )
model.save_pretrained(__a ,safe_serialization=__a )
shutil.rmtree(__a )
def __UpperCAmelCase ( __a : Tuple ,__a : List[Any] ) -> int:
"""simple docstring"""
_a : Optional[int] = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(F"""Saving a {tokenizer_class.__name__} to {tokenizer_path}.""" )
_a : List[Any] = tokenizer_class(__a )
tokenizer.save_pretrained(__a )
def __UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
_a : Dict = 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=__a ,help='''Whether or not to save using `safetensors`.''' )
_a : List[Any] = 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 ,)
_a : List[Any] = os.path.join(args.input_dir ,'''tokenizer.model''' )
write_tokenizer(args.output_dir ,__a )
if __name__ == "__main__":
main()
| 578
| 1
|
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def is_in_circle(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
__A : Tuple = 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 : Union[str, Any] = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(SCREAMING_SNAKE_CASE ) )
# The ratio of the area for circle to square is pi/4.
__A : List[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 _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0.0 , SCREAMING_SNAKE_CASE = 1.0 , ):
'''simple docstring'''
return mean(
function_to_integrate(uniform(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) for _ in range(SCREAMING_SNAKE_CASE ) ) * (max_value - min_value)
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0.0 , SCREAMING_SNAKE_CASE = 1.0 ):
'''simple docstring'''
def identity_function(SCREAMING_SNAKE_CASE ) -> float:
return x
__A : Tuple = area_under_curve_estimator(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__A : str = (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 _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def function_to_integrate(SCREAMING_SNAKE_CASE ) -> float:
return sqrt(4.0 - x * x )
__A : Dict = area_under_curve_estimator(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , 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()
| 111
|
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_UpperCamelCase = 16
_UpperCamelCase = 32
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 16 ):
'''simple docstring'''
__A : Tuple = AutoTokenizer.from_pretrained("bert-base-cased" )
__A : List[str] = load_dataset("glue" , "mrpc" )
def tokenize_function(SCREAMING_SNAKE_CASE ):
# max_length=None => use the model max length (it's actually the default)
__A : Any = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=SCREAMING_SNAKE_CASE , max_length=SCREAMING_SNAKE_CASE )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__A : Optional[Any] = datasets.map(
SCREAMING_SNAKE_CASE , batched=SCREAMING_SNAKE_CASE , remove_columns=["idx", "sentence1", "sentence2"] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__A : List[Any] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(SCREAMING_SNAKE_CASE ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__A : str = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__A : List[str] = 16
elif accelerator.mixed_precision != "no":
__A : Tuple = 8
else:
__A : Optional[Any] = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE , padding="longest" , max_length=SCREAMING_SNAKE_CASE , pad_to_multiple_of=SCREAMING_SNAKE_CASE , return_tensors="pt" , )
# Instantiate dataloaders.
__A : Optional[int] = DataLoader(
tokenized_datasets["train"] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
__A : Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=SCREAMING_SNAKE_CASE , collate_fn=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_UpperCamelCase = mocked_dataloaders # noqa: F811
def _lowercase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if os.environ.get("TESTING_MOCKED_DATALOADERS" , SCREAMING_SNAKE_CASE ) == "1":
__A : int = 2
# New Code #
__A : List[str] = int(args.gradient_accumulation_steps )
__A : int = int(args.local_sgd_steps )
# Initialize accelerator
__A : int = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=SCREAMING_SNAKE_CASE )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__A : Tuple = config["lr"]
__A : Union[str, Any] = int(config["num_epochs"] )
__A : Optional[int] = int(config["seed"] )
__A : Optional[Any] = int(config["batch_size"] )
__A : Optional[int] = evaluate.load("glue" , "mrpc" )
set_seed(SCREAMING_SNAKE_CASE )
__A ,__A : Tuple = get_dataloaders(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__A : List[str] = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=SCREAMING_SNAKE_CASE )
# 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 : Union[str, Any] = model.to(accelerator.device )
# Instantiate optimizer
__A : str = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE )
# Instantiate scheduler
__A : Optional[int] = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE , num_warmup_steps=100 , num_training_steps=(len(SCREAMING_SNAKE_CASE ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__A ,__A ,__A ,__A ,__A : List[str] = accelerator.prepare(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE ):
model.train()
with LocalSGD(
accelerator=SCREAMING_SNAKE_CASE , model=SCREAMING_SNAKE_CASE , local_sgd_steps=SCREAMING_SNAKE_CASE , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(SCREAMING_SNAKE_CASE ):
__A : List[Any] = model(**SCREAMING_SNAKE_CASE )
__A : List[str] = output.loss
accelerator.backward(SCREAMING_SNAKE_CASE )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__A : Tuple = model(**SCREAMING_SNAKE_CASE )
__A : Union[str, Any] = outputs.logits.argmax(dim=-1 )
__A ,__A : Tuple = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE , references=SCREAMING_SNAKE_CASE , )
__A : List[Any] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"epoch {epoch}:" , SCREAMING_SNAKE_CASE )
def _lowercase ():
'''simple docstring'''
__A : Dict = argparse.ArgumentParser(description="Simple example of training script." )
parser.add_argument(
"--mixed_precision" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=SCREAMING_SNAKE_CASE , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument(
"--local_sgd_steps" , type=SCREAMING_SNAKE_CASE , default=8 , help="Number of local SGD steps or None to disable local SGD" )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
__A : List[str] = parser.parse_args()
__A : List[str] = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 111
| 1
|
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
@staticmethod
@abstractmethod
def __lowerCamelCase ( __UpperCamelCase ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def __lowerCamelCase ( self ):
'''simple docstring'''
raise NotImplementedError()
| 716
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
'self_attn.rotary_emb': 'encoder.embed_positions',
'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
}
__SCREAMING_SNAKE_CASE : Optional[Any] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
]
def _snake_case ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[Any]:
for attribute in key.split(""".""" ):
__a : str = getattr(lowercase , lowercase )
if weight_type is not None:
__a : Dict = getattr(lowercase , lowercase ).shape
else:
__a : Dict = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__a : Any = value
elif weight_type == "weight_g":
__a : int = value
elif weight_type == "weight_v":
__a : int = value
elif weight_type == "bias":
__a : List[Any] = value
elif weight_type == "running_mean":
__a : Union[str, Any] = value
elif weight_type == "running_var":
__a : Tuple = value
elif weight_type == "num_batches_tracked":
__a : Optional[int] = value
elif weight_type == "inv_freq":
__a : List[str] = value
else:
__a : List[str] = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _snake_case ( lowercase , lowercase , lowercase ) -> Dict:
__a : Dict = []
__a : Dict = fairseq_model.state_dict()
__a : Tuple = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
__a : int = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
__a : List[Any] = True
else:
for key, mapped_key in MAPPING.items():
__a : Optional[int] = """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
__a : str = True
if "*" in mapped_key:
__a : Optional[int] = name.split(lowercase )[0].split(""".""" )[-2]
__a : List[Any] = mapped_key.replace("""*""" , lowercase )
if "pos_bias_u" in name:
__a : Union[str, Any] = None
elif "pos_bias_v" in name:
__a : List[Any] = None
elif "weight_g" in name:
__a : List[Any] = """weight_g"""
elif "weight_v" in name:
__a : List[Any] = """weight_v"""
elif "bias" in name:
__a : Optional[int] = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__a : str = """weight"""
elif "running_mean" in name:
__a : List[str] = """running_mean"""
elif "inv_freq" in name:
__a : Dict = """inv_freq"""
elif "running_var" in name:
__a : Union[str, Any] = """running_var"""
elif "num_batches_tracked" in name:
__a : int = """num_batches_tracked"""
else:
__a : Optional[int] = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _snake_case ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> List[str]:
__a : Optional[Any] = full_name.split("""conv_layers.""" )[-1]
__a : Union[str, Any] = name.split(""".""" )
__a : Optional[Any] = int(items[0] )
__a : int = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__a : Dict = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__a : str = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__a : Dict = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__a : Union[str, Any] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def _snake_case ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> Optional[Any]:
if config_path is not None:
__a : Any = WavaVecaConformerConfig.from_pretrained(lowercase , hidden_act="""swish""" )
else:
__a : Optional[int] = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
__a : Optional[Any] = """rotary"""
if is_finetuned:
if dict_path:
__a : List[Any] = Dictionary.load(lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__a : int = target_dict.pad_index
__a : List[str] = target_dict.bos_index
__a : str = target_dict.eos_index
__a : Dict = len(target_dict.symbols )
__a : Any = os.path.join(lowercase , """vocab.json""" )
if not os.path.isdir(lowercase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase ) )
return
os.makedirs(lowercase , exist_ok=lowercase )
__a : Dict = target_dict.indices
# fairseq has the <pad> and <s> switched
__a : Optional[Any] = 0
__a : List[Any] = 1
with open(lowercase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(lowercase , lowercase )
__a : int = WavaVecaCTCTokenizer(
lowercase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=lowercase , )
__a : Optional[int] = True if config.feat_extract_norm == """layer""" else False
__a : Dict = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=lowercase , return_attention_mask=lowercase , )
__a : str = WavaVecaProcessor(feature_extractor=lowercase , tokenizer=lowercase )
processor.save_pretrained(lowercase )
__a : List[str] = WavaVecaConformerForCTC(lowercase )
else:
__a : Optional[int] = WavaVecaConformerForPreTraining(lowercase )
if is_finetuned:
__a , __a , __a : Dict = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
__a : Optional[int] = argparse.Namespace(task="""audio_pretraining""" )
__a : Tuple = fairseq.tasks.setup_task(lowercase )
__a , __a , __a : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowercase )
__a : Any = model[0].eval()
recursively_load_weights(lowercase , lowercase , not is_finetuned )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 697
| 0
|
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *_snake_case : int , **_snake_case : Optional[int] ):
"""simple docstring"""
warnings.warn(
'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use CLIPImageProcessor instead.' , _snake_case , )
super().__init__(*_snake_case , **_snake_case )
| 9
|
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class a :
def __init__( self , _snake_case , _snake_case=13 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=5_12 , _snake_case=16 , _snake_case=2 , _snake_case=0.02 , _snake_case=3 , _snake_case=4 , _snake_case=None , _snake_case=10_00 , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_input_mask
lowerCAmelCase = use_token_type_ids
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_labels
lowerCAmelCase = num_choices
lowerCAmelCase = scope
lowerCAmelCase = range_bbox
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowerCAmelCase = bbox[i, j, 3]
lowerCAmelCase = bbox[i, j, 1]
lowerCAmelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
lowerCAmelCase = bbox[i, j, 2]
lowerCAmelCase = bbox[i, j, 0]
lowerCAmelCase = t
lowerCAmelCase = tf.convert_to_tensor(_snake_case )
lowerCAmelCase = None
if self.use_input_mask:
lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
lowerCAmelCase = None
if self.use_token_type_ids:
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
lowerCAmelCase = None
lowerCAmelCase = None
lowerCAmelCase = None
if self.use_labels:
lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
lowerCAmelCase = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , token_type_ids=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case )
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 UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForMaskedLM(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForSequenceClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = self.num_labels
lowerCAmelCase = TFLayoutLMForTokenClassification(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase__ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering(config=_snake_case )
lowerCAmelCase = model(_snake_case , _snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
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 UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.prepare_config_and_inputs()
(
(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,(
lowerCAmelCase
) ,
) = config_and_inputs
lowerCAmelCase = {
'input_ids': input_ids,
'bbox': bbox,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_tf
class a ( a__ , a__ , unittest.TestCase ):
snake_case__ = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
snake_case__ = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
snake_case__ = False
snake_case__ = True
snake_case__ = 1_0
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_snake_case )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = TFLayoutLMModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
@unittest.skip('Onnx compliancy broke with TF 2.10' )
def UpperCamelCase__ ( self ):
"""simple docstring"""
pass
def _SCREAMING_SNAKE_CASE ():
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
lowerCAmelCase = tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]] ) # noqa: E231
lowerCAmelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
lowerCAmelCase = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class a ( unittest.TestCase ):
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the sequence output on [0, :3, :3]
lowerCAmelCase = tf.convert_to_tensor(
[[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , _snake_case , atol=1E-3 ) )
# test the pooled output on [1, :3]
lowerCAmelCase = tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , _snake_case , atol=1E-3 ) )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=2 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
lowerCAmelCase = outputs.loss
lowerCAmelCase = (2,)
self.assertEqual(loss.shape , _snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = (2, 2)
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' , num_labels=13 )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(
input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , labels=_snake_case )
# test the shape of the logits
lowerCAmelCase = outputs.logits
lowerCAmelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , _snake_case )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased' )
lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = prepare_layoutlm_batch_inputs()
# forward pass
lowerCAmelCase = model(input_ids=_snake_case , bbox=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case )
# test the shape of the logits
lowerCAmelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , _snake_case )
self.assertEqual(outputs.end_logits.shape , _snake_case )
| 4
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_lowercase = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'AdaptiveEmbedding',
'TransfoXLForSequenceClassification',
'TransfoXLLMHeadModel',
'TransfoXLModel',
'TransfoXLPreTrainedModel',
'load_tf_weights_in_transfo_xl',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase = [
'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFAdaptiveEmbedding',
'TFTransfoXLForSequenceClassification',
'TFTransfoXLLMHeadModel',
'TFTransfoXLMainLayer',
'TFTransfoXLModel',
'TFTransfoXLPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
_lowercase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 708
|
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
_lowercase = [
{'dataset': 'wikipedia', 'config_name': '20220301.de'},
{'dataset': 'wikipedia', 'config_name': '20220301.en'},
{'dataset': 'wikipedia', 'config_name': '20220301.fr'},
{'dataset': 'wikipedia', 'config_name': '20220301.frr'},
{'dataset': 'wikipedia', 'config_name': '20220301.it'},
{'dataset': 'wikipedia', 'config_name': '20220301.simple'},
{'dataset': 'snli', 'config_name': 'plain_text'},
{'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
{'dataset': 'wiki40b', 'config_name': 'en'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
{'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
{'dataset': 'natural_questions', 'config_name': 'default'},
]
def __UpperCamelCase ( a : Dict=True ) ->str:
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a ) )
class _lowercase ( __a ):
_UpperCAmelCase = None
_UpperCAmelCase = None
def UpperCamelCase ( self , A__ , A__ ) -> str:
with TemporaryDirectory() as tmp_dir:
snake_case = dataset_module_factory(A__ , cache_dir=A__ )
snake_case = import_main_class(dataset_module.module_path , dataset=A__ )
snake_case = builder_cls(
cache_dir=A__ , config_name=A__ , hash=dataset_module.hash , )
snake_case = '''/'''.join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A__ ).replace(os.sep , '''/''' ),
config.DATASET_INFO_FILENAME,
] )
snake_case = cached_path(A__ , cache_dir=A__ )
self.assertTrue(os.path.exists(A__ ) )
@pytest.mark.integration
def __UpperCamelCase ( a : List[str] ) ->Any:
snake_case = tmp_path_factory.mktemp('''test_hf_gcp''' ) / '''test_wikipedia_simple'''
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
snake_case = None
builder_instance.download_and_prepare()
snake_case = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def __UpperCamelCase ( a : Any ) ->Union[str, Any]:
snake_case = dataset_module_factory('''wikipedia''' , cache_dir=a )
snake_case = import_main_class(dataset_module.module_path , dataset=a )
snake_case = builder_cls(
cache_dir=a , config_name='''20220301.frr''' , hash=dataset_module.hash , )
snake_case = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(a , a )
assert "train" in ds
assert isinstance(ds['''train'''] , a )
assert next(iter(ds['''train'''] ) )
| 44
| 0
|
'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> int:
A_ = None
if token is not None:
A_ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
A_ = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
A_ = requests.get(UpperCAmelCase__, headers=UpperCAmelCase__ ).json()
A_ = {}
try:
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
A_ = math.ceil((result["""total_count"""] - 1_00) / 1_00 )
for i in range(UpperCAmelCase__ ):
A_ = requests.get(url + F'''&page={i + 2}''', headers=UpperCAmelCase__ ).json()
job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return job_links
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> Any:
A_ = None
if token is not None:
A_ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
A_ = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
A_ = requests.get(UpperCAmelCase__, headers=UpperCAmelCase__ ).json()
A_ = {}
try:
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
A_ = math.ceil((result["""total_count"""] - 1_00) / 1_00 )
for i in range(UpperCAmelCase__ ):
A_ = requests.get(url + F'''&page={i + 2}''', headers=UpperCAmelCase__ ).json()
artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]} )
return artifacts
except Exception:
print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}''' )
return {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Optional[Any]:
A_ = None
if token is not None:
A_ = {"""Accept""": """application/vnd.github+json""", """Authorization""": F'''Bearer {token}'''}
A_ = requests.get(UpperCAmelCase__, headers=UpperCAmelCase__, allow_redirects=UpperCAmelCase__ )
A_ = result.headers["""Location"""]
A_ = requests.get(UpperCAmelCase__, allow_redirects=UpperCAmelCase__ )
A_ = os.path.join(UpperCAmelCase__, F'''{artifact_name}.zip''' )
with open(UpperCAmelCase__, """wb""" ) as fp:
fp.write(response.content )
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> Optional[int]:
A_ = []
A_ = []
A_ = None
with zipfile.ZipFile(UpperCAmelCase__ ) as z:
for filename in z.namelist():
if not os.path.isdir(UpperCAmelCase__ ):
# read the file
if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
with z.open(UpperCAmelCase__ ) as f:
for line in f:
A_ = line.decode("""UTF-8""" ).strip()
if filename == "failures_line.txt":
try:
# `error_line` is the place where `error` occurs
A_ = line[: line.index(""": """ )]
A_ = line[line.index(""": """ ) + len(""": """ ) :]
errors.append([error_line, error] )
except Exception:
# skip un-related lines
pass
elif filename == "summary_short.txt" and line.startswith("""FAILED """ ):
# `test` is the test method that failed
A_ = line[len("""FAILED """ ) :]
failed_tests.append(UpperCAmelCase__ )
elif filename == "job_name.txt":
A_ = line
if len(UpperCAmelCase__ ) != len(UpperCAmelCase__ ):
raise ValueError(
F'''`errors` and `failed_tests` should have the same number of elements. Got {len(UpperCAmelCase__ )} for `errors` '''
F'''and {len(UpperCAmelCase__ )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
""" problem.""" )
A_ = None
if job_name and job_links:
A_ = job_links.get(UpperCAmelCase__, UpperCAmelCase__ )
# A list with elements of the form (line of error, error, failed test)
A_ = [x + [y] + [job_link] for x, y in zip(UpperCAmelCase__, UpperCAmelCase__ )]
return result
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> str:
A_ = []
A_ = [os.path.join(UpperCAmelCase__, UpperCAmelCase__ ) for p in os.listdir(UpperCAmelCase__ ) if p.endswith(""".zip""" )]
for p in paths:
errors.extend(get_errors_from_single_artifact(UpperCAmelCase__, job_links=UpperCAmelCase__ ) )
return errors
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> int:
A_ = Counter()
counter.update([x[1] for x in logs] )
A_ = counter.most_common()
A_ = {}
for error, count in counts:
if error_filter is None or error not in error_filter:
A_ = {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}
A_ = dict(sorted(r.items(), key=lambda UpperCAmelCase__ : item[1]["count"], reverse=UpperCAmelCase__ ) )
return r
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
A_ = test.split("""::""" )[0]
if test.startswith("""tests/models/""" ):
A_ = test.split("""/""" )[2]
else:
A_ = None
return test
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__=None ) -> int:
A_ = [(x[0], x[1], get_model(x[2] )) for x in logs]
A_ = [x for x in logs if x[2] is not None]
A_ = {x[2] for x in logs}
A_ = {}
for test in tests:
A_ = Counter()
# count by errors in `test`
counter.update([x[1] for x in logs if x[2] == test] )
A_ = counter.most_common()
A_ = {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
A_ = sum(error_counts.values() )
if n_errors > 0:
A_ = {"""count""": n_errors, """errors""": error_counts}
A_ = dict(sorted(r.items(), key=lambda UpperCAmelCase__ : item[1]["count"], reverse=UpperCAmelCase__ ) )
return r
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Union[str, Any]:
A_ = """| no. | error | status |"""
A_ = """|-:|:-|:-|"""
A_ = [header, sep]
for error in reduced_by_error:
A_ = reduced_by_error[error]["""count"""]
A_ = F'''| {count} | {error[:1_00]} | |'''
lines.append(UpperCAmelCase__ )
return "\n".join(UpperCAmelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> str:
A_ = """| model | no. of errors | major error | count |"""
A_ = """|-:|-:|-:|-:|"""
A_ = [header, sep]
for model in reduced_by_model:
A_ = reduced_by_model[model]["""count"""]
A_ , A_ = list(reduced_by_model[model]["""errors"""].items() )[0]
A_ = F'''| {model} | {count} | {error[:60]} | {_count} |'''
lines.append(UpperCAmelCase__ )
return "\n".join(UpperCAmelCase__ )
if __name__ == "__main__":
__lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
parser.add_argument(
'''--output_dir''',
type=str,
required=True,
help='''Where to store the downloaded artifacts and other result files.''',
)
parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
__lowerCamelCase = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
__lowerCamelCase = get_job_links(args.workflow_run_id, token=args.token)
__lowerCamelCase = {}
# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
if _job_links:
for k, v in _job_links.items():
# This is how GitHub actions combine job names.
if " / " in k:
__lowerCamelCase = k.find(''' / ''')
__lowerCamelCase = k[index + len(''' / ''') :]
__lowerCamelCase = v
with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(job_links, fp, ensure_ascii=False, indent=4)
__lowerCamelCase = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
for idx, (name, url) in enumerate(artifacts.items()):
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
__lowerCamelCase = get_all_errors(args.output_dir, job_links=job_links)
# `e[1]` is the error
__lowerCamelCase = Counter()
counter.update([e[1] for e in errors])
# print the top 30 most common test errors
__lowerCamelCase = counter.most_common(30)
for item in most_common:
print(item)
with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(errors, fp, ensure_ascii=False, indent=4)
__lowerCamelCase = reduce_by_error(errors)
__lowerCamelCase = reduce_by_model(errors)
__lowerCamelCase = make_github_table(reduced_by_error)
__lowerCamelCase = make_github_table_per_model(reduced_by_model)
with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
fp.write(sa)
with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
fp.write(sa)
| 288
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
'''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
'''processing_whisper''': ['''WhisperProcessor'''],
'''tokenization_whisper''': ['''WhisperTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''WhisperTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WhisperForConditionalGeneration''',
'''WhisperModel''',
'''WhisperPreTrainedModel''',
'''WhisperForAudioClassification''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFWhisperForConditionalGeneration''',
'''TFWhisperModel''',
'''TFWhisperPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''FlaxWhisperForConditionalGeneration''',
'''FlaxWhisperModel''',
'''FlaxWhisperPreTrainedModel''',
'''FlaxWhisperForAudioClassification''',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 288
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCamelCase : Tuple = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Any , *_lowercase : Tuple , **_lowercase : Tuple ):
warnings.warn(
'The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use OwlViTImageProcessor instead.' , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 719
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase : Dict = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase : Any = [
"MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegatronBertForCausalLM",
"MegatronBertForMaskedLM",
"MegatronBertForMultipleChoice",
"MegatronBertForNextSentencePrediction",
"MegatronBertForPreTraining",
"MegatronBertForQuestionAnswering",
"MegatronBertForSequenceClassification",
"MegatronBertForTokenClassification",
"MegatronBertModel",
"MegatronBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
UpperCamelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 91
| 0
|
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Dict = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
SCREAMING_SNAKE_CASE__ : Optional[int] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class a__:
a_ : str = field(
default=snake_case__ , metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(snake_case__ )} )
a_ : str = field(
default=snake_case__ , metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD task.'''} )
a_ : int = field(
default=1_2_8 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
a_ : int = field(
default=1_2_8 , metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''} , )
a_ : int = field(
default=6_4 , metadata={
'''help''': (
'''The maximum number of tokens for the question. Questions longer than this will '''
'''be truncated to this length.'''
)
} , )
a_ : int = field(
default=3_0 , metadata={
'''help''': (
'''The maximum length of an answer that can be generated. This is needed because the start '''
'''and end predictions are not conditioned on one another.'''
)
} , )
a_ : bool = field(
default=snake_case__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
a_ : bool = field(
default=snake_case__ , metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''} )
a_ : float = field(
default=0.0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
a_ : int = field(
default=2_0 , metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''} )
a_ : int = field(
default=0 , metadata={
'''help''': (
'''language id of input for language-specific xlm models (see'''
''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
)
} , )
a_ : int = field(default=1 , metadata={'''help''': '''multiple threads for converting example to features'''} )
class a__( snake_case__ ):
a_ : Optional[int] = '''train'''
a_ : Optional[int] = '''dev'''
class a__( snake_case__ ):
a_ : SquadDataTrainingArguments
a_ : List[SquadFeatures]
a_ : Split
a_ : bool
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = Split.train , _UpperCAmelCase = False , _UpperCAmelCase = None , _UpperCAmelCase = "pt" , ) -> Any:
snake_case__ =args
snake_case__ =is_language_sensitive
snake_case__ =SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
try:
snake_case__ =Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
snake_case__ =mode
# Load data features from cache or dataset file
snake_case__ ='v2' if args.version_2_with_negative else 'v1'
snake_case__ =os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}""" , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
snake_case__ =cached_features_file + '.lock'
with FileLock(_UpperCAmelCase ):
if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
snake_case__ =time.time()
snake_case__ =torch.load(_UpperCAmelCase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
snake_case__ =self.old_features['features']
snake_case__ =self.old_features.get('dataset' , _UpperCAmelCase )
snake_case__ =self.old_features.get('examples' , _UpperCAmelCase )
logger.info(
f"""Loading features from cached file {cached_features_file} [took %.3f s]""" , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
' future run' )
else:
if mode == Split.dev:
snake_case__ =self.processor.get_dev_examples(args.data_dir )
else:
snake_case__ =self.processor.get_train_examples(args.data_dir )
snake_case__ , snake_case__ =squad_convert_examples_to_features(
examples=self.examples , tokenizer=_UpperCAmelCase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_UpperCAmelCase , )
snake_case__ =time.time()
torch.save(
{'features': self.features, 'dataset': self.dataset, 'examples': self.examples} , _UpperCAmelCase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )
def __len__( self ) -> Optional[int]:
return len(self.features )
def __getitem__( self , _UpperCAmelCase ) -> Dict[str, torch.Tensor]:
# Convert to Tensors and build dataset
snake_case__ =self.features[i]
snake_case__ =torch.tensor(feature.input_ids , dtype=torch.long )
snake_case__ =torch.tensor(feature.attention_mask , dtype=torch.long )
snake_case__ =torch.tensor(feature.token_type_ids , dtype=torch.long )
snake_case__ =torch.tensor(feature.cls_index , dtype=torch.long )
snake_case__ =torch.tensor(feature.p_mask , dtype=torch.float )
snake_case__ =torch.tensor(feature.is_impossible , dtype=torch.float )
snake_case__ ={
'input_ids': input_ids,
'attention_mask': attention_mask,
'token_type_ids': token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({'cls_index': cls_index, 'p_mask': p_mask} )
if self.args.version_2_with_negative:
inputs.update({'is_impossible': is_impossible} )
if self.is_language_sensitive:
inputs.update({'langs': (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
snake_case__ =torch.tensor(feature.start_position , dtype=torch.long )
snake_case__ =torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({'start_positions': start_positions, 'end_positions': end_positions} )
return inputs
| 538
|
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[Any] = '''src/transformers'''
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE__ : Optional[Any] = re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE__ : Union[str, Any] = re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma: "MyModel",
SCREAMING_SNAKE_CASE__ : Any = re.compile(r'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only: ["foo", "bar"],
SCREAMING_SNAKE_CASE__ : List[Any] = re.compile(r'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE__ : Optional[int] = re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
SCREAMING_SNAKE_CASE__ : str = re.compile(r'''^\s*try:''')
# Catches a line with else:
SCREAMING_SNAKE_CASE__ : Any = re.compile(r'''^\s*else:''')
def a ( UpperCamelCase_ : Dict ) -> List[str]:
if _re_test_backend.search(UpperCamelCase_ ) is None:
return None
snake_case__ =[b[0] for b in _re_backend.findall(UpperCamelCase_ )]
backends.sort()
return "_and_".join(UpperCamelCase_ )
def a ( UpperCamelCase_ : List[Any] ) -> Tuple:
with open(UpperCamelCase_ , 'r' , encoding='utf-8' , newline='\n' ) as f:
snake_case__ =f.readlines()
snake_case__ =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
snake_case__ =[]
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
snake_case__ =lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(UpperCamelCase_ ):
snake_case__ =_re_one_line_import_struct.search(UpperCamelCase_ ).groups()[0]
snake_case__ =re.findall(r'\[([^\]]+)\]' , UpperCamelCase_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
snake_case__ =_re_import_struct_key_value.search(UpperCamelCase_ )
if single_line_import_search is not None:
snake_case__ =[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
snake_case__ ={'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.
snake_case__ =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:
snake_case__ =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
snake_case__ =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
snake_case__ =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:
snake_case__ =_re_import_struct_add_many.search(UpperCamelCase_ ).groups()[0].split(', ' )
snake_case__ =[obj[1:-1] for obj in imports if len(UpperCamelCase_ ) > 0]
objects.extend(UpperCamelCase_ )
elif _re_between_brackets.search(UpperCamelCase_ ) is not None:
snake_case__ =_re_between_brackets.search(UpperCamelCase_ ).groups()[0].split(', ' )
snake_case__ =[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
snake_case__ =objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
snake_case__ =[]
while (
line_index < len(UpperCamelCase_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
snake_case__ =lines[line_index]
snake_case__ =_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
snake_case__ ={'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.
snake_case__ =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:
snake_case__ =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
snake_case__ =[]
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
snake_case__ =lines[line_index]
snake_case__ =_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
snake_case__ =objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def a ( UpperCamelCase_ : int , UpperCamelCase_ : List[Any] ) -> Union[str, Any]:
def find_duplicates(UpperCamelCase_ : Tuple ):
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!"]
snake_case__ =[]
for key in import_dict_objects.keys():
snake_case__ =find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
snake_case__ =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] ) ):
snake_case__ ='base imports' if key == 'none' else f"""{key} backend"""
errors.append(f"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def a ( ) -> Optional[Any]:
snake_case__ =[]
for root, _, files in os.walk(UpperCamelCase_ ):
if "__init__.py" in files:
snake_case__ =os.path.join(UpperCamelCase_ , '__init__.py' )
snake_case__ =parse_init(UpperCamelCase_ )
if objects is not None:
snake_case__ =analyze_results(*UpperCamelCase_ )
if len(UpperCamelCase_ ) > 0:
snake_case__ =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 ( ) -> Dict:
snake_case__ =[]
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
snake_case__ =str((Path(UpperCamelCase_ ) / folder).relative_to(UpperCamelCase_ ) )
snake_case__ =short_path.replace(os.path.sep , '.' )
submodules.append(UpperCamelCase_ )
for fname in files:
if fname == "__init__.py":
continue
snake_case__ =str((Path(UpperCamelCase_ ) / fname).relative_to(UpperCamelCase_ ) )
snake_case__ =short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(UpperCamelCase_ )
return submodules
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [
'''convert_pytorch_checkpoint_to_tf2''',
'''modeling_flax_pytorch_utils''',
'''models.esm.openfold_utils''',
]
def a ( ) -> Union[str, Any]:
# This is to make sure the transformers module imported is the one in the repo.
from transformers.utils import direct_transformers_import
snake_case__ =direct_transformers_import(UpperCamelCase_ )
snake_case__ =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:
snake_case__ =f.read()
import_structure_keys.update(set(re.findall(r'import_structure\[\"([^\"]*)\"\]' , UpperCamelCase_ ) ) )
snake_case__ =[
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(UpperCamelCase_ ) > 0:
snake_case__ ='\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()
| 538
| 1
|
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
A = TypeVar('''T''')
class __lowercase ( Generic[T] ):
'''simple docstring'''
__lowerCAmelCase = 42 # Cache store of keys
__lowerCAmelCase = 42 # References of the keys in cache
__lowerCAmelCase = 10 # Maximum capacity of cache
def __init__( self , _UpperCAmelCase ):
__a : List[Any] = deque()
__a : Optional[Any] = set()
if not n:
__a : List[str] = sys.maxsize
elif n < 0:
raise ValueError('''n should be an integer greater than 0.''' )
else:
__a : Dict = n
def _lowerCamelCase ( self , _UpperCAmelCase ):
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
__a : List[str] = self.dq_store.pop()
self.key_reference.remove(_UpperCAmelCase )
else:
self.dq_store.remove(_UpperCAmelCase )
self.dq_store.appendleft(_UpperCAmelCase )
self.key_reference.add(_UpperCAmelCase )
def _lowerCamelCase ( self ):
for k in self.dq_store:
print(_UpperCAmelCase )
def __repr__( self ):
return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
A = LRUCache(4)
lru_cache.refer('''A''')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('''A''')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 101
|
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=768 ):
super().__init__(_UpperCAmelCase )
__a : str = proj_size
__a : Optional[Any] = CLIPVisionModel(_UpperCAmelCase )
__a : List[Any] = PaintByExampleMapper(_UpperCAmelCase )
__a : int = nn.LayerNorm(config.hidden_size )
__a : List[Any] = nn.Linear(config.hidden_size , self.proj_size )
# uncondition for scaling
__a : int = nn.Parameter(torch.randn((1, 1, self.proj_size) ) )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase=False ):
__a : str = self.model(pixel_values=_UpperCAmelCase )
__a : Union[str, Any] = clip_output.pooler_output
__a : Optional[int] = self.mapper(latent_states[:, None] )
__a : int = self.final_layer_norm(_UpperCAmelCase )
__a : Optional[Any] = self.proj_out(_UpperCAmelCase )
if return_uncond_vector:
return latent_states, self.uncond_vector
return latent_states
class __lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self , _UpperCAmelCase ):
super().__init__()
__a : List[str] = (config.num_hidden_layers + 1) // 5
__a : Optional[Any] = config.hidden_size
__a : str = 1
__a : Union[str, Any] = nn.ModuleList(
[
BasicTransformerBlock(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , activation_fn='''gelu''' , attention_bias=_UpperCAmelCase )
for _ in range(_UpperCAmelCase )
] )
def _lowerCamelCase ( self , _UpperCAmelCase ):
for block in self.blocks:
__a : Union[str, Any] = block(_UpperCAmelCase )
return hidden_states
| 101
| 1
|
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}
class lowerCAmelCase_ ( _a):
lowerCamelCase_ = 'xlnet'
lowerCamelCase_ = ['mems']
lowerCamelCase_ = {
'n_token': 'vocab_size', # Backward compatibility
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : int , __A : Optional[Any]=32000 , __A : List[str]=1024 , __A : Optional[int]=24 , __A : int=16 , __A : Tuple=4096 , __A : List[Any]="gelu" , __A : str=True , __A : Dict="bi" , __A : List[str]=0.02 , __A : Union[str, Any]=1E-12 , __A : int=0.1 , __A : List[str]=512 , __A : Optional[Any]=None , __A : Union[str, Any]=True , __A : str=False , __A : List[str]=False , __A : str=-1 , __A : Dict=False , __A : Union[str, Any]="last" , __A : Any=True , __A : int="tanh" , __A : Union[str, Any]=0.1 , __A : Any=5 , __A : Union[str, Any]=5 , __A : Dict=5 , __A : List[str]=1 , __A : Any=2 , **__A : int , ) ->Union[str, Any]:
"""simple docstring"""
a__ :List[Any] = vocab_size
a__ :int = d_model
a__ :Tuple = n_layer
a__ :List[Any] = n_head
if d_model % n_head != 0:
raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''' )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})''' )
a__ :Union[str, Any] = d_model // n_head
a__ :Optional[Any] = ff_activation
a__ :Optional[int] = d_inner
a__ :Optional[int] = untie_r
a__ :Optional[Any] = attn_type
a__ :Tuple = initializer_range
a__ :Tuple = layer_norm_eps
a__ :int = dropout
a__ :Optional[int] = mem_len
a__ :List[Any] = reuse_len
a__ :Optional[int] = bi_data
a__ :Tuple = clamp_len
a__ :int = same_length
a__ :List[Any] = summary_type
a__ :Any = summary_use_proj
a__ :Dict = summary_activation
a__ :int = summary_last_dropout
a__ :Union[str, Any] = start_n_top
a__ :List[Any] = end_n_top
a__ :Union[str, Any] = bos_token_id
a__ :List[str] = pad_token_id
a__ :List[Any] = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , __A , )
a__ :Optional[Any] = kwargs["use_cache"]
a__ :List[Any] = use_mems_eval
a__ :Optional[Any] = use_mems_train
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
@property
def _snake_case ( self : str ) ->Union[str, Any]:
"""simple docstring"""
logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
return -1
@max_position_embeddings.setter
def _snake_case ( self : List[str] , __A : Any ) ->Optional[Any]:
"""simple docstring"""
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 395
|
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCAmelCase_ :
def __init__( self : Dict , __A : Optional[int] , __A : int=2 , __A : str=True , __A : List[Any]=False , __A : List[str]=10 , __A : Union[str, Any]=3 , __A : Dict=32 * 8 , __A : str=32 * 8 , __A : int=4 , __A : List[str]=64 , ) ->Tuple:
"""simple docstring"""
a__ :Optional[Any] = parent
a__ :Dict = batch_size
a__ :str = is_training
a__ :Optional[int] = use_auxiliary_loss
a__ :str = num_queries
a__ :int = num_channels
a__ :Optional[int] = min_size
a__ :Optional[Any] = max_size
a__ :Dict = num_labels
a__ :Union[str, Any] = hidden_dim
a__ :Any = hidden_dim
def _snake_case ( self : Tuple ) ->List[str]:
"""simple docstring"""
a__ :Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
__A )
a__ :Optional[int] = torch.ones([self.batch_size, self.min_size, self.max_size] , device=__A )
a__ :Optional[int] = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=__A ) > 0.5
).float()
a__ :List[str] = (torch.rand((self.batch_size, self.num_labels) , device=__A ) > 0.5).long()
a__ :Tuple = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _snake_case ( self : Tuple ) ->Union[str, Any]:
"""simple docstring"""
a__ :List[str] = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
a__ :List[str] = self.num_queries
a__ :Optional[int] = self.num_labels
a__ :Tuple = [1, 1, 1, 1]
a__ :Dict = self.num_channels
a__ :Optional[Any] = 64
a__ :Union[str, Any] = 128
a__ :Optional[Any] = self.hidden_dim
a__ :int = self.hidden_dim
a__ :List[str] = self.hidden_dim
return config
def _snake_case ( self : Any ) ->Dict:
"""simple docstring"""
a__ , a__ , a__ , a__ , a__ :int = self.prepare_config_and_inputs()
a__ :Optional[int] = {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
return config, inputs_dict
def _snake_case ( self : int , __A : Union[str, Any] , __A : Tuple ) ->List[Any]:
"""simple docstring"""
a__ :Tuple = output.encoder_hidden_states
a__ :List[Any] = output.pixel_decoder_hidden_states
a__ :Optional[Any] = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(__A ) , config.decoder_layers )
def _snake_case ( self : Dict , __A : List[str] , __A : Tuple , __A : Union[str, Any] , __A : Dict=False ) ->Any:
"""simple docstring"""
with torch.no_grad():
a__ :Dict = MaskaFormerModel(config=__A )
model.to(__A )
model.eval()
a__ :Tuple = model(pixel_values=__A , pixel_mask=__A )
a__ :Dict = model(__A , output_hidden_states=__A )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(__A , __A )
def _snake_case ( self : Optional[int] , __A : int , __A : str , __A : Tuple , __A : List[Any] , __A : List[str] ) ->Any:
"""simple docstring"""
a__ :Dict = MaskaFormerForUniversalSegmentation(config=__A )
model.to(__A )
model.eval()
def comm_check_on_output(__A : Tuple ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
a__ :Union[str, Any] = model(pixel_values=__A , pixel_mask=__A )
a__ :List[Any] = model(__A )
comm_check_on_output(__A )
a__ :str = model(
pixel_values=__A , pixel_mask=__A , mask_labels=__A , class_labels=__A )
comm_check_on_output(__A )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowerCAmelCase_ ( _a ,_a ,unittest.TestCase):
lowerCamelCase_ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
lowerCamelCase_ = {'feature-extraction': MaskaFormerModel} if is_torch_available() else {}
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
def _snake_case ( self : Tuple ) ->Dict:
"""simple docstring"""
a__ :List[str] = MaskaFormerModelTester(self )
a__ :List[str] = ConfigTester(self , config_class=__A , has_text_modality=__A )
def _snake_case ( self : Tuple ) ->str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _snake_case ( self : str ) ->Tuple:
"""simple docstring"""
a__ , a__ :str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__A , **__A , output_hidden_states=__A )
def _snake_case ( self : Union[str, Any] ) ->int:
"""simple docstring"""
a__ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__A )
@unittest.skip(reason="Mask2Former does not use inputs_embeds" )
def _snake_case ( self : List[str] ) ->Optional[Any]:
"""simple docstring"""
pass
@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method" )
def _snake_case ( self : int ) ->Dict:
"""simple docstring"""
pass
@unittest.skip(reason="Mask2Former is not a generative model" )
def _snake_case ( self : Any ) ->List[str]:
"""simple docstring"""
pass
@unittest.skip(reason="Mask2Former does not use token embeddings" )
def _snake_case ( self : Union[str, Any] ) ->Union[str, Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def _snake_case ( self : str ) ->str:
"""simple docstring"""
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def _snake_case ( self : Optional[int] ) ->Any:
"""simple docstring"""
pass
def _snake_case ( self : Any ) ->Union[str, Any]:
"""simple docstring"""
a__ , a__ :Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ :Any = model_class(__A )
a__ :Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ :Optional[Any] = [*signature.parameters.keys()]
a__ :str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __A )
@slow
def _snake_case ( self : Dict ) ->str:
"""simple docstring"""
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
a__ :Any = MaskaFormerModel.from_pretrained(__A )
self.assertIsNotNone(__A )
def _snake_case ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a__ :Tuple = (self.model_tester.min_size,) * 2
a__ :Optional[int] = {
"pixel_values": torch.randn((2, 3, *size) , device=__A ),
"mask_labels": torch.randn((2, 10, *size) , device=__A ),
"class_labels": torch.zeros(2 , 10 , device=__A ).long(),
}
a__ :Dict = self.model_tester.get_config()
a__ :str = MaskaFormerForUniversalSegmentation(__A ).to(__A )
a__ :int = model(**__A )
self.assertTrue(outputs.loss is not None )
def _snake_case ( self : List[str] ) ->Any:
"""simple docstring"""
a__ , a__ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(__A , **__A , output_hidden_states=__A )
def _snake_case ( self : Tuple ) ->Tuple:
"""simple docstring"""
a__ , a__ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ :Union[str, Any] = model_class(__A ).to(__A )
a__ :int = model(**__A , output_attentions=__A )
self.assertTrue(outputs.attentions is not None )
def _snake_case ( self : Any ) ->Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
a__ :str = self.all_model_classes[1]
a__ , a__ , a__ , a__ , a__ :Any = self.model_tester.prepare_config_and_inputs()
a__ :Optional[Any] = model_class(__A )
model.to(__A )
model.train()
a__ :Any = model(__A , mask_labels=__A , class_labels=__A ).loss
loss.backward()
def _snake_case ( self : Tuple ) ->Optional[Any]:
"""simple docstring"""
a__ :List[str] = self.all_model_classes[1]
a__ , a__ , a__ , a__ , a__ :int = self.model_tester.prepare_config_and_inputs()
a__ :Optional[Any] = True
a__ :Optional[int] = True
a__ :List[Any] = model_class(__A ).to(__A )
model.train()
a__ :Optional[Any] = model(__A , mask_labels=__A , class_labels=__A )
a__ :int = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
a__ :List[str] = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
a__ :List[Any] = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
a__ :Optional[int] = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=__A )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
snake_case__ = 1e-4
def lowerCamelCase__ ( ) -> Optional[int]:
"""simple docstring"""
a__ :Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_vision
@slow
class lowerCAmelCase_ ( unittest.TestCase):
@cached_property
def _snake_case ( self : int ) ->Optional[int]:
"""simple docstring"""
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def _snake_case ( self : int ) ->Dict:
"""simple docstring"""
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def _snake_case ( self : str ) ->int:
"""simple docstring"""
a__ :Tuple = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(__A )
a__ :List[Any] = self.default_image_processor
a__ :str = prepare_img()
a__ :Union[str, Any] = image_processor(__A , return_tensors="pt" ).to(__A )
a__ :Union[str, Any] = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 384, 384) )
with torch.no_grad():
a__ :Optional[int] = model(**__A )
a__ :str = torch.tensor(
[[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
a__ :Dict = torch.tensor(
[[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , __A , atol=__A ) )
a__ :Union[str, Any] = torch.tensor(
[[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]] ).to(__A )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , __A , atol=__A ) )
def _snake_case ( self : Any ) ->Dict:
"""simple docstring"""
a__ :Dict = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__A ).eval()
a__ :Tuple = self.default_image_processor
a__ :Any = prepare_img()
a__ :str = image_processor(__A , return_tensors="pt" ).to(__A )
a__ :Any = inputs["pixel_values"].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(__A , (1, 3, 384, 384) )
with torch.no_grad():
a__ :int = model(**__A )
# masks_queries_logits
a__ :Optional[Any] = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
a__ :Dict = [
[-8.7_839, -9.0_056, -8.8_121],
[-7.4_104, -7.0_313, -6.5_401],
[-6.6_105, -6.3_427, -6.4_675],
]
a__ :Any = torch.tensor(__A ).to(__A )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , __A , atol=__A ) )
# class_queries_logits
a__ :Tuple = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
a__ :Optional[int] = torch.tensor(
[
[1.8_324, -8.0_835, -4.1_922],
[0.8_450, -9.0_050, -3.6_053],
[0.3_045, -7.7_293, -3.0_275],
] ).to(__A )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , __A , atol=__A ) )
def _snake_case ( self : List[str] ) ->List[Any]:
"""simple docstring"""
a__ :List[str] = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(__A ).eval()
a__ :Tuple = self.default_image_processor
a__ :str = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="pt" , )
a__ :Tuple = inputs["pixel_values"].to(__A )
a__ :List[Any] = [el.to(__A ) for el in inputs["mask_labels"]]
a__ :List[str] = [el.to(__A ) for el in inputs["class_labels"]]
with torch.no_grad():
a__ :List[str] = model(**__A )
self.assertTrue(outputs.loss is not None )
| 395
| 1
|
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : list[int] ) -> int:
"""simple docstring"""
a_ = len(UpperCamelCase ) // 2
# choose the middle 3 elements
a_ = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702
|
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : Tuple ) -> Tuple:
"""simple docstring"""
if isinstance(UpperCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class lowerCamelCase_ :
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
pass
def __magic_name__ ( self ):
pass
def __magic_name__ ( self ):
pass
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = np.abs((a - b) ).max()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ = VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ , a_ = self.get_vision_text_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = {"""vision_model""": vision_model, """text_model""": text_model}
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ , a_ = self.get_vision_text_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = {"""vision_model""": vision_model, """text_model""": text_model}
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
a_ = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
a_ = after_output[0]
a_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1E-3 )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ , a_ = self.get_vision_text_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = {"""vision_model""": vision_model, """text_model""": text_model}
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )
a_ = model(
input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , output_attentions=_SCREAMING_SNAKE_CASE )
a_ = output.vision_model_output.attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
a_ = to_atuple(vision_model.config.image_size )
a_ = to_atuple(vision_model.config.patch_size )
a_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
a_ = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
a_ = output.text_model_output.attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
pt_model.to(_SCREAMING_SNAKE_CASE )
pt_model.eval()
# prepare inputs
a_ = inputs_dict
a_ = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
a_ = pt_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
a_ = fx_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_SCREAMING_SNAKE_CASE , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
a_ = fx_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_SCREAMING_SNAKE_CASE , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = VisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE , from_flax=_SCREAMING_SNAKE_CASE )
pt_model_loaded.to(_SCREAMING_SNAKE_CASE )
pt_model_loaded.eval()
with torch.no_grad():
a_ = pt_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(_SCREAMING_SNAKE_CASE , pt_output_loaded.numpy() , 4E-2 )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _SCREAMING_SNAKE_CASE )
a_ = fx_state
self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE , fx_model.params )
self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_save_load(**_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE )
@is_pt_flax_cross_test
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ = config_inputs_dict.pop("""vision_config""" )
a_ = config_inputs_dict.pop("""text_config""" )
a_ = config_inputs_dict
self.check_equivalence_pt_to_flax(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.check_equivalence_flax_to_pt(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __magic_name__ ( self ):
a_ , a_ = self.get_pretrained_model_and_inputs()
a_ = model_a(**_SCREAMING_SNAKE_CASE )
a_ = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE )
a_ = model_a(**_SCREAMING_SNAKE_CASE )
a_ = after_outputs[0]
a_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1E-5 )
@require_flax
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
def __magic_name__ ( self ):
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=_SCREAMING_SNAKE_CASE , text_from_pt=_SCREAMING_SNAKE_CASE , )
a_ = 13
a_ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
a_ = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
a_ = random_attention_mask([batch_size, 4] )
a_ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = FlaxViTModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxBertModel(_SCREAMING_SNAKE_CASE )
return vision_model, text_model
def __magic_name__ ( self ):
a_ = FlaxViTModelTester(self )
a_ = FlaxBertModelTester(self )
a_ = vit_model_tester.prepare_config_and_inputs()
a_ = bert_model_tester.prepare_config_and_inputs()
a_ , a_ = vision_config_and_inputs
a_ , a_ , a_ , a_ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
def __magic_name__ ( self ):
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=_SCREAMING_SNAKE_CASE , text_from_pt=_SCREAMING_SNAKE_CASE , )
a_ = 13
a_ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
a_ = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
a_ = random_attention_mask([batch_size, 4] )
a_ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = FlaxCLIPVisionModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxBertModel(_SCREAMING_SNAKE_CASE )
return vision_model, text_model
def __magic_name__ ( self ):
a_ = FlaxCLIPVisionModelTester(self )
a_ = FlaxBertModelTester(self )
a_ = clip_model_tester.prepare_config_and_inputs()
a_ = bert_model_tester.prepare_config_and_inputs()
a_ , a_ = vision_config_and_inputs
a_ , a_ , a_ , a_ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ):
a_ = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
a_ = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
a_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
a_ = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
a_ = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
a_ = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
| 403
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case ( lowercase ):
"""simple docstring"""
def __init__( self , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = None , ):
"""simple docstring"""
super().__init__()
self.register_modules(transformer=UpperCamelCase , vae=UpperCamelCase , scheduler=UpperCamelCase )
# create a imagenet -> id dictionary for easier use
lowerCamelCase_ = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
lowerCamelCase_ = int(UpperCamelCase )
lowerCamelCase_ = dict(sorted(self.labels.items() ) )
def snake_case ( self , UpperCamelCase ):
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ):
lowerCamelCase_ = list(UpperCamelCase )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self , UpperCamelCase , UpperCamelCase = 4.0 , UpperCamelCase = None , UpperCamelCase = 50 , UpperCamelCase = "pil" , UpperCamelCase = True , ):
"""simple docstring"""
lowerCamelCase_ = len(UpperCamelCase )
lowerCamelCase_ = self.transformer.config.sample_size
lowerCamelCase_ = self.transformer.config.in_channels
lowerCamelCase_ = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=UpperCamelCase , device=self.device , dtype=self.transformer.dtype , )
lowerCamelCase_ = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowerCamelCase_ = torch.tensor(UpperCamelCase , device=self.device ).reshape(-1 )
lowerCamelCase_ = torch.tensor([1000] * batch_size , device=self.device )
lowerCamelCase_ = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(UpperCamelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowerCamelCase_ = latent_model_input[: len(UpperCamelCase ) // 2]
lowerCamelCase_ = torch.cat([half, half] , dim=0 )
lowerCamelCase_ = self.scheduler.scale_model_input(UpperCamelCase , UpperCamelCase )
lowerCamelCase_ = t
if not torch.is_tensor(UpperCamelCase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
lowerCamelCase_ = latent_model_input.device.type == "mps"
if isinstance(UpperCamelCase , UpperCamelCase ):
lowerCamelCase_ = torch.floataa if is_mps else torch.floataa
else:
lowerCamelCase_ = torch.intaa if is_mps else torch.intaa
lowerCamelCase_ = torch.tensor([timesteps] , dtype=UpperCamelCase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowerCamelCase_ = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowerCamelCase_ = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowerCamelCase_ = self.transformer(
UpperCamelCase , timestep=UpperCamelCase , class_labels=UpperCamelCase ).sample
# perform guidance
if guidance_scale > 1:
lowerCamelCase_ ,lowerCamelCase_ = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowerCamelCase_ ,lowerCamelCase_ = torch.split(UpperCamelCase , len(UpperCamelCase ) // 2 , dim=0 )
lowerCamelCase_ = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowerCamelCase_ = torch.cat([half_eps, half_eps] , dim=0 )
lowerCamelCase_ = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowerCamelCase_ ,lowerCamelCase_ = torch.split(UpperCamelCase , UpperCamelCase , dim=1 )
else:
lowerCamelCase_ = noise_pred
# compute previous image: x_t -> x_t-1
lowerCamelCase_ = self.scheduler.step(UpperCamelCase , UpperCamelCase , UpperCamelCase ).prev_sample
if guidance_scale > 1:
lowerCamelCase_ ,lowerCamelCase_ = latent_model_input.chunk(2 , dim=0 )
else:
lowerCamelCase_ = latent_model_input
lowerCamelCase_ = 1 / self.vae.config.scaling_factor * latents
lowerCamelCase_ = self.vae.decode(UpperCamelCase ).sample
lowerCamelCase_ = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowerCamelCase_ = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCamelCase_ = self.numpy_to_pil(UpperCamelCase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=UpperCamelCase )
| 675
|
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th
logging.set_verbosity_info()
a_ : Optional[Any] = logging.get_logger("""transformers.models.encodec""")
a_ : List[str] = {
"""quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
"""quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
"""quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
"""quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
a_ : Optional[int] = {
"""encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
"""encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
"""encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
"""encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
"""encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
"""encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
"""encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
"""encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
"""encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
"""encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
"""encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
"""encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
"""encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
"""encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
"""encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
"""encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
"""encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
"""encoder.model.13.lstm""": """encoder.layers.13.lstm""",
"""encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
a_ : Tuple = {
"""encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
"""encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
"""encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
"""encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
"""encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
"""encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
"""encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
"""encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
"""encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
"""encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
"""encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
"""encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
"""encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
"""encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
"""encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
"""encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
"""encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
"""encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
a_ : Union[str, Any] = {
"""decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
"""decoder.model.1.lstm""": """decoder.layers.1.lstm""",
"""decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
"""decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
"""decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
"""decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
"""decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
"""decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
"""decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
"""decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
"""decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
"""decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
"""decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
"""decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
"""decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
"""decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
"""decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
"""decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
"""decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
a_ : Union[str, Any] = {
"""decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
"""decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
"""decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
"""decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
"""decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
"""decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
"""decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
"""decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
"""decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
"""decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
"""decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
"""decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
"""decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
"""decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
"""decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
"""decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
"""decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
"""decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
a_ : Optional[Any] = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_DECODER,
}
a_ : List[str] = {
**MAPPING_QUANTIZER,
**MAPPING_ENCODER,
**MAPPING_ENCODER_48K,
**MAPPING_DECODER,
**MAPPING_DECODER_48K,
}
a_ : Any = []
a_ : str = []
def __snake_case ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Tuple ):
for attribute in key.split("." ):
lowerCamelCase_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ )
if weight_type is not None:
lowerCamelCase_ = getattr(UpperCAmelCase_ , UpperCAmelCase_ ).shape
else:
lowerCamelCase_ = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''' )
if weight_type == "weight":
lowerCamelCase_ = value
elif weight_type == "weight_g":
lowerCamelCase_ = value
elif weight_type == "weight_v":
lowerCamelCase_ = value
elif weight_type == "bias":
lowerCamelCase_ = value
elif weight_type == "running_mean":
lowerCamelCase_ = value
elif weight_type == "running_var":
lowerCamelCase_ = value
elif weight_type == "num_batches_tracked":
lowerCamelCase_ = value
elif weight_type == "weight_ih_l0":
lowerCamelCase_ = value
elif weight_type == "weight_hh_l0":
lowerCamelCase_ = value
elif weight_type == "bias_ih_l0":
lowerCamelCase_ = value
elif weight_type == "bias_hh_l0":
lowerCamelCase_ = value
elif weight_type == "weight_ih_l1":
lowerCamelCase_ = value
elif weight_type == "weight_hh_l1":
lowerCamelCase_ = value
elif weight_type == "bias_ih_l1":
lowerCamelCase_ = value
elif weight_type == "bias_hh_l1":
lowerCamelCase_ = value
else:
lowerCamelCase_ = value
logger.info(F'''{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.''' )
def __snake_case ( UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[int] ):
for key in ignore_keys:
if key.endswith(".*" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
lowerCamelCase_ ,lowerCamelCase_ = key.split(".*." )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __snake_case ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple ):
lowerCamelCase_ = []
if model_name == "encodec_24khz" or "encodec_32khz":
lowerCamelCase_ = MAPPING_24K
elif model_name == "encodec_48khz":
lowerCamelCase_ = MAPPING_48K
else:
raise ValueError(F'''Unsupported model: {model_name}''' )
for name, value in orig_dict.items():
if should_ignore(UpperCAmelCase_ , UpperCAmelCase_ ):
logger.info(F'''{name} was ignored''' )
continue
lowerCamelCase_ = False
for key, mapped_key in MAPPING.items():
if "*" in key:
lowerCamelCase_ ,lowerCamelCase_ = key.split(".*." )
if prefix in name and suffix in name:
lowerCamelCase_ = suffix
if key in name:
# HACK otherwise .embed gets initialized with .embed_avg too
if key.endswith("embed" ) and name.endswith("embed_avg" ):
continue
lowerCamelCase_ = True
if "*" in mapped_key:
lowerCamelCase_ = name.split(UpperCAmelCase_ )[0].split("." )[-2]
lowerCamelCase_ = mapped_key.replace("*" , UpperCAmelCase_ )
if "weight_g" in name:
lowerCamelCase_ = "weight_g"
elif "weight_v" in name:
lowerCamelCase_ = "weight_v"
elif "weight_ih_l0" in name:
lowerCamelCase_ = "weight_ih_l0"
elif "weight_hh_l0" in name:
lowerCamelCase_ = "weight_hh_l0"
elif "bias_ih_l0" in name:
lowerCamelCase_ = "bias_ih_l0"
elif "bias_hh_l0" in name:
lowerCamelCase_ = "bias_hh_l0"
elif "weight_ih_l1" in name:
lowerCamelCase_ = "weight_ih_l1"
elif "weight_hh_l1" in name:
lowerCamelCase_ = "weight_hh_l1"
elif "bias_ih_l1" in name:
lowerCamelCase_ = "bias_ih_l1"
elif "bias_hh_l1" in name:
lowerCamelCase_ = "bias_hh_l1"
elif "bias" in name:
lowerCamelCase_ = "bias"
elif "weight" in name:
lowerCamelCase_ = "weight"
elif "running_mean" in name:
lowerCamelCase_ = "running_mean"
elif "running_var" in name:
lowerCamelCase_ = "running_var"
elif "num_batches_tracked" in name:
lowerCamelCase_ = "num_batches_tracked"
else:
lowerCamelCase_ = None
set_recursively(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
continue
if not is_used:
unused_weights.append(UpperCAmelCase_ )
logger.warning(F'''Unused weights: {unused_weights}''' )
@torch.no_grad()
def __snake_case ( UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : List[str]=None , UpperCAmelCase_ : Optional[int]=None , ):
if config_path is not None:
lowerCamelCase_ = EncodecConfig.from_pretrained(UpperCAmelCase_ )
else:
lowerCamelCase_ = EncodecConfig()
if model_name == "encodec_24khz":
pass # config is already correct
elif model_name == "encodec_32khz":
lowerCamelCase_ = [8, 5, 4, 4]
lowerCamelCase_ = [2.2]
lowerCamelCase_ = 64
lowerCamelCase_ = 32000
lowerCamelCase_ = 2048
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
elif model_name == "encodec_48khz":
lowerCamelCase_ = [8, 5, 4, 2]
lowerCamelCase_ = [3.0, 6.0, 12.0, 24.0]
lowerCamelCase_ = 48000
lowerCamelCase_ = 2
lowerCamelCase_ = False
lowerCamelCase_ = "time_group_norm"
lowerCamelCase_ = True
lowerCamelCase_ = 1.0
lowerCamelCase_ = 0.01
else:
raise ValueError(F'''Unknown model name: {model_name}''' )
lowerCamelCase_ = EncodecModel(UpperCAmelCase_ )
lowerCamelCase_ = EncodecFeatureExtractor(
feature_size=config.audio_channels , sampling_rate=config.sampling_rate , chunk_length_s=config.chunk_length_s , overlap=config.overlap , )
feature_extractor.save_pretrained(UpperCAmelCase_ )
lowerCamelCase_ = torch.load(UpperCAmelCase_ )
if "best_state" in original_checkpoint:
# we might have a training state saved, in which case discard the yaml results and just retain the weights
lowerCamelCase_ = original_checkpoint["best_state"]
recursively_load_weights(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
model.save_pretrained(UpperCAmelCase_ )
if repo_id:
print("Pushing to the hub..." )
feature_extractor.push_to_hub(UpperCAmelCase_ )
model.push_to_hub(UpperCAmelCase_ )
if __name__ == "__main__":
a_ : Dict = argparse.ArgumentParser()
parser.add_argument(
"""--model""",
default="""encodec_24khz""",
type=str,
help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
a_ : str = parser.parse_args()
convert_checkpoint(
args.model,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 675
| 1
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase__ ( UpperCamelCase ):
lowerCAmelCase_ : int = ["""image_processor""", """tokenizer"""]
lowerCAmelCase_ : str = """ViltImageProcessor"""
lowerCAmelCase_ : List[str] = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[Any] , snake_case : Tuple=None , snake_case : str=None , **snake_case : Any ) -> Dict:
'''simple docstring'''
A = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , snake_case , )
A = kwargs.pop('feature_extractor' )
A = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('You need to specify an `image_processor`.' )
if tokenizer is None:
raise ValueError('You need to specify a `tokenizer`.' )
super().__init__(snake_case , snake_case )
A = self.image_processor
def __call__( self : Tuple , snake_case : Union[str, Any] , snake_case : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , snake_case : bool = True , snake_case : Union[bool, str, PaddingStrategy] = False , snake_case : Union[bool, str, TruncationStrategy] = None , snake_case : Optional[int] = None , snake_case : int = 0 , snake_case : Optional[int] = None , snake_case : Optional[bool] = None , snake_case : Optional[bool] = None , snake_case : bool = False , snake_case : bool = False , snake_case : bool = False , snake_case : bool = False , snake_case : bool = True , snake_case : Optional[Union[str, TensorType]] = None , **snake_case : int , ) -> BatchEncoding:
'''simple docstring'''
A = self.tokenizer(
text=snake_case , add_special_tokens=snake_case , padding=snake_case , truncation=snake_case , max_length=snake_case , stride=snake_case , pad_to_multiple_of=snake_case , return_token_type_ids=snake_case , return_attention_mask=snake_case , return_overflowing_tokens=snake_case , return_special_tokens_mask=snake_case , return_offsets_mapping=snake_case , return_length=snake_case , verbose=snake_case , return_tensors=snake_case , **snake_case , )
# add pixel_values + pixel_mask
A = self.image_processor(snake_case , return_tensors=snake_case )
encoding.update(snake_case )
return encoding
def A_ ( self : str , *snake_case : Dict , **snake_case : Optional[Any] ) -> List[str]:
'''simple docstring'''
return self.tokenizer.batch_decode(*snake_case , **snake_case )
def A_ ( self : Union[str, Any] , *snake_case : Optional[Any] , **snake_case : int ) -> str:
'''simple docstring'''
return self.tokenizer.decode(*snake_case , **snake_case )
@property
def A_ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
A = self.tokenizer.model_input_names
A = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A_ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , snake_case , )
return self.image_processor_class
@property
def A_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , snake_case , )
return self.image_processor
| 709
|
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
A = ['bert-base-uncased', 'bert-base-cased']
A = 'hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class UpperCAmelCase__ ( tf.keras.Model ):
def __init__( self : Tuple , snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
super().__init__()
A = tokenizer
A = AutoConfig.from_pretrained(snake_case )
A = TFAutoModel.from_config(snake_case )
def A_ ( self : Any , snake_case : Optional[int] ) -> Dict:
'''simple docstring'''
A = self.tokenizer(snake_case )
A = self.bert(**snake_case )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class UpperCAmelCase__ ( unittest.TestCase ):
def A_ ( self : int ) -> Tuple:
'''simple docstring'''
super().setUp()
A = [
BertTokenizer.from_pretrained(snake_case ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
A = [TFBertTokenizer.from_pretrained(snake_case ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(snake_case , use_fast_bert_tokenizer=snake_case )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
A = [
'This is a straightforward English test sentence.',
'This one has some weird characters\rto\nsee\r\nif those\u00E9break things.',
'Now we\'re going to add some Chinese: 一 二 三 一二三',
'And some much more rare Chinese: 齉 堃 齉堃',
'Je vais aussi écrire en français pour tester les accents',
'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
]
A = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def A_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
A = tokenizer(snake_case , return_tensors='tf' , padding='longest' )
A = tf_tokenizer(snake_case )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def A_ ( self : Tuple ) -> Tuple:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
A = tf_tokenizer(self.paired_sentences )
A = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def A_ ( self : Any ) -> Tuple:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
A = tf.function(snake_case )
for test_inputs in (self.test_sentences, self.paired_sentences):
A = tf.constant(snake_case )
A = compiled_tokenizer(snake_case )
A = tf_tokenizer(snake_case )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def A_ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
A = ModelToSave(tokenizer=snake_case )
A = tf.convert_to_tensor(self.test_sentences )
A = model(snake_case ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
A = Path(snake_case ) / 'saved.model'
model.save(snake_case )
A = tf.keras.models.load_model(snake_case )
A = loaded_model(snake_case )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1E-5 )
| 109
| 0
|
'''simple docstring'''
def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> list:
UpperCamelCase = len(snake_case_ )
UpperCamelCase = []
for i in range(len(snake_case_ ) - pat_len + 1 ):
UpperCamelCase = True
for j in range(snake_case_ ):
if s[i + j] != pattern[j]:
UpperCamelCase = False
break
if match_found:
position.append(snake_case_ )
return position
if __name__ == "__main__":
assert naive_pattern_search('ABCDEFG', 'DE') == [3]
print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
| 301
|
"""simple docstring"""
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : str = " " ) ->list:
lowerCamelCase__ : str =[]
lowerCamelCase__ : int =0
for index, char in enumerate(snake_case_ ):
if char == separator:
split_words.append(string[last_index:index] )
lowerCamelCase__ : Dict =index + 1
elif index + 1 == len(snake_case_ ):
split_words.append(string[last_index : index + 1] )
return split_words
if __name__ == "__main__":
from doctest import testmod
testmod()
| 174
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"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
snake_case = "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"
def __UpperCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE_ : Optional[int]=0 ):
lowerCamelCase__ = np.random.RandomState(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __UpperCAmelCase ( self : Dict ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ = np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : List[Any] ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCamelCase__ = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=SCREAMING_SNAKE_CASE_ )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ = np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : Optional[Any] ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCamelCase__ = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ = np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : List[str] ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCamelCase__ = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ = np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : str ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCamelCase__ = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ = np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : Any ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCamelCase__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ ).images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
lowerCamelCase__ = np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCAmelCase ( self : Optional[Any] ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = 3 * [inputs["""prompt"""]]
# forward
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = output.images[0, -3:, -3:, -1]
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = 3 * [inputs.pop("""prompt""" )]
lowerCamelCase__ = pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" , )
lowerCamelCase__ = text_inputs["""input_ids"""]
lowerCamelCase__ = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
lowerCamelCase__ = prompt_embeds
# forward
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def __UpperCAmelCase ( self : Tuple ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = 3 * ["""this is a negative prompt"""]
lowerCamelCase__ = negative_prompt
lowerCamelCase__ = 3 * [inputs["""prompt"""]]
# forward
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = output.images[0, -3:, -3:, -1]
lowerCamelCase__ = self.get_dummy_inputs()
lowerCamelCase__ = 3 * [inputs.pop("""prompt""" )]
lowerCamelCase__ = []
for p in [prompt, negative_prompt]:
lowerCamelCase__ = pipe.tokenizer(
SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""np""" , )
lowerCamelCase__ = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
lowerCamelCase__ , lowerCamelCase__ = embeds
# forward
lowerCamelCase__ = pipe(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@property
def __UpperCAmelCase ( self : List[Any] ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCAmelCase ( self : Tuple ):
lowerCamelCase__ = ort.SessionOptions()
lowerCamelCase__ = False
return options
def __UpperCAmelCase ( self : Any ):
# using the PNDM scheduler by default
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
lowerCamelCase__ = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase__ = np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCAmelCase ( self : List[Any] ):
lowerCamelCase__ = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = """open neural network exchange"""
lowerCamelCase__ = np.random.RandomState(0 )
lowerCamelCase__ = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" )
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase__ = np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCAmelCase ( self : int ):
lowerCamelCase__ = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=SCREAMING_SNAKE_CASE_ , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = """open neural network exchange"""
lowerCamelCase__ = np.random.RandomState(0 )
lowerCamelCase__ = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=SCREAMING_SNAKE_CASE_ , output_type="""np""" )
lowerCamelCase__ = output.images
lowerCamelCase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase__ = np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCAmelCase ( self : Union[str, Any] ):
lowerCamelCase__ = 0
def test_callback_fn(SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : np.ndarray ) -> None:
lowerCamelCase__ = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase__ = latents[0, -3:, -3:, -1]
lowerCamelCase__ = np.array(
[-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
lowerCamelCase__ = latents[0, -3:, -3:, -1]
lowerCamelCase__ = np.array(
[-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
lowerCamelCase__ = False
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = """Andromeda galaxy in a bottle"""
lowerCamelCase__ = np.random.RandomState(0 )
pipe(
prompt=SCREAMING_SNAKE_CASE_ , num_inference_steps=5 , guidance_scale=7.5 , generator=SCREAMING_SNAKE_CASE_ , callback=SCREAMING_SNAKE_CASE_ , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def __UpperCAmelCase ( self : Any ):
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=SCREAMING_SNAKE_CASE_ , feature_extractor=SCREAMING_SNAKE_CASE_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert pipe.safety_checker is None
lowerCamelCase__ = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = OnnxStableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_ )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
lowerCamelCase__ = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
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"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Distribution , SCREAMING_SNAKE_CASE_ : Optional[int]=None , SCREAMING_SNAKE_CASE_ : Tuple=None , SCREAMING_SNAKE_CASE_ : List[str]=0 ):
lowerCamelCase__ = 1.0 if scale is None else scale
lowerCamelCase__ = 0.0 if loc is None else loc
super().__init__(SCREAMING_SNAKE_CASE_ , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=SCREAMING_SNAKE_CASE_ )] )
@property
def __UpperCAmelCase ( self : Dict ):
return self.base_dist.mean * self.scale + self.loc
@property
def __UpperCAmelCase ( self : List[str] ):
return self.base_dist.variance * self.scale**2
@property
def __UpperCAmelCase ( self : int ):
return self.variance.sqrt()
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : List[str] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Dict[str, int] , SCREAMING_SNAKE_CASE_ : Callable[..., Tuple[torch.Tensor]] , **SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
super().__init__(**SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = args_dim
lowerCamelCase__ = nn.ModuleList([nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for dim in args_dim.values()] )
lowerCamelCase__ = domain_map
def __UpperCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : torch.Tensor ):
lowerCamelCase__ = [proj(SCREAMING_SNAKE_CASE_ ) for proj in self.proj]
return self.domain_map(*SCREAMING_SNAKE_CASE_ )
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
super().__init__()
lowerCamelCase__ = function
def __UpperCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE_ : Tuple , *SCREAMING_SNAKE_CASE_ : List[Any] ):
return self.function(SCREAMING_SNAKE_CASE_ , *SCREAMING_SNAKE_CASE_ )
class SCREAMING_SNAKE_CASE__ :
snake_case = 42
snake_case = 42
snake_case = 42
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : int = 1 ):
lowerCamelCase__ = dim
lowerCamelCase__ = {k: dim * self.args_dim[k] for k in self.args_dim}
def __UpperCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
if self.dim == 1:
return self.distribution_class(*SCREAMING_SNAKE_CASE_ )
else:
return Independent(self.distribution_class(*SCREAMING_SNAKE_CASE_ ) , 1 )
def __UpperCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[torch.Tensor] = None , SCREAMING_SNAKE_CASE_ : Optional[torch.Tensor] = None , ):
lowerCamelCase__ = self._base_distribution(SCREAMING_SNAKE_CASE_ )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(SCREAMING_SNAKE_CASE_ , loc=SCREAMING_SNAKE_CASE_ , scale=SCREAMING_SNAKE_CASE_ , event_dim=self.event_dim )
@property
def __UpperCAmelCase ( self : Optional[int] ):
return () if self.dim == 1 else (self.dim,)
@property
def __UpperCAmelCase ( self : List[Any] ):
return len(self.event_shape )
@property
def __UpperCAmelCase ( self : Union[str, Any] ):
return 0.0
def __UpperCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : int ):
return ParameterProjection(
in_features=SCREAMING_SNAKE_CASE_ , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def __UpperCAmelCase ( self : Union[str, Any] , *SCREAMING_SNAKE_CASE_ : torch.Tensor ):
raise NotImplementedError()
@staticmethod
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE_ : torch.Tensor ):
return (x + torch.sqrt(torch.square(SCREAMING_SNAKE_CASE_ ) + 4.0 )) / 2.0
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
snake_case = {"df": 1, "loc": 1, "scale": 1}
snake_case = StudentT
@classmethod
def __UpperCAmelCase ( cls : Dict , SCREAMING_SNAKE_CASE_ : torch.Tensor , SCREAMING_SNAKE_CASE_ : torch.Tensor , SCREAMING_SNAKE_CASE_ : torch.Tensor ):
lowerCamelCase__ = cls.squareplus(SCREAMING_SNAKE_CASE_ ).clamp_min(torch.finfo(scale.dtype ).eps )
lowerCamelCase__ = 2.0 + cls.squareplus(SCREAMING_SNAKE_CASE_ )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
snake_case = {"loc": 1, "scale": 1}
snake_case = Normal
@classmethod
def __UpperCAmelCase ( cls : Tuple , SCREAMING_SNAKE_CASE_ : torch.Tensor , SCREAMING_SNAKE_CASE_ : torch.Tensor ):
lowerCamelCase__ = cls.squareplus(SCREAMING_SNAKE_CASE_ ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
snake_case = {"total_count": 1, "logits": 1}
snake_case = NegativeBinomial
@classmethod
def __UpperCAmelCase ( cls : Union[str, Any] , SCREAMING_SNAKE_CASE_ : torch.Tensor , SCREAMING_SNAKE_CASE_ : torch.Tensor ):
lowerCamelCase__ = cls.squareplus(SCREAMING_SNAKE_CASE_ )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def __UpperCAmelCase ( self : int , SCREAMING_SNAKE_CASE_ : List[Any] ):
lowerCamelCase__ , lowerCamelCase__ = distr_args
if self.dim == 1:
return self.distribution_class(total_count=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ )
else:
return Independent(self.distribution_class(total_count=SCREAMING_SNAKE_CASE_ , logits=SCREAMING_SNAKE_CASE_ ) , 1 )
def __UpperCAmelCase ( self : Tuple , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[torch.Tensor] = None , SCREAMING_SNAKE_CASE_ : Optional[torch.Tensor] = None ):
lowerCamelCase__ , lowerCamelCase__ = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 258
| 1
|
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCamelCase__ ( __lowerCAmelCase : str ):
"""simple docstring"""
lowerCAmelCase_ = os.path.join(args.tf_model_dir , "parameters.json" )
lowerCAmelCase_ = json.loads(open(__lowerCAmelCase ).read() )
if not params:
raise ValueError(
F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith(".pt" ):
lowerCAmelCase_ = args.output + ".pt"
lowerCAmelCase_ = OrderedDict()
with tf.device("/CPU:0" ):
lowerCAmelCase_ = tf.train.load_checkpoint(args.tf_model_dir )
lowerCAmelCase_ = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
lowerCAmelCase_ = reader.get_tensor(__lowerCAmelCase ).astype(np.floataa )
if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
continue
if key_name.startswith("pasts/" ):
if key_name.startswith("pasts/mlp" ):
lowerCAmelCase_ = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
lowerCAmelCase_ = 8
lowerCAmelCase_ = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
lowerCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.startswith("model/moe" ):
lowerCAmelCase_ = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
lowerCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/softmlp/kernel" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
lowerCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
lowerCAmelCase_ = key_name[-9:-7]
for i in range(16 ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
lowerCAmelCase_ = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.startswith("model/mlp" ):
lowerCAmelCase_ = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
lowerCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/p1/bias" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/p2/kernel" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
lowerCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/p2/bias" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.startswith("model/ln" ):
lowerCAmelCase_ = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.norm.bias" % player
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/g" ):
lowerCAmelCase_ = "model.blocks.%d.feed_forward.norm.weight" % player
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.startswith("model/att" ):
lowerCAmelCase_ = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
lowerCAmelCase_ = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
lowerCAmelCase_ = state[:, 0, :, :]
lowerCAmelCase_ = state[:, 1, :, :]
lowerCAmelCase_ = state[:, 2, :, :]
lowerCAmelCase_ = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
lowerCAmelCase_ = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
lowerCAmelCase_ = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/o/kernel" ):
lowerCAmelCase_ = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
lowerCAmelCase_ = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.startswith("model/an" ):
lowerCAmelCase_ = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
lowerCAmelCase_ = "model.blocks.%d.self_attn.norm.bias" % player
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.endswith("/g" ):
lowerCAmelCase_ = "model.blocks.%d.self_attn.norm.weight" % player
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
lowerCAmelCase_ = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
lowerCAmelCase_ = "model.%s.weight" % nlayer
lowerCAmelCase_ = vnp.copy() # same in embedded
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
if key_name.startswith("model/wte" ):
lowerCAmelCase_ = "lm_head.weight"
lowerCAmelCase_ = vnp.copy() # same in embedded
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name.startswith("model/wob" ):
lowerCAmelCase_ = "final_logits_bias"
lowerCAmelCase_ = vnp.copy() # same in embedded
lowerCAmelCase_ = state.reshape((1, -1) )
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name == "model/dense/kernel":
lowerCAmelCase_ = "model.last_project.weight"
lowerCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
elif key_name == "model/dense_1/bias":
lowerCAmelCase_ = "model.last_project.bias"
lowerCAmelCase_ = vnp.copy() # same because it is one dimensional
lowerCAmelCase_ = torch.tensor(__lowerCAmelCase )
torch.save(__lowerCAmelCase , args.output )
if __name__ == "__main__":
_A = argparse.ArgumentParser(
description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
_A = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 290
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
_A = logging.get_logger(__name__)
_A = {
"bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
"bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
"bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
"bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
"bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
"bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}
class _lowerCAmelCase ( __a ):
_lowercase ='''bloom'''
_lowercase =['''past_key_values''']
_lowercase ={
'''num_hidden_layers''': '''n_layer''',
'''num_attention_heads''': '''n_head''',
}
def __init__( self , _UpperCamelCase=250_880 , _UpperCamelCase=64 , _UpperCamelCase=2 , _UpperCamelCase=8 , _UpperCamelCase=1e-5 , _UpperCamelCase=0.02 , _UpperCamelCase=True , _UpperCamelCase=1 , _UpperCamelCase=2 , _UpperCamelCase=False , _UpperCamelCase=0.0 , _UpperCamelCase=0.0 , _UpperCamelCase=1 , _UpperCamelCase=False , **_UpperCamelCase , ) -> str:
lowerCAmelCase_ = vocab_size
# Backward compatibility with n_embed kwarg
lowerCAmelCase_ = kwargs.pop("n_embed" , _UpperCamelCase )
lowerCAmelCase_ = hidden_size if n_embed is None else n_embed
lowerCAmelCase_ = n_layer
lowerCAmelCase_ = n_head
lowerCAmelCase_ = layer_norm_epsilon
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = use_cache
lowerCAmelCase_ = pretraining_tp
lowerCAmelCase_ = apply_residual_connection_post_layernorm
lowerCAmelCase_ = hidden_dropout
lowerCAmelCase_ = attention_dropout
lowerCAmelCase_ = bos_token_id
lowerCAmelCase_ = eos_token_id
lowerCAmelCase_ = slow_but_exact
super().__init__(bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase )
class _lowerCAmelCase ( __a ):
_lowercase =version.parse('''1.12''' )
def __init__( self , _UpperCamelCase , _UpperCamelCase = "default" , _UpperCamelCase = None , _UpperCamelCase = False , ) -> int:
super().__init__(_UpperCamelCase , task=_UpperCamelCase , patching_specs=_UpperCamelCase , use_past=_UpperCamelCase )
if not getattr(self._config , "pad_token_id" , _UpperCamelCase ):
# TODO: how to do that better?
lowerCAmelCase_ = 0
@property
def __a ( self ) -> Mapping[str, Mapping[int, str]]:
lowerCAmelCase_ = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(_UpperCamelCase , direction="inputs" , inverted_values_shape=_UpperCamelCase )
lowerCAmelCase_ = {0: "batch", 1: "past_sequence + sequence"}
else:
lowerCAmelCase_ = {0: "batch", 1: "sequence"}
return common_inputs
@property
def __a ( self ) -> int:
return self._config.n_layer
@property
def __a ( self ) -> int:
return self._config.n_head
@property
def __a ( self ) -> float:
return 1e-3
def __a ( self , _UpperCamelCase , _UpperCamelCase = -1 , _UpperCamelCase = -1 , _UpperCamelCase = False , _UpperCamelCase = None , ) -> Mapping[str, Any]:
lowerCAmelCase_ = super(_UpperCamelCase , self ).generate_dummy_inputs(
_UpperCamelCase , batch_size=_UpperCamelCase , seq_length=_UpperCamelCase , is_pair=_UpperCamelCase , framework=_UpperCamelCase )
# We need to order the input in the way they appears in the forward()
lowerCAmelCase_ = OrderedDict({"input_ids": common_inputs["input_ids"]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowerCAmelCase_ , lowerCAmelCase_ = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowerCAmelCase_ = seqlen + 2
lowerCAmelCase_ = self._config.hidden_size // self.num_attention_heads
lowerCAmelCase_ = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
lowerCAmelCase_ = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
lowerCAmelCase_ = [
(torch.zeros(_UpperCamelCase ), torch.zeros(_UpperCamelCase )) for _ in range(self.num_layers )
]
lowerCAmelCase_ = common_inputs["attention_mask"]
if self.use_past:
lowerCAmelCase_ = ordered_inputs["attention_mask"].dtype
lowerCAmelCase_ = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(_UpperCamelCase , _UpperCamelCase , dtype=_UpperCamelCase )] , dim=1 )
return ordered_inputs
@property
def __a ( self ) -> int:
return 13
| 290
| 1
|
"""simple docstring"""
import heapq
def __A ( a_ :dict) -> str:
__a : int = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(_UpperCamelCase , [-1 * len(_UpperCamelCase), (key, value)])
# chosen_vertices = set of chosen vertices
__a : Optional[Any] = set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
__a : Optional[Any] = heapq.heappop(_UpperCamelCase)[1][0]
chosen_vertices.add(_UpperCamelCase)
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
__a : List[str] = elem[1][1].index(_UpperCamelCase)
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(_UpperCamelCase)
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
A = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')
| 721
|
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def __A ( a_ :int) -> typing.Counter[int]:
__a : typing.Counter[int] = Counter()
for base in range(1 , max_perimeter + 1):
for perpendicular in range(a_ , max_perimeter + 1):
__a : Any = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a_):
__a : List[Any] = int(base + perpendicular + hypotenuse)
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __A ( a_ :int = 10_00) -> int:
__a : Dict = pythagorean_triple(a_)
return triplets.most_common(1)[0][0]
if __name__ == "__main__":
print(F'Perimeter {solution()} has maximum solutions')
| 101
| 0
|
"""simple docstring"""
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 ( _lowercase):
snake_case__ : Optional[Any] = "camembert"
def __init__( self : Optional[Any] , __lowerCAmelCase : Any=3_0_5_2_2 , __lowerCAmelCase : List[str]=7_6_8 , __lowerCAmelCase : List[str]=1_2 , __lowerCAmelCase : Optional[int]=1_2 , __lowerCAmelCase : List[Any]=3_0_7_2 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=0.1 , __lowerCAmelCase : Optional[int]=5_1_2 , __lowerCAmelCase : str=2 , __lowerCAmelCase : int=0.02 , __lowerCAmelCase : List[Any]=1E-12 , __lowerCAmelCase : Union[str, Any]=1 , __lowerCAmelCase : Optional[Any]=0 , __lowerCAmelCase : List[Any]=2 , __lowerCAmelCase : str="absolute" , __lowerCAmelCase : Any=True , __lowerCAmelCase : Optional[int]=None , **__lowerCAmelCase : Optional[int] , ):
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , bos_token_id=__lowerCAmelCase , eos_token_id=__lowerCAmelCase , **__lowerCAmelCase )
_lowerCamelCase : Tuple = vocab_size
_lowerCamelCase : str = hidden_size
_lowerCamelCase : Union[str, Any] = num_hidden_layers
_lowerCamelCase : Any = num_attention_heads
_lowerCamelCase : Optional[Any] = hidden_act
_lowerCamelCase : List[str] = intermediate_size
_lowerCamelCase : Optional[Any] = hidden_dropout_prob
_lowerCamelCase : List[Any] = attention_probs_dropout_prob
_lowerCamelCase : Optional[Any] = max_position_embeddings
_lowerCamelCase : Tuple = type_vocab_size
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : Dict = layer_norm_eps
_lowerCamelCase : List[Any] = position_embedding_type
_lowerCamelCase : int = use_cache
_lowerCamelCase : List[str] = classifier_dropout
class __snake_case ( _lowercase):
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
"""simple docstring"""
if self.task == "multiple-choice":
_lowerCamelCase : Optional[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_lowerCamelCase : Union[str, Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 83
|
class __snake_case :
"""simple docstring"""
def __init__( self : Union[str, Any] ,lowerCAmelCase__ : str = "" ,lowerCAmelCase__ : bool = False ) -> None:
'''simple docstring'''
lowerCAmelCase_ : dict[str, RadixNode] = {}
# A node will be a leaf if the tree contains its word
lowerCAmelCase_ : Optional[int] = is_leaf
lowerCAmelCase_ : List[str] = prefix
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : str ) -> tuple[str, str, str]:
'''simple docstring'''
lowerCAmelCase_ : List[str] = 0
for q, w in zip(self.prefix ,lowerCAmelCase__ ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ,lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
if self.prefix == word:
lowerCAmelCase_ : Optional[Any] = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
lowerCAmelCase_ : Optional[int] = RadixNode(prefix=lowerCAmelCase__ ,is_leaf=lowerCAmelCase__ )
else:
lowerCAmelCase_ : Optional[Any] = self.nodes[word[0]]
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Any = incoming_node.match(
lowerCAmelCase__ )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
lowerCAmelCase_ : Dict = remaining_prefix
lowerCAmelCase_ : str = self.nodes[matching_string[0]]
lowerCAmelCase_ : Dict = RadixNode(lowerCAmelCase__ ,lowerCAmelCase__ )
lowerCAmelCase_ : Any = aux_node
if remaining_word == "":
lowerCAmelCase_ : Optional[Any] = True
else:
self.nodes[matching_string[0]].insert(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : List[str] = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : str ) -> bool:
'''simple docstring'''
lowerCAmelCase_ : int = self.nodes.get(word[0] ,lowerCAmelCase__ )
if not incoming_node:
return False
else:
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : List[Any] = incoming_node.match(
lowerCAmelCase__ )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(lowerCAmelCase__ )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
lowerCAmelCase_ : int = list(self.nodes.values() )[0]
lowerCAmelCase_ : List[Any] = merging_node.is_leaf
self.prefix += merging_node.prefix
lowerCAmelCase_ : int = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
lowerCAmelCase_ : List[str] = False
# If there is 1 edge, we merge it with its child
else:
lowerCAmelCase_ : Union[str, Any] = list(incoming_node.nodes.values() )[0]
lowerCAmelCase_ : Optional[int] = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
lowerCAmelCase_ : List[str] = merging_node.nodes
return True
def UpperCAmelCase_ ( self : int ,lowerCAmelCase__ : int = 0 ) -> None:
'''simple docstring'''
if self.prefix != "":
print("-" * height ,self.prefix ," (leaf)" if self.is_leaf else "" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def UpperCamelCase ( ):
lowerCAmelCase_ : List[Any] = "banana bananas bandana band apple all beast".split()
lowerCAmelCase_ : Optional[Any] = RadixNode()
root.insert_many(snake_case__)
assert all(root.find(snake_case__) for word in words)
assert not root.find("bandanas")
assert not root.find("apps")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def UpperCamelCase ( ):
assert test_trie()
def UpperCamelCase ( ):
lowerCAmelCase_ : str = RadixNode()
lowerCAmelCase_ : str = "banana bananas bandanas bandana band apple all beast".split()
root.insert_many(snake_case__)
print("Words:" , snake_case__)
print("Tree:")
root.print_tree()
if __name__ == "__main__":
main()
| 659
| 0
|
'''simple docstring'''
import sys
_lowercase = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def A (__lowerCamelCase :str = N ):
_lowerCAmelCase = -sys.maxsize - 1
for i in range(len(__lowerCamelCase ) - 12 ):
_lowerCAmelCase = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_lowerCAmelCase = product
return largest_product
if __name__ == "__main__":
print(F"""{solution() = }""")
| 162
|
'''simple docstring'''
from math import pi, sqrt
def A (__lowerCamelCase :float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError("""num must be an integer or a half-integer""" )
elif num == 0.5:
return sqrt(__lowerCamelCase )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def A ():
assert gamma(0.5 ) == sqrt(__lowerCamelCase )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
_lowercase = 1.0
while num:
_lowercase = float(input("""Gamma of: """))
print(F"""gamma({num}) = {gamma(num)}""")
print("""\nEnter 0 to exit...""")
| 162
| 1
|
import unittest
from transformers import BigBirdConfig, 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
from transformers.models.big_bird.modeling_flax_big_bird import (
FlaxBigBirdForCausalLM,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForPreTraining,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
FlaxBigBirdModel,
)
class UpperCAmelCase__ ( unittest.TestCase ):
def __init__( self ,A__ ,A__=2 ,A__=56 ,A__=True ,A__=True ,A__=True ,A__=True ,A__=99 ,A__=32 ,A__=2 ,A__=2 ,A__=7 ,A__="gelu_new" ,A__=0.1 ,A__=0.1 ,A__=512 ,A__=16 ,A__=2 ,A__=0.02 ,A__=4 ,A__="block_sparse" ,A__=True ,A__=False ,A__=2 ,A__=3 ,):
_A : Tuple = parent
_A : List[Any] = batch_size
_A : Dict = seq_length
_A : Tuple = is_training
_A : Optional[Any] = use_attention_mask
_A : Tuple = use_token_type_ids
_A : Optional[int] = use_labels
_A : Optional[int] = vocab_size
_A : Optional[Any] = hidden_size
_A : List[str] = num_hidden_layers
_A : Optional[int] = num_attention_heads
_A : int = intermediate_size
_A : Optional[int] = hidden_act
_A : Tuple = hidden_dropout_prob
_A : Optional[Any] = attention_probs_dropout_prob
_A : Optional[Any] = max_position_embeddings
_A : List[Any] = type_vocab_size
_A : Tuple = type_sequence_label_size
_A : str = initializer_range
_A : Any = num_choices
_A : Tuple = rescale_embeddings
_A : Tuple = attention_type
_A : Tuple = use_bias
_A : Any = block_size
_A : Optional[int] = num_random_blocks
def A__ ( self ):
_A : List[str] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
_A : Optional[Any] = None
if self.use_attention_mask:
_A : int = random_attention_mask([self.batch_size, self.seq_length] )
_A : Tuple = None
if self.use_token_type_ids:
_A : Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
_A : Tuple = BigBirdConfig(
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 ,attention_type=self.attention_type ,block_size=self.block_size ,num_random_blocks=self.num_random_blocks ,use_bias=self.use_bias ,rescale_embeddings=self.rescale_embeddings ,)
return config, input_ids, token_type_ids, attention_mask
def A__ ( self ):
_A : Union[str, Any] = self.prepare_config_and_inputs()
_A , _A , _A , _A : List[Any] = config_and_inputs
_A : Dict = {
'''input_ids''': input_ids,
'''token_type_ids''': token_type_ids,
'''attention_mask''': attention_mask,
}
return config, inputs_dict
@require_flax
class UpperCAmelCase__ ( __snake_case , unittest.TestCase ):
__snake_case : Any = (
(
FlaxBigBirdForCausalLM,
FlaxBigBirdModel,
FlaxBigBirdForPreTraining,
FlaxBigBirdForMaskedLM,
FlaxBigBirdForMultipleChoice,
FlaxBigBirdForQuestionAnswering,
FlaxBigBirdForSequenceClassification,
FlaxBigBirdForTokenClassification,
)
if is_flax_available()
else ()
)
__snake_case : str = False
__snake_case : Optional[Any] = False
def A__ ( self ):
_A : Dict = FlaxBigBirdModelTester(self )
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def A__ ( self ):
super().test_from_pretrained_save_pretrained()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def A__ ( self ):
super().test_from_pretrained_with_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def A__ ( self ):
super().test_no_automatic_init()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def A__ ( self ):
super().test_hidden_states_output()
@slow
def A__ ( self ):
for model_class_name in self.all_model_classes:
_A : str = model_class_name.from_pretrained('''google/bigbird-roberta-base''' )
self.assertIsNotNone(A__ )
def A__ ( self ):
if self.test_attn_probs:
super().test_attention_outputs()
@slow
# copied from `test_modeling_flax_common` because it takes much longer than other models
def A__ ( self ):
_A , _A : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_A : Dict = self._prepare_for_class(A__ ,A__ )
_A : Any = model_class(A__ )
@jax.jit
def model_jitted(A__ ,A__=None ,**A__ ):
return model(input_ids=A__ ,attention_mask=A__ ,**A__ )
with self.subTest('''JIT Enabled''' ):
_A : Dict = model_jitted(**A__ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_A : List[str] = model_jitted(**A__ ).to_tuple()
self.assertEqual(len(A__ ) ,len(A__ ) )
for jitted_output, output in zip(A__ ,A__ ):
self.assertEqual(jitted_output.shape ,output.shape )
def A__ ( self ,A__ ,A__ ,A__ ,A__=1E-5 ,A__="outputs" ,A__=None ):
# `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
# an effort was done to return `attention_probs` (yet to be verified).
if name.startswith('''outputs.attentions''' ):
return
else:
super().check_pt_flax_outputs(A__ ,A__ ,A__ ,A__ ,A__ ,A__ )
| 206
|
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase__ ( __snake_case ):
__snake_case : Optional[Any] = ["image_processor", "tokenizer"]
__snake_case : Tuple = "BridgeTowerImageProcessor"
__snake_case : List[str] = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self ,A__ ,A__ ):
super().__init__(A__ ,A__ )
def __call__( self ,A__ ,A__ = None ,A__ = True ,A__ = False ,A__ = None ,A__ = None ,A__ = 0 ,A__ = None ,A__ = None ,A__ = None ,A__ = False ,A__ = False ,A__ = False ,A__ = False ,A__ = True ,A__ = None ,**A__ ,):
_A : List[Any] = self.tokenizer(
text=A__ ,add_special_tokens=A__ ,padding=A__ ,truncation=A__ ,max_length=A__ ,stride=A__ ,pad_to_multiple_of=A__ ,return_token_type_ids=A__ ,return_attention_mask=A__ ,return_overflowing_tokens=A__ ,return_special_tokens_mask=A__ ,return_offsets_mapping=A__ ,return_length=A__ ,verbose=A__ ,return_tensors=A__ ,**A__ ,)
# add pixel_values + pixel_mask
_A : Optional[Any] = self.image_processor(
A__ ,return_tensors=A__ ,do_normalize=A__ ,do_center_crop=A__ ,**A__ )
encoding.update(A__ )
return encoding
def A__ ( self ,*A__ ,**A__ ):
return self.tokenizer.batch_decode(*A__ ,**A__ )
def A__ ( self ,*A__ ,**A__ ):
return self.tokenizer.decode(*A__ ,**A__ )
@property
def A__ ( self ):
_A : Union[str, Any] = self.tokenizer.model_input_names
_A : str = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 206
| 1
|
"""simple docstring"""
from math import ceil
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->Optional[Any]:
"""simple docstring"""
__lowercase : List[Any] = list(range(0, _lowerCamelCase ) )
__lowercase : Any = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
__lowercase : str = []
for i in device_map_blocks:
if device_map_blocks.count(_lowerCamelCase ) > 1 and i not in duplicate_blocks:
duplicate_blocks.append(_lowerCamelCase )
# Missing blocks
__lowercase : List[str] = [i for i in blocks if i not in device_map_blocks]
__lowercase : Dict = [i for i in device_map_blocks if i not in blocks]
if len(_lowerCamelCase ) != 0:
raise ValueError(
"Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
" These attention blocks were specified more than once: " + str(_lowerCamelCase ) )
if len(_lowerCamelCase ) != 0:
raise ValueError(
"There are attention blocks for this model that are not specified in the device_map. Add these attention "
"blocks to a device on the device_map: " + str(_lowerCamelCase ) )
if len(_lowerCamelCase ) != 0:
raise ValueError(
"The device_map contains more attention blocks than this model has. Remove these from the device_map:"
+ str(_lowerCamelCase ) )
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->Any:
"""simple docstring"""
__lowercase : Union[str, Any] = list(range(_lowerCamelCase ) )
__lowercase : List[str] = int(ceil(n_layers / len(_lowerCamelCase ) ) )
__lowercase : Optional[int] = [layers[i : i + n_blocks] for i in range(0, _lowerCamelCase, _lowerCamelCase )]
return dict(zip(_lowerCamelCase, _lowerCamelCase ) )
| 718
|
"""simple docstring"""
from __future__ import annotations
def snake_case__ ( _lowerCamelCase, _lowerCamelCase = None ) ->list[list[str]]:
"""simple docstring"""
__lowercase : List[Any] = word_bank or []
# create a table
__lowercase : int = len(_lowerCamelCase ) + 1
__lowercase : list[list[list[str]]] = []
for _ in range(_lowerCamelCase ):
table.append([] )
# seed value
__lowercase : Any = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(_lowerCamelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(_lowerCamelCase )] == word:
__lowercase : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(_lowerCamelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(_lowerCamelCase )]:
combination.reverse()
return table[len(_lowerCamelCase )]
if __name__ == "__main__":
print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
print(
all_construct(
'hexagonosaurus',
['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
)
)
| 281
| 0
|
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCAmelCase :List[Any] = logging.get_logger(__name__)
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : Optional[int] = ["""input_features""", """attention_mask"""]
def __init__( self : int , _A : List[Any]=80 , _A : Tuple=16000 , _A : Optional[int]=80 , _A : Dict=0.0 , _A : str=True , _A : Optional[Any]=True , _A : Dict=True , **_A : Dict , ) -> Any:
super().__init__(feature_size=_A , sampling_rate=_A , padding_value=_A , **_A )
__magic_name__ : Optional[Any] = num_mel_bins
__magic_name__ : Optional[int] = do_ceptral_normalize
__magic_name__ : int = normalize_means
__magic_name__ : Dict = normalize_vars
__magic_name__ : int = True
def __lowerCAmelCase ( self : str , _A : np.ndarray , ) -> np.ndarray:
__magic_name__ : List[str] = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
__magic_name__ : Tuple = torch.from_numpy(_A ).unsqueeze(0 )
__magic_name__ : Optional[int] = ta_kaldi.fbank(_A , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def __lowerCAmelCase ( _A : np.ndarray , _A : int , _A : Optional[bool] = True , _A : Optional[bool] = True , _A : float = 0.0 , ) -> np.ndarray:
# make sure we normalize float32 arrays
if normalize_means:
__magic_name__ : int = x[:input_length].mean(axis=0 )
__magic_name__ : Dict = np.subtract(_A , _A )
if normalize_vars:
__magic_name__ : Dict = x[:input_length].std(axis=0 )
__magic_name__ : List[Any] = np.divide(_A , _A )
if input_length < x.shape[0]:
__magic_name__ : Optional[int] = padding_value
# make sure array is in float32
__magic_name__ : str = x.astype(np.floataa )
return x
def __lowerCAmelCase ( self : Any , _A : List[np.ndarray] , _A : Optional[np.ndarray] = None ) -> List[np.ndarray]:
__magic_name__ : Dict = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(_A , _A , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(_A , _A )
]
def __call__( self : Tuple , _A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , _A : Union[bool, str, PaddingStrategy] = False , _A : Optional[int] = None , _A : bool = False , _A : Optional[int] = None , _A : Optional[Union[str, TensorType]] = None , _A : Optional[int] = None , _A : Optional[bool] = None , **_A : List[Any] , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
F' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
__magic_name__ : Any = isinstance(_A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F'Only mono-channel audio is supported for input to {self}' )
__magic_name__ : Dict = is_batched_numpy or (
isinstance(_A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
__magic_name__ : List[str] = [np.asarray(_A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(_A , np.ndarray ):
__magic_name__ : List[Any] = np.asarray(_A , dtype=np.floataa )
elif isinstance(_A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
__magic_name__ : List[str] = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
__magic_name__ : Any = [raw_speech]
# extract fbank features
__magic_name__ : Any = [self._extract_fbank_features(_A ) for waveform in raw_speech]
# convert into correct format for padding
__magic_name__ : int = BatchFeature({'input_features': features} )
__magic_name__ : str = self.pad(
_A , padding=_A , max_length=_A , truncation=_A , pad_to_multiple_of=_A , return_attention_mask=_A , **_A , )
# make sure list is in array format
__magic_name__ : Optional[int] = padded_inputs.get('input_features' )
if isinstance(input_features[0] , _A ):
__magic_name__ : List[str] = [np.asarray(_A , dtype=np.floataa ) for feature in input_features]
__magic_name__ : Optional[Any] = padded_inputs.get('attention_mask' )
if attention_mask is not None:
__magic_name__ : Dict = [np.asarray(_A , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
__magic_name__ : Dict = (
np.array(_A , dtype=np.intaa )
if self._get_padding_strategies(_A , max_length=_A ) is not PaddingStrategy.DO_NOT_PAD
else None
)
__magic_name__ : Dict = self.normalize(
padded_inputs['input_features'] , attention_mask=_A )
if return_tensors is not None:
__magic_name__ : List[Any] = padded_inputs.convert_to_tensors(_A )
return padded_inputs
| 561
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = ViTImageProcessor if is_vision_available() else None
@property
def __lowerCAmelCase ( self : str ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : List[str] ) -> Optional[Any]:
__magic_name__ : Dict = (3, 32, 128)
__magic_name__ : Any = tempfile.mkdtemp()
# fmt: off
__magic_name__ : Optional[int] = ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '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']
# fmt: on
__magic_name__ : Union[str, Any] = dict(zip(_A , range(len(_A ) ) ) )
__magic_name__ : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(_A ) + '\n' )
__magic_name__ : Tuple = {
'do_normalize': False,
'do_resize': True,
'image_processor_type': 'ViTImageProcessor',
'resample': 3,
'size': {'height': 32, 'width': 128},
}
__magic_name__ : Union[str, Any] = os.path.join(self.tmpdirname , _A )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(_A , _A )
def __lowerCAmelCase ( self : str , **_A : Optional[int] ) -> List[str]:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_A )
def __lowerCAmelCase ( self : int , **_A : Optional[int] ) -> List[Any]:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_A )
def __lowerCAmelCase ( self : Dict ) -> Tuple:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self : Dict ) -> Any:
__magic_name__ : str = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
__magic_name__ : List[Any] = Image.fromarray(np.moveaxis(_A , 0 , -1 ) )
return image_input
def __lowerCAmelCase ( self : List[str] ) -> List[Any]:
__magic_name__ : Union[str, Any] = self.get_tokenizer()
__magic_name__ : Union[str, Any] = self.get_image_processor()
__magic_name__ : List[str] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : Optional[Any] = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_A )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def __lowerCAmelCase ( self : List[str] ) -> Optional[int]:
__magic_name__ : int = self.get_tokenizer()
__magic_name__ : int = self.get_image_processor()
__magic_name__ : int = MgpstrProcessor(tokenizer=_A , image_processor=_A )
processor.save_pretrained(self.tmpdirname )
__magic_name__ : Any = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
__magic_name__ : Optional[Any] = self.get_image_processor(do_normalize=_A , padding_value=1.0 )
__magic_name__ : List[str] = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=_A , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _A )
def __lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
__magic_name__ : Any = self.get_image_processor()
__magic_name__ : Optional[Any] = self.get_tokenizer()
__magic_name__ : Optional[int] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : List[str] = self.prepare_image_inputs()
__magic_name__ : str = image_processor(_A , return_tensors='np' )
__magic_name__ : Tuple = processor(images=_A , 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[Any] ) -> List[Any]:
__magic_name__ : Optional[int] = self.get_image_processor()
__magic_name__ : int = self.get_tokenizer()
__magic_name__ : Optional[int] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : Union[str, Any] = 'test'
__magic_name__ : Optional[Any] = processor(text=_A )
__magic_name__ : int = tokenizer(_A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __lowerCAmelCase ( self : int ) -> int:
__magic_name__ : Union[str, Any] = self.get_image_processor()
__magic_name__ : str = self.get_tokenizer()
__magic_name__ : List[Any] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : Union[str, Any] = 'test'
__magic_name__ : str = self.prepare_image_inputs()
__magic_name__ : Dict = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , ['pixel_values', 'labels'] )
# test if it raises when no input is passed
with pytest.raises(_A ):
processor()
def __lowerCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
__magic_name__ : Dict = self.get_image_processor()
__magic_name__ : Optional[Any] = self.get_tokenizer()
__magic_name__ : Optional[Any] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : Any = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
__magic_name__ : str = processor.char_decode(_A )
__magic_name__ : Tuple = tokenizer.batch_decode(_A )
__magic_name__ : Union[str, Any] = [seq.replace(' ' , '' ) for seq in decoded_tok]
self.assertListEqual(_A , _A )
def __lowerCAmelCase ( self : Optional[Any] ) -> Any:
__magic_name__ : int = self.get_image_processor()
__magic_name__ : Tuple = self.get_tokenizer()
__magic_name__ : Any = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : int = None
__magic_name__ : Tuple = self.prepare_image_inputs()
__magic_name__ : Dict = processor(text=_A , images=_A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def __lowerCAmelCase ( self : List[str] ) -> Dict:
__magic_name__ : Any = self.get_image_processor()
__magic_name__ : Tuple = self.get_tokenizer()
__magic_name__ : List[str] = MgpstrProcessor(tokenizer=_A , image_processor=_A )
__magic_name__ : List[str] = torch.randn(1 , 27 , 38 )
__magic_name__ : Optional[Any] = torch.randn(1 , 27 , 50257 )
__magic_name__ : Optional[int] = torch.randn(1 , 27 , 30522 )
__magic_name__ : List[Any] = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ['generated_text', 'scores', 'char_preds', 'bpe_preds', 'wp_preds'] )
| 561
| 1
|
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : int = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class __magic_name__ ( a__):
UpperCamelCase__ = '''mctct'''
def __init__( self : Tuple , lowercase_ : Any=8065 , lowercase_ : int=1536 , lowercase_ : Tuple=36 , lowercase_ : int=6144 , lowercase_ : Optional[int]=4 , lowercase_ : List[Any]=384 , lowercase_ : Optional[Any]=920 , lowercase_ : int=1E-5 , lowercase_ : Tuple=0.3 , lowercase_ : List[str]="relu" , lowercase_ : Any=0.02 , lowercase_ : Optional[Any]=0.3 , lowercase_ : int=0.3 , lowercase_ : int=1 , lowercase_ : Dict=0 , lowercase_ : List[str]=2 , lowercase_ : Dict=1 , lowercase_ : List[Any]=0.3 , lowercase_ : List[Any]=1 , lowercase_ : Optional[Any]=(7,) , lowercase_ : List[str]=(3,) , lowercase_ : List[str]=80 , lowercase_ : List[Any]=1 , lowercase_ : Union[str, Any]=None , lowercase_ : Union[str, Any]="sum" , lowercase_ : Tuple=False , **lowercase_ : Optional[Any] , ):
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
lowercase_ : List[Any] = vocab_size
lowercase_ : Optional[Any] = hidden_size
lowercase_ : List[str] = num_hidden_layers
lowercase_ : Union[str, Any] = intermediate_size
lowercase_ : List[str] = num_attention_heads
lowercase_ : Optional[Any] = attention_head_dim
lowercase_ : Optional[int] = max_position_embeddings
lowercase_ : List[str] = layer_norm_eps
lowercase_ : Dict = layerdrop
lowercase_ : Optional[Any] = hidden_act
lowercase_ : Tuple = initializer_range
lowercase_ : Optional[int] = hidden_dropout_prob
lowercase_ : Dict = attention_probs_dropout_prob
lowercase_ : Union[str, Any] = pad_token_id
lowercase_ : Any = bos_token_id
lowercase_ : Union[str, Any] = eos_token_id
lowercase_ : str = conv_glu_dim
lowercase_ : int = conv_dropout
lowercase_ : str = num_conv_layers
lowercase_ : Union[str, Any] = input_feat_per_channel
lowercase_ : Any = input_channels
lowercase_ : Optional[int] = conv_channels
lowercase_ : str = ctc_loss_reduction
lowercase_ : str = ctc_zero_infinity
# prevents config testing fail with exporting to json
lowercase_ : Any = list(_A )
lowercase_ : List[Any] = list(_A )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
f'''but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, '''
f'''`config.num_conv_layers = {self.num_conv_layers}`.''' )
| 716
|
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = field(default='''image-classification''', metadata={'''include_in_asdict_even_if_is_default''': True})
UpperCamelCase__ = Features({'''image''': Image()})
UpperCamelCase__ = Features({'''labels''': ClassLabel})
UpperCamelCase__ = "image"
UpperCamelCase__ = "labels"
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str ):
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , lowercase_ ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
lowercase_ : List[str] = copy.deepcopy(self )
lowercase_ : List[str] = self.label_schema.copy()
lowercase_ : List[Any] = features[self.label_column]
lowercase_ : Optional[Any] = label_schema
return task_template
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return {
self.image_column: "image",
self.label_column: "labels",
}
| 30
| 0
|
from collections.abc import Sequence
def lowerCAmelCase_ ( __UpperCAmelCase: Sequence[float] , __UpperCAmelCase: bool = False ) -> float:
if not arr:
return 0
UpperCamelCase__ : Union[str, Any] = 0 if allow_empty_subarrays else float('''-inf''' )
UpperCamelCase__ : Optional[int] = 0.0
for num in arr:
UpperCamelCase__ : List[Any] = max(0 if allow_empty_subarrays else num , curr_sum + num )
UpperCamelCase__ : str = max(__UpperCAmelCase , __UpperCAmelCase )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
UpperCAmelCase_ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(F'''{max_subarray_sum(nums) = }''')
| 253
|
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ :
'''simple docstring'''
def __init__( self, __magic_name__, __magic_name__=3, __magic_name__=32, __magic_name__=3, __magic_name__=10, __magic_name__=[10, 20, 30, 40], __magic_name__=[1, 1, 2, 1], __magic_name__=True, __magic_name__=True, __magic_name__="relu", __magic_name__=3, __magic_name__=None, ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = parent
UpperCamelCase__ : Tuple = batch_size
UpperCamelCase__ : int = image_size
UpperCamelCase__ : Tuple = num_channels
UpperCamelCase__ : Union[str, Any] = embeddings_size
UpperCamelCase__ : Dict = hidden_sizes
UpperCamelCase__ : Any = depths
UpperCamelCase__ : List[str] = is_training
UpperCamelCase__ : Optional[int] = use_labels
UpperCamelCase__ : Optional[int] = hidden_act
UpperCamelCase__ : List[Any] = num_labels
UpperCamelCase__ : Optional[int] = scope
UpperCamelCase__ : int = len(__magic_name__ )
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase__ : str = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ : List[str] = None
if self.use_labels:
UpperCamelCase__ : Dict = ids_tensor([self.batch_size], self.num_labels )
UpperCamelCase__ : Dict = self.get_config()
return config, pixel_values, labels
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
return ResNetConfig(
num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, image_size=self.image_size, )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : str = TFResNetModel(config=__magic_name__ )
UpperCamelCase__ : Dict = model(__magic_name__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), )
def UpperCamelCase__ ( self, __magic_name__, __magic_name__, __magic_name__ ) -> Any:
"""simple docstring"""
UpperCamelCase__ : int = self.num_labels
UpperCamelCase__ : List[Any] = TFResNetForImageClassification(__magic_name__ )
UpperCamelCase__ : int = model(__magic_name__, labels=__magic_name__ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase__ : List[Any] = self.prepare_config_and_inputs()
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Any = config_and_inputs
UpperCamelCase__ : Dict = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowercase__ ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
a : Optional[Any] = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
a : Optional[int] = (
{"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
if is_tf_available()
else {}
)
a : int = False
a : List[str] = False
a : Optional[Any] = False
a : Tuple = False
a : List[str] = False
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase__ : Tuple = TFResNetModelTester(self )
UpperCamelCase__ : Tuple = ConfigTester(self, config_class=__magic_name__, has_text_modality=__magic_name__ )
def UpperCamelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
return
@unittest.skip(reason='''ResNet does not use inputs_embeds''' )
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip(reason='''ResNet does not support input and output embeddings''' )
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
pass
def UpperCamelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ ,UpperCamelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ : Tuple = model_class(__magic_name__ )
UpperCamelCase__ : List[Any] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ : Dict = [*signature.parameters.keys()]
UpperCamelCase__ : Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1], __magic_name__ )
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__magic_name__ )
def UpperCamelCase__ ( self ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(__magic_name__, __magic_name__, __magic_name__ ):
UpperCamelCase__ : List[str] = model_class(__magic_name__ )
UpperCamelCase__ : Optional[Any] = model(**self._prepare_for_class(__magic_name__, __magic_name__ ) )
UpperCamelCase__ : Optional[int] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCamelCase__ : Tuple = self.model_tester.num_stages
self.assertEqual(len(__magic_name__ ), expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], )
UpperCamelCase__ ,UpperCamelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ : int = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCamelCase__ : Any = layer_type
UpperCamelCase__ : Union[str, Any] = True
check_hidden_states_output(__magic_name__, __magic_name__, __magic_name__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__ : Any = True
check_hidden_states_output(__magic_name__, __magic_name__, __magic_name__ )
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__magic_name__ )
@slow
def UpperCamelCase__ ( self ) -> Tuple:
"""simple docstring"""
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ : Any = TFResNetModel.from_pretrained(__magic_name__ )
self.assertIsNotNone(__magic_name__ )
def lowerCAmelCase_ ( ) -> List[Any]:
UpperCamelCase__ : Optional[Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : List[Any] = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCamelCase__ : Any = self.default_image_processor
UpperCamelCase__ : str = prepare_img()
UpperCamelCase__ : Union[str, Any] = image_processor(images=__magic_name__, return_tensors='''tf''' )
# forward pass
UpperCamelCase__ : str = model(**__magic_name__ )
# verify the logits
UpperCamelCase__ : Union[str, Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape, __magic_name__ )
UpperCamelCase__ : List[Any] = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), __magic_name__, atol=1E-4 ) )
| 253
| 1
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( __A , __A , __A , unittest.TestCase):
__SCREAMING_SNAKE_CASE : Optional[Any] = AltDiffusionPipeline
__SCREAMING_SNAKE_CASE : int = TEXT_TO_IMAGE_PARAMS
__SCREAMING_SNAKE_CASE : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
__SCREAMING_SNAKE_CASE : str = TEXT_TO_IMAGE_IMAGE_PARAMS
__SCREAMING_SNAKE_CASE : Union[str, Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase__ ( self : str ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
_UpperCAmelCase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5_002 , )
_UpperCAmelCase = CLIPTextModel(UpperCamelCase__ )
_UpperCAmelCase = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
_UpperCAmelCase = 77
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCAmelCase__ ( self : str , __UpperCamelCase : List[str] , __UpperCamelCase : Union[str, Any]=0 ):
if str(UpperCamelCase__ ).startswith("mps" ):
_UpperCAmelCase = torch.manual_seed(UpperCamelCase__ )
else:
_UpperCAmelCase = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
_UpperCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCAmelCase__ ( self : List[Any] ):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3 )
def UpperCAmelCase__ ( self : str ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def UpperCAmelCase__ ( self : str ):
_UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
torch.manual_seed(0 )
_UpperCAmelCase = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_002 , )
# TODO: remove after fixing the non-deterministic text encoder
_UpperCAmelCase = RobertaSeriesModelWithTransformation(UpperCamelCase__ )
_UpperCAmelCase = text_encoder
_UpperCAmelCase = AltDiffusionPipeline(**UpperCamelCase__ )
_UpperCAmelCase = alt_pipe.to(UpperCamelCase__ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
_UpperCAmelCase = self.get_dummy_inputs(UpperCamelCase__ )
_UpperCAmelCase = 'A photo of an astronaut'
_UpperCAmelCase = alt_pipe(**UpperCamelCase__ )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array(
[0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self : Union[str, Any] ):
_UpperCAmelCase = 'cpu' # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
torch.manual_seed(0 )
_UpperCAmelCase = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5_002 , )
# TODO: remove after fixing the non-deterministic text encoder
_UpperCAmelCase = RobertaSeriesModelWithTransformation(UpperCamelCase__ )
_UpperCAmelCase = text_encoder
_UpperCAmelCase = AltDiffusionPipeline(**UpperCamelCase__ )
_UpperCAmelCase = alt_pipe.to(UpperCamelCase__ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
_UpperCAmelCase = self.get_dummy_inputs(UpperCamelCase__ )
_UpperCAmelCase = alt_pipe(**UpperCamelCase__ )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array(
[0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCAmelCase__ ( self : List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self : Optional[int] ):
# make sure here that pndm scheduler skips prk
_UpperCAmelCase = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" , safety_checker=UpperCamelCase__ )
_UpperCAmelCase = alt_pipe.to(UpperCamelCase__ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = alt_pipe([prompt] , generator=UpperCamelCase__ , guidance_scale=6.0 , num_inference_steps=20 , output_type="np" )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self : Dict ):
_UpperCAmelCase = DDIMScheduler.from_pretrained("BAAI/AltDiffusion" , subfolder="scheduler" )
_UpperCAmelCase = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" , scheduler=UpperCamelCase__ , safety_checker=UpperCamelCase__ )
_UpperCAmelCase = alt_pipe.to(UpperCamelCase__ )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = alt_pipe([prompt] , generator=UpperCamelCase__ , num_inference_steps=2 , output_type="numpy" )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 709
|
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__lowerCAmelCase = pd.read_csv("sample_data.csv", header=None)
__lowerCAmelCase = df.shape[:1][0]
# If you're using some other dataset input the target column
__lowerCAmelCase = df.iloc[:, 1:2]
__lowerCAmelCase = actual_data.values.reshape(len_data, 1)
__lowerCAmelCase = MinMaxScaler().fit_transform(actual_data)
__lowerCAmelCase = 1_0
__lowerCAmelCase = 5
__lowerCAmelCase = 2_0
__lowerCAmelCase = len_data - periods * look_back
__lowerCAmelCase = actual_data[:division]
__lowerCAmelCase = actual_data[division - look_back :]
__lowerCAmelCase , __lowerCAmelCase = [], []
__lowerCAmelCase , __lowerCAmelCase = [], []
for i in range(0, len(train_data) - forward_days - look_back + 1):
train_x.append(train_data[i : i + look_back])
train_y.append(train_data[i + look_back : i + look_back + forward_days])
for i in range(0, len(test_data) - forward_days - look_back + 1):
test_x.append(test_data[i : i + look_back])
test_y.append(test_data[i + look_back : i + look_back + forward_days])
__lowerCAmelCase = np.array(train_x)
__lowerCAmelCase = np.array(test_x)
__lowerCAmelCase = np.array([list(i.ravel()) for i in train_y])
__lowerCAmelCase = np.array([list(i.ravel()) for i in test_y])
__lowerCAmelCase = Sequential()
model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
model.add(Dense(forward_days))
model.compile(loss="mean_squared_error", optimizer="adam")
__lowerCAmelCase = model.fit(
x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
)
__lowerCAmelCase = model.predict(x_test)
| 129
| 0
|
from PIL import Image
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> Image:
"""simple docstring"""
def brightness(snake_case_ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' )
return img.point(lowerCAmelCase__ )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change brightness to 100
UpperCamelCase__ : Tuple = change_brightness(img, 100)
brigt_img.save("""image_data/lena_brightness.png""", format="""png""")
| 387
|
"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class _A ( lowerCAmelCase ):
snake_case__ : List[Any] = 'philschmid/bart-large-cnn-samsum'
snake_case__ : int = (
'This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '
'and returns a summary of the text.'
)
snake_case__ : Tuple = 'summarizer'
snake_case__ : int = AutoTokenizer
snake_case__ : Any = AutoModelForSeqaSeqLM
snake_case__ : Optional[int] = ['text']
snake_case__ : Optional[int] = ['text']
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
return self.pre_processor(__lowerCAmelCase , return_tensors="""pt""" , truncation=__lowerCAmelCase )
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
return self.model.generate(**__lowerCAmelCase )[0]
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
return self.pre_processor.decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
| 359
| 0
|
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None ):
'''simple docstring'''
if version.parse(hfh.__version__ ).release < version.parse("""0.11.0""" ).release:
# old versions of hfh don't url-encode the file path
A_ : str = quote(__lowerCAmelCase )
return hfh.hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" , revision=__lowerCAmelCase )
| 718
|
'''simple docstring'''
import pytest
lowerCamelCase :Optional[Any] = '''__dummy_dataset1__'''
lowerCamelCase :List[Any] = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def a ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def a ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : List[str] = dataset_loading_script_name
A_ : int = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=lowerCamelCase__ )
A_ : Tuple = script_dir / f'{script_name}.py'
with open(lowerCamelCase__ , """w""" ) as f:
f.write(lowerCamelCase__ )
return str(lowerCamelCase__ )
| 686
| 0
|
def lowercase__ ( __snake_case : list , __snake_case : list ):
'''simple docstring'''
_validate_point(__snake_case )
_validate_point(__snake_case )
if len(__snake_case ) != len(__snake_case ):
raise ValueError('Both points must be in the same n-dimensional space' )
return float(sum(abs(a - b ) for a, b in zip(__snake_case , __snake_case ) ) )
def lowercase__ ( __snake_case : list[float] ):
'''simple docstring'''
if point:
if isinstance(__snake_case , __snake_case ):
for item in point:
if not isinstance(__snake_case , (int, float) ):
UpperCAmelCase_ : Optional[int] = (
'Expected a list of numbers as input, found '
F"{type(__snake_case ).__name__}"
)
raise TypeError(__snake_case )
else:
UpperCAmelCase_ : int = F"Expected a list of numbers as input, found {type(__snake_case ).__name__}"
raise TypeError(__snake_case )
else:
raise ValueError('Missing an input' )
def lowercase__ ( __snake_case : list , __snake_case : list ):
'''simple docstring'''
_validate_point(__snake_case )
_validate_point(__snake_case )
if len(__snake_case ) != len(__snake_case ):
raise ValueError('Both points must be in the same n-dimensional space' )
return float(sum(abs(x - y ) for x, y in zip(__snake_case , __snake_case ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 406
|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Tuple = ['''image_processor''', '''tokenizer''']
_snake_case : Any = '''ViTImageProcessor'''
_snake_case : str = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self , _UpperCamelCase=None , _UpperCamelCase=None , **_UpperCamelCase ) -> Optional[int]:
UpperCAmelCase_ : int = 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 , )
UpperCAmelCase_ : str = kwargs.pop('feature_extractor' )
UpperCAmelCase_ : Optional[int] = 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 )
def __call__( self , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None , _UpperCamelCase=None , **_UpperCamelCase ) -> Optional[Any]:
if text is None and visual_prompt is None and images is None:
raise ValueError('You have to specify either text, visual prompt or images.' )
if text is not None and visual_prompt is not None:
raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.' )
if text is not None:
UpperCAmelCase_ : int = self.tokenizer(_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase )
if visual_prompt is not None:
UpperCAmelCase_ : str = self.image_processor(_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase )
if images is not None:
UpperCAmelCase_ : Union[str, Any] = self.image_processor(_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase )
if visual_prompt is not None and images is not None:
UpperCAmelCase_ : Tuple = {
'pixel_values': image_features.pixel_values,
'conditional_pixel_values': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
UpperCAmelCase_ : List[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
UpperCAmelCase_ : Optional[Any] = {
'conditional_pixel_values': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**_UpperCamelCase ) , tensor_type=_UpperCamelCase )
def __UpperCAmelCase ( self , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[Any]:
return self.tokenizer.batch_decode(*_UpperCamelCase , **_UpperCamelCase )
def __UpperCAmelCase ( self , *_UpperCamelCase , **_UpperCamelCase ) -> int:
return self.tokenizer.decode(*_UpperCamelCase , **_UpperCamelCase )
@property
def __UpperCAmelCase ( self ) -> List[Any]:
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 __UpperCAmelCase ( self ) -> int:
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , _UpperCamelCase , )
return self.image_processor
| 406
| 1
|
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class lowercase_ ( lowerCamelCase__ ):
def UpperCamelCase ( self , lowercase_ ):
if isinstance(__lowerCamelCase , __lowerCamelCase ):
_snake_case : Dict = [label.strip() for label in labels.split("," ) if label.strip()]
return labels
def __call__( self , lowercase_ , lowercase_ , lowercase_ ):
if len(__lowerCamelCase ) == 0 or len(__lowerCamelCase ) == 0:
raise ValueError("You must include at least one label and at least one sequence." )
if hypothesis_template.format(labels[0] ) == hypothesis_template:
raise ValueError(
(
"The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
"Make sure the passed template includes formatting syntax such as {{}} where the label should go."
).format(__lowerCamelCase ) )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
_snake_case : Optional[Any] = [sequences]
_snake_case : Tuple = []
for sequence in sequences:
sequence_pairs.extend([[sequence, hypothesis_template.format(__lowerCamelCase )] for label in labels] )
return sequence_pairs, sequences
@add_end_docstrings(lowerCamelCase__ )
class lowercase_ ( lowerCamelCase__ ):
def __init__( self , lowercase_=ZeroShotClassificationArgumentHandler() , *lowercase_ , **lowercase_ ):
_snake_case : Optional[int] = args_parser
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
if self.entailment_id == -1:
logger.warning(
"Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to "
"-1. Define a descriptive label2id mapping in the model config to ensure correct outputs." )
@property
def UpperCamelCase ( self ):
for label, ind in self.model.config.labelaid.items():
if label.lower().startswith("entail" ):
return ind
return -1
def UpperCamelCase ( self , lowercase_ , lowercase_=True , lowercase_=True , lowercase_=TruncationStrategy.ONLY_FIRST , **lowercase_ ):
_snake_case : Dict = self.framework
if self.tokenizer.pad_token is None:
# Override for tokenizers not supporting padding
logger.error(
"Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
" `pad_token=eos_token`" )
_snake_case : List[Any] = self.tokenizer.eos_token
try:
_snake_case : str = self.tokenizer(
__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_tensors=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , )
except Exception as e:
if "too short" in str(__lowerCamelCase ):
# tokenizers might yell that we want to truncate
# to a value that is not even reached by the input.
# In that case we don't want to truncate.
# It seems there's not a really better way to catch that
# exception.
_snake_case : Optional[int] = self.tokenizer(
__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_tensors=__lowerCamelCase , padding=__lowerCamelCase , truncation=TruncationStrategy.DO_NOT_TRUNCATE , )
else:
raise e
return inputs
def UpperCamelCase ( self , **lowercase_ ):
if kwargs.get("multi_class" , __lowerCamelCase ) is not None:
_snake_case : Tuple = kwargs['''multi_class''']
logger.warning(
"The `multi_class` argument has been deprecated and renamed to `multi_label`. "
"`multi_class` will be removed in a future version of Transformers." )
_snake_case : Tuple = {}
if "candidate_labels" in kwargs:
_snake_case : Dict = self._args_parser._parse_labels(kwargs["candidate_labels"] )
if "hypothesis_template" in kwargs:
_snake_case : List[str] = kwargs['''hypothesis_template''']
_snake_case : Tuple = {}
if "multi_label" in kwargs:
_snake_case : List[Any] = kwargs['''multi_label''']
return preprocess_params, {}, postprocess_params
def __call__( self , lowercase_ , *lowercase_ , **lowercase_ , ):
if len(__lowerCamelCase ) == 0:
pass
elif len(__lowerCamelCase ) == 1 and "candidate_labels" not in kwargs:
_snake_case : Union[str, Any] = args[0]
else:
raise ValueError(f"""Unable to understand extra arguments {args}""" )
return super().__call__(__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase ( self , lowercase_ , lowercase_=None , lowercase_="This example is {}." ):
_snake_case : int = self._args_parser(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
for i, (candidate_label, sequence_pair) in enumerate(zip(__lowerCamelCase , __lowerCamelCase ) ):
_snake_case : Optional[int] = self._parse_and_tokenize([sequence_pair] )
yield {
"candidate_label": candidate_label,
"sequence": sequences[0],
"is_last": i == len(__lowerCamelCase ) - 1,
**model_input,
}
def UpperCamelCase ( self , lowercase_ ):
_snake_case : Any = inputs['''candidate_label''']
_snake_case : List[Any] = inputs['''sequence''']
_snake_case : Dict = {k: inputs[k] for k in self.tokenizer.model_input_names}
_snake_case : List[str] = self.model(**__lowerCamelCase )
_snake_case : Any = {
'''candidate_label''': candidate_label,
'''sequence''': sequence,
'''is_last''': inputs['''is_last'''],
**outputs,
}
return model_outputs
def UpperCamelCase ( self , lowercase_ , lowercase_=False ):
_snake_case : Optional[int] = [outputs['''candidate_label'''] for outputs in model_outputs]
_snake_case : int = [outputs['''sequence'''] for outputs in model_outputs]
_snake_case : Dict = np.concatenate([output["logits"].numpy() for output in model_outputs] )
_snake_case : List[str] = logits.shape[0]
_snake_case : Optional[int] = len(__lowerCamelCase )
_snake_case : List[Any] = N // n
_snake_case : List[str] = logits.reshape((num_sequences, n, -1) )
if multi_label or len(__lowerCamelCase ) == 1:
# softmax over the entailment vs. contradiction dim for each label independently
_snake_case : Optional[Any] = self.entailment_id
_snake_case : List[str] = -1 if entailment_id == 0 else 0
_snake_case : Optional[Any] = reshaped_outputs[..., [contradiction_id, entailment_id]]
_snake_case : Dict = np.exp(__lowerCamelCase ) / np.exp(__lowerCamelCase ).sum(-1 , keepdims=__lowerCamelCase )
_snake_case : List[Any] = scores[..., 1]
else:
# softmax the "entailment" logits over all candidate labels
_snake_case : List[Any] = reshaped_outputs[..., self.entailment_id]
_snake_case : str = np.exp(__lowerCamelCase ) / np.exp(__lowerCamelCase ).sum(-1 , keepdims=__lowerCamelCase )
_snake_case : List[Any] = list(reversed(scores[0].argsort() ) )
return {
"sequence": sequences[0],
"labels": [candidate_labels[i] for i in top_inds],
"scores": scores[0, top_inds].tolist(),
}
| 702
|
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class lowercase_ ( datasets.BuilderConfig ):
_lowerCamelCase = None
class lowercase_ ( datasets.ArrowBasedBuilder ):
_lowerCamelCase = PandasConfig
def UpperCamelCase ( self ):
return datasets.DatasetInfo(features=self.config.features )
def UpperCamelCase ( self , lowercase_ ):
if not self.config.data_files:
raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
_snake_case : Any = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowercase_ , (str, list, tuple) ):
_snake_case : str = data_files
if isinstance(lowercase_ , lowercase_ ):
_snake_case : int = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_snake_case : Optional[Any] = [dl_manager.iter_files(lowercase_ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
_snake_case : Optional[Any] = []
for split_name, files in data_files.items():
if isinstance(lowercase_ , lowercase_ ):
_snake_case : Tuple = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
_snake_case : int = [dl_manager.iter_files(lowercase_ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowercase_ , gen_kwargs={"files": files} ) )
return splits
def UpperCamelCase ( self , lowercase_ ):
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
_snake_case : str = table_cast(lowercase_ , self.config.features.arrow_schema )
return pa_table
def UpperCamelCase ( self , lowercase_ ):
for i, file in enumerate(itertools.chain.from_iterable(lowercase_ ) ):
with open(lowercase_ , "rb" ) as f:
_snake_case : Dict = pa.Table.from_pandas(pd.read_pickle(lowercase_ ) )
yield i, self._cast_table(lowercase_ )
| 580
| 0
|
"""simple docstring"""
class UpperCamelCase_ (__A ):
pass
class UpperCamelCase_ (__A ):
pass
class UpperCamelCase_ :
def __init__( self : List[str] ) -> Tuple:
UpperCAmelCase_ : int = [
[],
[],
[],
]
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase_ : int , lowerCAmelCase_ : int ) -> None:
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError("Maximum queue size is 100" )
self.queues[priority].append(lowerCAmelCase_ )
except IndexError:
raise ValueError("Valid priorities are 0, 1, and 2" )
def _SCREAMING_SNAKE_CASE ( self : str ) -> int:
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError("All queues are empty" )
def __str__( self : int ) -> str:
return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues ) )
class UpperCamelCase_ :
def __init__( self : int ) -> Union[str, Any]:
UpperCAmelCase_ : Tuple = []
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase_ : int ) -> None:
if len(self.queue ) == 100:
raise OverFlowError("Maximum queue size is 100" )
self.queue.append(lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str ) -> int:
if not self.queue:
raise UnderFlowError("The queue is empty" )
else:
UpperCAmelCase_ : Union[str, Any] = min(self.queue )
self.queue.remove(lowerCAmelCase_ )
return data
def __str__( self : Dict ) -> str:
return str(self.queue )
def snake_case ( ):
UpperCAmelCase_ : Dict = FixedPriorityQueue()
fpq.enqueue(0 ,10 )
fpq.enqueue(1 ,70 )
fpq.enqueue(0 ,1_00 )
fpq.enqueue(2 ,1 )
fpq.enqueue(2 ,5 )
fpq.enqueue(1 ,7 )
fpq.enqueue(2 ,4 )
fpq.enqueue(1 ,64 )
fpq.enqueue(0 ,1_28 )
print(A__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(A__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def snake_case ( ):
UpperCAmelCase_ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(A__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(A__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 95
|
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _lowerCamelCase ( lowercase : Any ) -> List[str]:
return getitem, k
def _lowerCamelCase ( lowercase : Optional[Any] , lowercase : Union[str, Any] ) -> Any:
return setitem, k, v
def _lowerCamelCase ( lowercase : int ) -> Union[str, Any]:
return delitem, k
def _lowerCamelCase ( lowercase : Tuple , lowercase : Dict , *lowercase : Union[str, Any] ) -> int:
try:
return fun(lowercase , *lowercase ), None
except Exception as e:
return None, e
lowerCAmelCase_ : Optional[Any] = (
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
)
lowerCAmelCase_ : Optional[int] = [
_set('key_a', 'val_a'),
_set('key_a', 'val_b'),
]
lowerCAmelCase_ : int = [
_set('key_a', 'val_a'),
_set('key_b', 'val_b'),
_del('key_a'),
_del('key_b'),
_set('key_a', 'val_a'),
_del('key_a'),
]
lowerCAmelCase_ : List[Any] = [
_get('key_a'),
_del('key_a'),
_set('key_a', 'val_a'),
_del('key_a'),
_del('key_a'),
_get('key_a'),
]
lowerCAmelCase_ : str = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase_ : str = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('key_a', 'val_b'),
]
@pytest.mark.parametrize(
"operations" , (
pytest.param(_add_items , id="add items" ),
pytest.param(_overwrite_items , id="overwrite items" ),
pytest.param(_delete_items , id="delete items" ),
pytest.param(_access_absent_items , id="access absent items" ),
pytest.param(_add_with_resize_up , id="add with resize up" ),
pytest.param(_add_with_resize_down , id="add with resize down" ),
) , )
def _lowerCamelCase ( lowercase : Optional[int] ) -> Optional[int]:
_a = HashMap(initial_block_size=4 )
_a = {}
for _, (fun, *args) in enumerate(lowercase ):
_a , _a = _run_operation(lowercase , lowercase , *lowercase )
_a , _a = _run_operation(lowercase , lowercase , *lowercase )
assert my_res == py_res
assert str(lowercase ) == str(lowercase )
assert set(lowercase ) == set(lowercase )
assert len(lowercase ) == len(lowercase )
assert set(my.items() ) == set(py.items() )
def _lowerCamelCase ( ) -> str:
def is_public(lowercase : str ) -> bool:
return not name.startswith("_" )
_a = {name for name in dir({} ) if is_public(lowercase )}
_a = {name for name in dir(HashMap() ) if is_public(lowercase )}
assert dict_public_names > hash_public_names
| 692
| 0
|
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case_ ( __lowercase ):
UpperCAmelCase_ : Any = filter(lambda __lowercase : p.requires_grad , model.parameters() )
UpperCAmelCase_ : Optional[Any] = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__UpperCamelCase : Dict = logging.getLogger(__name__)
def snake_case_ ( __lowercase , __lowercase ):
if metric == "rouge2":
UpperCAmelCase_ : List[Any] = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
UpperCAmelCase_ : str = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
UpperCAmelCase_ : Optional[Any] = '''{val_avg_em:.4f}-{step_count}'''
elif metric == "loss":
UpperCAmelCase_ : Union[str, Any] = '''{val_avg_loss:.4f}-{step_count}'''
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
''' function.''' )
UpperCAmelCase_ : Optional[int] = ModelCheckpoint(
dirpath=__lowercase , filename=__lowercase , monitor=F'''val_{metric}''' , mode='''max''' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def snake_case_ ( __lowercase , __lowercase ):
return EarlyStopping(
monitor=F'''val_{metric}''' , mode='''min''' if '''loss''' in metric else '''max''' , patience=__lowercase , verbose=__lowercase , )
class lowerCAmelCase__( pl.Callback ):
'''simple docstring'''
def _lowerCamelCase ( self : Dict , __snake_case : Optional[int] , __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : int = {f'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(__snake_case )
@rank_zero_only
def _lowerCamelCase ( self : Tuple , __snake_case : pl.Trainer , __snake_case : pl.LightningModule , __snake_case : str , __snake_case : Optional[Any]=True ):
'''simple docstring'''
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
UpperCAmelCase_ : int = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
UpperCAmelCase_ : int = Path(pl_module.hparams.output_dir )
if type_path == "test":
UpperCAmelCase_ : Optional[Any] = od / '''test_results.txt'''
UpperCAmelCase_ : Any = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
UpperCAmelCase_ : Tuple = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
UpperCAmelCase_ : Tuple = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=__snake_case )
generations_file.parent.mkdir(exist_ok=__snake_case )
with open(__snake_case , '''a+''' ) as writer:
for key in sorted(__snake_case ):
if key in ["log", "progress_bar", "preds"]:
continue
UpperCAmelCase_ : str = metrics[key]
if isinstance(__snake_case , torch.Tensor ):
UpperCAmelCase_ : Dict = val.item()
UpperCAmelCase_ : Optional[int] = f'''{key}: {val:.6f}\n'''
writer.write(__snake_case )
if not save_generations:
return
if "preds" in metrics:
UpperCAmelCase_ : Union[str, Any] = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(__snake_case )
@rank_zero_only
def _lowerCamelCase ( self : Any , __snake_case : Tuple , __snake_case : List[str] ):
'''simple docstring'''
try:
UpperCAmelCase_ : List[str] = pl_module.model.model.num_parameters()
except AttributeError:
UpperCAmelCase_ : Union[str, Any] = pl_module.model.num_parameters()
UpperCAmelCase_ : Tuple = count_trainable_parameters(__snake_case )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1E6, '''grad_mp''': n_trainable_pars / 1E6} )
@rank_zero_only
def _lowerCamelCase ( self : List[str] , __snake_case : pl.Trainer , __snake_case : pl.LightningModule ):
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(__snake_case , __snake_case , '''test''' )
@rank_zero_only
def _lowerCamelCase ( self : Any , __snake_case : pl.Trainer , __snake_case : Optional[int] ):
'''simple docstring'''
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 641
|
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
__UpperCamelCase : str = 'true'
def snake_case_ ( __lowercase , __lowercase=8_2 , __lowercase=1_6 ):
set_seed(4_2 )
UpperCAmelCase_ : Optional[int] = RegressionModel()
UpperCAmelCase_ : Optional[int] = deepcopy(__lowercase )
UpperCAmelCase_ : Union[str, Any] = RegressionDataset(length=__lowercase )
UpperCAmelCase_ : Any = DataLoader(__lowercase , batch_size=__lowercase )
model.to(accelerator.device )
UpperCAmelCase_ , UpperCAmelCase_ : Dict = accelerator.prepare(__lowercase , __lowercase )
return model, ddp_model, dataloader
def snake_case_ ( __lowercase , __lowercase=False ):
UpperCAmelCase_ : Optional[int] = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
UpperCAmelCase_ : List[Any] = load_dataset('''glue''' , '''mrpc''' , split='''validation''' )
def tokenize_function(__lowercase ):
UpperCAmelCase_ : int = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__lowercase , max_length=__lowercase )
return outputs
with accelerator.main_process_first():
UpperCAmelCase_ : List[str] = dataset.map(
__lowercase , batched=__lowercase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
UpperCAmelCase_ : Any = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(__lowercase ):
if use_longest:
return tokenizer.pad(__lowercase , padding='''longest''' , return_tensors='''pt''' )
return tokenizer.pad(__lowercase , padding='''max_length''' , max_length=1_2_8 , return_tensors='''pt''' )
return DataLoader(__lowercase , shuffle=__lowercase , collate_fn=__lowercase , batch_size=1_6 )
def snake_case_ ( __lowercase , __lowercase ):
UpperCAmelCase_ : Optional[int] = Accelerator(dispatch_batches=__lowercase , split_batches=__lowercase )
UpperCAmelCase_ : int = get_dataloader(__lowercase , not dispatch_batches )
UpperCAmelCase_ : Optional[int] = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' , return_dict=__lowercase )
UpperCAmelCase_ , UpperCAmelCase_ : Any = accelerator.prepare(__lowercase , __lowercase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def snake_case_ ( __lowercase , __lowercase , __lowercase ):
UpperCAmelCase_ : Dict = []
for batch in dataloader:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = batch.values()
with torch.no_grad():
UpperCAmelCase_ : List[Any] = model(__lowercase )
UpperCAmelCase_ , UpperCAmelCase_ : Dict = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
UpperCAmelCase_ , UpperCAmelCase_ : Any = [], []
for logit, targ in logits_and_targets:
logits.append(__lowercase )
targs.append(__lowercase )
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = torch.cat(__lowercase ), torch.cat(__lowercase )
return logits, targs
def snake_case_ ( __lowercase , __lowercase=8_2 , __lowercase=False , __lowercase=False , __lowercase=1_6 ):
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Any = get_basic_setup(__lowercase , __lowercase , __lowercase )
UpperCAmelCase_ , UpperCAmelCase_ : Optional[int] = generate_predictions(__lowercase , __lowercase , __lowercase )
assert (
len(__lowercase ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__lowercase )}'''
def snake_case_ ( __lowercase = False , __lowercase = False ):
UpperCAmelCase_ : Optional[Any] = evaluate.load('''glue''' , '''mrpc''' )
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = get_mrpc_setup(__lowercase , __lowercase )
# First do baseline
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = setup['''no''']
model.to(__lowercase )
model.eval()
for batch in dataloader:
batch.to(__lowercase )
with torch.inference_mode():
UpperCAmelCase_ : str = model(**__lowercase )
UpperCAmelCase_ : Dict = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__lowercase , references=batch['''labels'''] )
UpperCAmelCase_ : Optional[int] = metric.compute()
# Then do distributed
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
UpperCAmelCase_ : Optional[int] = model(**__lowercase )
UpperCAmelCase_ : int = outputs.logits.argmax(dim=-1 )
UpperCAmelCase_ : Optional[int] = batch['''labels''']
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__lowercase , references=__lowercase )
UpperCAmelCase_ : Dict = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def snake_case_ ( ):
UpperCAmelCase_ : str = Accelerator(split_batches=__lowercase , dispatch_batches=__lowercase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(__lowercase , __lowercase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
UpperCAmelCase_ : Optional[Any] = Accelerator(split_batches=__lowercase , dispatch_batches=__lowercase )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(__lowercase , 9_9 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
UpperCAmelCase_ : List[Any] = Accelerator()
test_torch_metrics(__lowercase , 5_1_2 )
accelerator.state._reset_state()
def snake_case_ ( __lowercase ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 641
| 1
|
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
A_ = 0
A_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
A_ = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
A_ = tuple[int, int]
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> None:
'''simple docstring'''
lowerCamelCase_ = pos_x
lowerCamelCase_ = pos_y
lowerCamelCase_ = (pos_y, pos_x)
lowerCamelCase_ = goal_x
lowerCamelCase_ = goal_y
lowerCamelCase_ = g_cost
lowerCamelCase_ = parent
lowerCamelCase_ = self.calculate_heuristic()
lowerCamelCase_ = self.g_cost + self.h_cost
def UpperCamelCase( self ) -> float:
'''simple docstring'''
lowerCamelCase_ = self.pos_x - self.goal_x
lowerCamelCase_ = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(SCREAMING_SNAKE_CASE_ ) + abs(SCREAMING_SNAKE_CASE_ )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self , SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
return self.f_cost < other.f_cost
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = Node(goal[1] , goal[0] , goal[1] , goal[0] , 99999 , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = [self.start]
lowerCamelCase_ = []
lowerCamelCase_ = False
def UpperCamelCase( self ) -> list[TPosition]:
'''simple docstring'''
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
lowerCamelCase_ = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(SCREAMING_SNAKE_CASE_ )
self.closed_nodes.append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.get_successors(SCREAMING_SNAKE_CASE_ )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(SCREAMING_SNAKE_CASE_ )
else:
# retrieve the best current path
lowerCamelCase_ = self.open_nodes.pop(self.open_nodes.index(SCREAMING_SNAKE_CASE_ ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(SCREAMING_SNAKE_CASE_ )
else:
self.open_nodes.append(SCREAMING_SNAKE_CASE_ )
return [self.start.pos]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> list[Node]:
'''simple docstring'''
lowerCamelCase_ = []
for action in delta:
lowerCamelCase_ = parent.pos_x + action[1]
lowerCamelCase_ = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(SCREAMING_SNAKE_CASE_ ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , SCREAMING_SNAKE_CASE_ , ) )
return successors
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> list[TPosition]:
'''simple docstring'''
lowerCamelCase_ = node
lowerCamelCase_ = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
lowerCamelCase_ = current_node.parent
path.reverse()
return path
class UpperCAmelCase :
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None:
'''simple docstring'''
lowerCamelCase_ = AStar(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = AStar(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = False
def UpperCamelCase( self ) -> list[TPosition]:
'''simple docstring'''
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
lowerCamelCase_ = self.fwd_astar.open_nodes.pop(0 )
lowerCamelCase_ = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.fwd_astar.closed_nodes.append(SCREAMING_SNAKE_CASE_ )
self.bwd_astar.closed_nodes.append(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = current_bwd_node
lowerCamelCase_ = current_fwd_node
lowerCamelCase_ = {
self.fwd_astar: self.fwd_astar.get_successors(SCREAMING_SNAKE_CASE_ ),
self.bwd_astar: self.bwd_astar.get_successors(SCREAMING_SNAKE_CASE_ ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(SCREAMING_SNAKE_CASE_ )
else:
# retrieve the best current path
lowerCamelCase_ = astar.open_nodes.pop(
astar.open_nodes.index(SCREAMING_SNAKE_CASE_ ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(SCREAMING_SNAKE_CASE_ )
else:
astar.open_nodes.append(SCREAMING_SNAKE_CASE_ )
return [self.fwd_astar.start.pos]
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[TPosition]:
'''simple docstring'''
lowerCamelCase_ = self.fwd_astar.retrace_path(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = self.bwd_astar.retrace_path(SCREAMING_SNAKE_CASE_ )
bwd_path.pop()
bwd_path.reverse()
lowerCamelCase_ = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
A_ = (0, 0)
A_ = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
A_ = time.time()
A_ = AStar(init, goal)
A_ = a_star.search()
A_ = time.time() - start_time
print(f'''AStar execution time = {end_time:f} seconds''')
A_ = time.time()
A_ = BidirectionalAStar(init, goal)
A_ = time.time() - bd_start_time
print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')
| 42
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCAmelCase__ : Union[str, Any] = abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def lowercase_ ( _snake_case ):
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(_snake_case )
def lowercase_ ( _snake_case ):
from diffusers.utils.testing_utils import pytest_terminal_summary_main
SCREAMING_SNAKE_CASE__ : Any = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(_snake_case ,id=_snake_case )
| 223
| 0
|
'''simple docstring'''
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Union[str, Any] = set()
# Replace all the whitespace in our sentence
UpperCAmelCase : List[str] = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alpha.lower() <= "z":
frequency.add(alpha.lower() )
return len(_lowercase ) == 2_6
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
UpperCAmelCase : Tuple = [False] * 2_6
for char in input_str:
if char.islower():
UpperCAmelCase : Any = True
elif char.isupper():
UpperCAmelCase : Union[str, Any] = True
return all(_lowercase )
def __lowerCamelCase ( _lowercase = "The quick brown fox jumps over the lazy dog" , ) -> bool:
return len({char for char in input_str.lower() if char.isalpha()} ) == 2_6
def __lowerCamelCase ( ) -> None:
from timeit import timeit
UpperCAmelCase : str = """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
print(timeit("""is_pangram()""" , setup=_lowercase ) )
print(timeit("""is_pangram_faster()""" , setup=_lowercase ) )
print(timeit("""is_pangram_fastest()""" , setup=_lowercase ) )
# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
# 5.036091582966037, 2.644472333951853, 1.8869528750656173
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 672
|
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
a : Any = get_logger()
a : Optional[dict] = None
class UpperCamelCase_ ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
def __init__( self , A=None , A=None , **A ) -> str:
super().__init__(features=A )
import jax
from jaxlib.xla_client import Device
if isinstance(A , A ):
raise ValueError(
f'''Expected {device} to be a `str` not {type(A )}, as `jaxlib.xla_extension.Device` '''
"""is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
"""the device with `str()` to get its string identifier that will be internally mapped """
"""to the actual `jaxlib.xla_extension.Device`.""" )
UpperCAmelCase : Optional[int] = device if isinstance(A , A ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase : Any = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f'''Device with string identifier {self.device} not listed among the available '''
f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
f'''device: {str(jax.devices()[0] )}.''' )
UpperCAmelCase : List[Any] = str(jax.devices()[0] )
UpperCAmelCase : Union[str, Any] = jnp_array_kwargs
@staticmethod
def _lowercase( ) -> Dict[str, "jaxlib.xla_extension.Device"]:
import jax
return {str(A ): device for device in jax.devices()}
def _lowercase( self , A ) -> str:
import jax
import jax.numpy as jnp
if isinstance(A , A ) and column:
if all(
isinstance(A , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(A , axis=0 )
return column
def _lowercase( self , A ) -> Tuple:
import jax
import jax.numpy as jnp
if isinstance(A , (str, bytes, type(A )) ):
return value
elif isinstance(A , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
UpperCAmelCase : List[str] = {}
if isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
UpperCAmelCase : str = {"""dtype""": jnp.intaa}
else:
UpperCAmelCase : int = {"""dtype""": jnp.intaa}
elif isinstance(A , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
UpperCAmelCase : Any = {"""dtype""": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(A , PIL.Image.Image ):
UpperCAmelCase : List[str] = np.asarray(A )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
UpperCAmelCase : Dict = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(A , **{**default_dtype, **self.jnp_array_kwargs} )
def _lowercase( self , A ) -> Tuple:
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(A , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(A , """__array__""" ) and not isinstance(A , jax.Array ):
UpperCAmelCase : Optional[int] = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(A , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
elif isinstance(A , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(A ) for substruct in data_struct] )
return self._tensorize(A )
def _lowercase( self , A ) -> Dict:
return map_nested(self._recursive_tensorize , A , map_list=A )
def _lowercase( self , A ) -> Mapping:
UpperCAmelCase : Union[str, Any] = self.numpy_arrow_extractor().extract_row(A )
UpperCAmelCase : Dict = self.python_features_decoder.decode_row(A )
return self.recursive_tensorize(A )
def _lowercase( self , A ) -> "jax.Array":
UpperCAmelCase : int = self.numpy_arrow_extractor().extract_column(A )
UpperCAmelCase : Optional[Any] = self.python_features_decoder.decode_column(A , pa_table.column_names[0] )
UpperCAmelCase : Optional[int] = self.recursive_tensorize(A )
UpperCAmelCase : Any = self._consolidate(A )
return column
def _lowercase( self , A ) -> Mapping:
UpperCAmelCase : Optional[int] = self.numpy_arrow_extractor().extract_batch(A )
UpperCAmelCase : List[str] = self.python_features_decoder.decode_batch(A )
UpperCAmelCase : Union[str, Any] = self.recursive_tensorize(A )
for column_name in batch:
UpperCAmelCase : Optional[Any] = self._consolidate(batch[column_name] )
return batch
| 672
| 1
|
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Undefined for non-natural numbers" )
snake_case_ : List[str] = precision
snake_case_ : Union[str, Any] = ceil(precision / 1_4 )
snake_case_ : List[str] = 4_2_6_8_8_0 * Decimal(1_0_0_0_5 ).sqrt()
snake_case_ : str = 1
snake_case_ : List[str] = 1_3_5_9_1_4_0_9
snake_case_ : str = Decimal(lowerCAmelCase_ )
for k in range(1 , lowerCAmelCase_ ):
snake_case_ : Tuple = factorial(6 * k ) // (factorial(3 * k ) * factorial(lowerCAmelCase_ ) ** 3)
linear_term += 5_4_5_1_4_0_1_3_4
exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
return str(constant_term / partial_sum )[:-1]
if __name__ == "__main__":
UpperCAmelCase = 5_0
print(F"The first {n} digits of pi is: {pi(n)}")
| 666
|
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class snake_case__ ( _UpperCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = "git_vision_model"
def __init__( self : int , A__ : Union[str, Any]=7_68 , A__ : List[Any]=30_72 , A__ : Tuple=12 , A__ : Optional[Any]=12 , A__ : Optional[int]=3 , A__ : List[str]=2_24 , A__ : Dict=16 , A__ : int="quick_gelu" , A__ : Any=1E-5 , A__ : Tuple=0.0 , A__ : Optional[int]=0.02 , **A__ : List[str] , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**A__ )
snake_case_ : Optional[Any] = hidden_size
snake_case_ : str = intermediate_size
snake_case_ : Optional[Any] = num_hidden_layers
snake_case_ : int = num_attention_heads
snake_case_ : Optional[int] = num_channels
snake_case_ : Union[str, Any] = patch_size
snake_case_ : List[str] = image_size
snake_case_ : List[Any] = initializer_range
snake_case_ : Any = attention_dropout
snake_case_ : Any = layer_norm_eps
snake_case_ : int = hidden_act
@classmethod
def UpperCAmelCase__ ( cls : List[Any] , A__ : Union[str, os.PathLike] , **A__ : Optional[int] ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(A__ )
snake_case_ ,snake_case_ : Tuple = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
snake_case_ : 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 ):
_SCREAMING_SNAKE_CASE : Optional[Any] = "git"
def __init__( self : Any , A__ : List[str]=None , A__ : List[str]=3_05_22 , A__ : Tuple=7_68 , A__ : Tuple=6 , A__ : str=12 , A__ : Any=30_72 , A__ : List[str]="gelu" , A__ : int=0.1 , A__ : Dict=0.1 , A__ : Any=10_24 , A__ : Optional[Any]=0.02 , A__ : Optional[Any]=1E-12 , A__ : Dict=0 , A__ : Any="absolute" , A__ : Tuple=True , A__ : Any=False , A__ : Tuple=1_01 , A__ : Tuple=1_02 , A__ : List[Any]=None , **A__ : List[str] , ) -> int:
'''simple docstring'''
super().__init__(bos_token_id=A__ , eos_token_id=A__ , pad_token_id=A__ , **A__ )
if vision_config is None:
snake_case_ : int = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
snake_case_ : str = GitVisionConfig(**A__ )
snake_case_ : int = vocab_size
snake_case_ : List[Any] = hidden_size
snake_case_ : Tuple = num_hidden_layers
snake_case_ : List[Any] = num_attention_heads
snake_case_ : Any = hidden_act
snake_case_ : Dict = intermediate_size
snake_case_ : Any = hidden_dropout_prob
snake_case_ : Any = attention_probs_dropout_prob
snake_case_ : Union[str, Any] = max_position_embeddings
snake_case_ : List[str] = initializer_range
snake_case_ : List[str] = layer_norm_eps
snake_case_ : Any = position_embedding_type
snake_case_ : Union[str, Any] = use_cache
snake_case_ : str = tie_word_embeddings
snake_case_ : List[Any] = num_image_with_embedding
snake_case_ : Dict = bos_token_id
snake_case_ : int = eos_token_id
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
snake_case_ : Tuple = copy.deepcopy(self.__dict__ )
snake_case_ : Optional[int] = self.vision_config.to_dict()
snake_case_ : Tuple = self.__class__.model_type
return output
| 666
| 1
|
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_lowerCAmelCase = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH AH QH""",
"""TS KS 5S 9S AC""",
"""KD 6S 9D TH AD""",
"""KS 8D 4D 9S 4S""", # pair
"""8C 4S KH JS 4D""", # pair
"""QH 8H KD JH 8S""", # pair
"""KC 4H KS 2H 8D""", # pair
"""KD 4S KC 3H 8S""", # pair
"""AH 8S AS KC JH""", # pair
"""3H 4C 4H 3S 2H""", # 2 pairs
"""5S 5D 2C KH KH""", # 2 pairs
"""3C KH 5D 5S KH""", # 2 pairs
"""AS 3C KH AD KH""", # 2 pairs
"""7C 7S 3S 7H 5S""", # 3 of a kind
"""7C 7S KH 2H 7H""", # 3 of a kind
"""AC KH QH AH AS""", # 3 of a kind
"""2H 4D 3C AS 5S""", # straight (low ace)
"""3C 5C 4C 2C 6H""", # straight
"""6S 8S 7S 5H 9H""", # straight
"""JS QS 9H TS KH""", # straight
"""QC KH TS JS AH""", # straight (high ace)
"""8C 9C 5C 3C TC""", # flush
"""3S 8S 9S 5S KS""", # flush
"""4C 5C 9C 8C KC""", # flush
"""JH 8H AH KH QH""", # flush
"""3D 2H 3H 2C 2D""", # full house
"""2H 2C 3S 3H 3D""", # full house
"""KH KC 3S 3H 3D""", # full house
"""JC 6H JS JD JH""", # 4 of a kind
"""JC 7H JS JD JH""", # 4 of a kind
"""JC KH JS JD JH""", # 4 of a kind
"""2S AS 4S 5S 3S""", # straight flush (low ace)
"""2D 6D 3D 4D 5D""", # straight flush
"""5C 6C 3C 7C 4C""", # straight flush
"""JH 9H TH KH QH""", # straight flush
"""JH AH TH KH QH""", # royal flush (high ace straight flush)
)
_lowerCAmelCase = (
("""2H 3H 4H 5H 6H""", """KS AS TS QS JS""", """Loss"""),
("""2H 3H 4H 5H 6H""", """AS AD AC AH JD""", """Win"""),
("""AS AH 2H AD AC""", """JS JD JC JH 3D""", """Win"""),
("""2S AH 2H AS AC""", """JS JD JC JH AD""", """Loss"""),
("""2S AH 2H AS AC""", """2H 3H 5H 6H 7H""", """Win"""),
("""AS 3S 4S 8S 2S""", """2H 3H 5H 6H 7H""", """Win"""),
("""2H 3H 5H 6H 7H""", """2S 3H 4H 5S 6C""", """Win"""),
("""2S 3H 4H 5S 6C""", """3D 4C 5H 6H 2S""", """Tie"""),
("""2S 3H 4H 5S 6C""", """AH AC 5H 6H AS""", """Win"""),
("""2S 2H 4H 5S 4C""", """AH AC 5H 6H AS""", """Loss"""),
("""2S 2H 4H 5S 4C""", """AH AC 5H 6H 7S""", """Win"""),
("""6S AD 7H 4S AS""", """AH AC 5H 6H 7S""", """Loss"""),
("""2S AH 4H 5S KC""", """AH AC 5H 6H 7S""", """Loss"""),
("""2S 3H 6H 7S 9C""", """7H 3C TH 6H 9S""", """Loss"""),
("""4S 5H 6H TS AC""", """3S 5H 6H TS AC""", """Win"""),
("""2S AH 4H 5S 6C""", """AD 4C 5H 6H 2C""", """Tie"""),
("""AS AH 3H AD AC""", """AS AH 2H AD AC""", """Win"""),
("""AH AC 5H 5C QS""", """AH AC 5H 5C KS""", """Loss"""),
("""AH AC 5H 5C QS""", """KH KC 5H 5C QS""", """Win"""),
("""7C 7S KH 2H 7H""", """3C 3S AH 2H 3H""", """Win"""),
("""3C 3S AH 2H 3H""", """7C 7S KH 2H 7H""", """Loss"""),
("""6H 5H 4H 3H 2H""", """5H 4H 3H 2H AH""", """Win"""),
("""5H 4H 3H 2H AH""", """5H 4H 3H 2H AH""", """Tie"""),
("""5H 4H 3H 2H AH""", """6H 5H 4H 3H 2H""", """Loss"""),
("""AH AD KS KC AC""", """AH KD KH AC KC""", """Win"""),
("""2H 4D 3C AS 5S""", """2H 4D 3C 6S 5S""", """Loss"""),
("""2H 3S 3C 3H 2S""", """3S 3C 2S 2H 2D""", """Win"""),
("""4D 6D 5D 2D JH""", """3S 8S 3H TC KH""", """Loss"""),
("""4S 6C 8S 3S 7S""", """AD KS 2D 7D 7C""", """Loss"""),
("""6S 4C 7H 8C 3H""", """5H JC AH 9D 9C""", """Loss"""),
("""9D 9H JH TC QH""", """3C 2S JS 5C 7H""", """Win"""),
("""2H TC 8S AD 9S""", """4H TS 7H 2C 5C""", """Win"""),
("""9D 3S 2C 7S 7C""", """JC TD 3C TC 9H""", """Loss"""),
)
_lowerCAmelCase = (
("""2H 3H 4H 5H 6H""", True),
("""AS AH 2H AD AC""", False),
("""2H 3H 5H 6H 7H""", True),
("""KS AS TS QS JS""", True),
("""8H 9H QS JS TH""", False),
("""AS 3S 4S 8S 2S""", True),
)
_lowerCAmelCase = (
("""2H 3H 4H 5H 6H""", True),
("""AS AH 2H AD AC""", False),
("""2H 3H 5H 6H 7H""", False),
("""KS AS TS QS JS""", True),
("""8H 9H QS JS TH""", True),
)
_lowerCAmelCase = (
("""2H 4D 3C AS 5S""", True, [5, 4, 3, 2, 14]),
("""2H 5D 3C AS 5S""", False, [14, 5, 5, 3, 2]),
("""JH QD KC AS TS""", False, [14, 13, 12, 11, 10]),
("""9D 3S 2C 7S 7C""", False, [9, 7, 7, 3, 2]),
)
_lowerCAmelCase = (
("""JH AH TH KH QH""", 0),
("""JH 9H TH KH QH""", 0),
("""JC KH JS JD JH""", 7),
("""KH KC 3S 3H 3D""", 6),
("""8C 9C 5C 3C TC""", 0),
("""JS QS 9H TS KH""", 0),
("""7C 7S KH 2H 7H""", 3),
("""3C KH 5D 5S KH""", 2),
("""QH 8H KD JH 8S""", 1),
("""2D 6D 9D TH 7D""", 0),
)
_lowerCAmelCase = (
("""JH AH TH KH QH""", 23),
("""JH 9H TH KH QH""", 22),
("""JC KH JS JD JH""", 21),
("""KH KC 3S 3H 3D""", 20),
("""8C 9C 5C 3C TC""", 19),
("""JS QS 9H TS KH""", 18),
("""7C 7S KH 2H 7H""", 17),
("""3C KH 5D 5S KH""", 16),
("""QH 8H KD JH 8S""", 15),
("""2D 6D 9D TH 7D""", 14),
)
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ , A_ : Optional[Any] = randrange(len(_lowerCAmelCase ) ), randrange(len(_lowerCAmelCase ) )
A_ : int = ["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
A_ , A_ : Optional[int] = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def _lowerCAmelCase ( _lowerCAmelCase = 1_0_0 ):
'''simple docstring'''
return (generate_random_hand() for _ in range(_lowerCAmelCase ))
@pytest.mark.parametrize("""hand, expected""" ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
assert PokerHand(_lowerCAmelCase )._is_flush() == expected
@pytest.mark.parametrize("""hand, expected""" ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
assert PokerHand(_lowerCAmelCase )._is_straight() == expected
@pytest.mark.parametrize("""hand, expected, card_values""" ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : List[str] = PokerHand(_lowerCAmelCase )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize("""hand, expected""" ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
assert PokerHand(_lowerCAmelCase )._is_same_kind() == expected
@pytest.mark.parametrize("""hand, expected""" ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
assert PokerHand(_lowerCAmelCase )._hand_type == expected
@pytest.mark.parametrize("""hand, other, expected""" ,_lowerCAmelCase )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
assert PokerHand(_lowerCAmelCase ).compare_with(PokerHand(_lowerCAmelCase ) ) == expected
@pytest.mark.parametrize("""hand, other, expected""" ,generate_random_hands() )
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
assert PokerHand(_lowerCAmelCase ).compare_with(PokerHand(_lowerCAmelCase ) ) == expected
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Optional[Any] = [PokerHand(_lowerCAmelCase ) for hand in SORTED_HANDS]
A_ : Tuple = poker_hands.copy()
shuffle(_lowerCAmelCase )
A_ : Dict = chain(sorted(_lowerCAmelCase ) )
for index, hand in enumerate(_lowerCAmelCase ):
assert hand == poker_hands[index]
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Any = [PokerHand("""2D AC 3H 4H 5S""" ), PokerHand("""2S 3H 4H 5S 6C""" )]
pokerhands.sort(reverse=_lowerCAmelCase )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : List[Any] = PokerHand("""2C 4S AS 3D 5C""" )
A_ : Tuple = True
A_ : Any = [5, 4, 3, 2, 1_4]
for _ in range(1_0 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : Tuple = 0
A_ : Union[str, Any] = os.path.abspath(os.path.dirname(_lowerCAmelCase ) )
A_ : Tuple = os.path.join(_lowerCAmelCase ,"""poker_hands.txt""" )
with open(_lowerCAmelCase ) as file_hand:
for line in file_hand:
A_ : Tuple = line[:1_4].strip()
A_ : Dict = line[1_5:].strip()
A_ , A_ : List[str] = PokerHand(_lowerCAmelCase ), PokerHand(_lowerCAmelCase )
A_ : str = player.compare_with(_lowerCAmelCase )
if output == "Win":
answer += 1
assert answer == 3_7_6
| 481
|
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTMAEForPreTraining, ViTMAEModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _UpperCAmelCase :
def __init__( self , a__ , a__=13 , a__=30 , a__=2 , a__=3 , a__=True , a__=True , a__=32 , a__=5 , a__=4 , a__=37 , a__="gelu" , a__=0.1 , a__=0.1 , a__=10 , a__=0.02 , a__=3 , a__=0.6 , a__=None , ):
A_ : int = parent
A_ : Optional[int] = batch_size
A_ : Any = image_size
A_ : Optional[int] = patch_size
A_ : int = num_channels
A_ : str = is_training
A_ : str = use_labels
A_ : str = hidden_size
A_ : Union[str, Any] = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : Any = intermediate_size
A_ : List[Any] = hidden_act
A_ : List[Any] = hidden_dropout_prob
A_ : Optional[int] = attention_probs_dropout_prob
A_ : str = type_sequence_label_size
A_ : int = initializer_range
A_ : List[Any] = mask_ratio
A_ : str = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
A_ : Optional[Any] = (image_size // patch_size) ** 2
A_ : List[Any] = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def _lowerCamelCase ( self ):
A_ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : int = None
if self.use_labels:
A_ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Any = self.get_config()
return config, pixel_values, labels
def _lowerCamelCase ( self ):
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=a__ , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def _lowerCamelCase ( self , a__ , a__ , a__ ):
A_ : Optional[int] = ViTMAEModel(config=a__ )
model.to(a__ )
model.eval()
A_ : int = model(a__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowerCamelCase ( self , a__ , a__ , a__ ):
A_ : int = ViTMAEForPreTraining(a__ )
model.to(a__ )
model.eval()
A_ : Optional[Any] = model(a__ )
A_ : Dict = (self.image_size // self.patch_size) ** 2
A_ : Optional[int] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
A_ : Optional[int] = 1
A_ : Any = ViTMAEForPreTraining(a__ )
model.to(a__ )
model.eval()
A_ : Dict = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : List[Any] = model(a__ )
A_ : Optional[int] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def _lowerCamelCase ( self ):
A_ : Optional[Any] = self.prepare_config_and_inputs()
A_ , A_ , A_ : Any = config_and_inputs
A_ : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
a = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
a = {'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}
a = False
a = False
a = False
a = False
def _lowerCamelCase ( self ):
A_ : int = ViTMAEModelTester(self )
A_ : List[Any] = ConfigTester(self , config_class=a__ , has_text_modality=a__ , hidden_size=37 )
def _lowerCamelCase ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViTMAE does not use inputs_embeds""" )
def _lowerCamelCase ( self ):
pass
def _lowerCamelCase ( self ):
A_ , A_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Dict = model_class(a__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : int = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a__ , nn.Linear ) )
def _lowerCamelCase ( self ):
A_ , A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : Any = model_class(a__ )
A_ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Dict = [*signature.parameters.keys()]
A_ : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , a__ )
def _lowerCamelCase ( self ):
A_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a__ )
def _lowerCamelCase ( self ):
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*a__ )
def _lowerCamelCase ( self , a__ , a__ , a__ ):
# make masks reproducible
np.random.seed(2 )
A_ : Optional[Any] = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2 )
A_ : List[str] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
A_ : Optional[Any] = torch.from_numpy(a__ )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
A_ : Any = pt_noise
super().check_pt_tf_models(a__ , a__ , a__ )
def _lowerCamelCase ( self ):
A_ , A_ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(a__ )
model.to(a__ )
model.eval()
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
A_ : Union[str, Any] = model(**self._prepare_for_class(a__ , a__ ) )
A_ : int = outputs[0].cpu().numpy()
A_ : Union[str, Any] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(a__ )
A_ : Union[str, Any] = model_class.from_pretrained(a__ )
model.to(a__ )
# make random mask reproducible
torch.manual_seed(2 )
with torch.no_grad():
A_ : Optional[int] = model(**self._prepare_for_class(a__ , a__ ) )
# Make sure we don't have nans
A_ : Optional[int] = after_outputs[0].cpu().numpy()
A_ : str = 0
A_ : Optional[int] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(a__ , 1E-5 )
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _lowerCamelCase ( self ):
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _lowerCamelCase ( self ):
pass
@unittest.skip(
reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
to get deterministic results.""" )
def _lowerCamelCase ( self ):
pass
@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""" )
def _lowerCamelCase ( self ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _lowerCamelCase ( self ):
pass
@slow
def _lowerCamelCase ( self ):
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : Union[str, Any] = ViTMAEModel.from_pretrained(a__ )
self.assertIsNotNone(a__ )
def _lowerCAmelCase ( ):
'''simple docstring'''
A_ : List[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
@cached_property
def _lowerCamelCase ( self ):
return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base""" ) if is_vision_available() else None
@slow
def _lowerCamelCase ( self ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
A_ : Union[str, Any] = ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""" ).to(a__ )
A_ : Optional[Any] = self.default_image_processor
A_ : Union[str, Any] = prepare_img()
A_ : Union[str, Any] = image_processor(images=a__ , return_tensors="""pt""" ).to(a__ )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
A_ : Optional[int] = ViTMAEConfig()
A_ : Tuple = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
A_ : Tuple = np.random.uniform(size=(1, num_patches) )
# forward pass
with torch.no_grad():
A_ : Dict = model(**a__ , noise=torch.from_numpy(a__ ).to(device=a__ ) )
# verify the logits
A_ : Tuple = torch.Size((1, 196, 768) )
self.assertEqual(outputs.logits.shape , a__ )
A_ : List[str] = torch.tensor(
[[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , expected_slice.to(a__ ) , atol=1E-4 ) )
| 481
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Optional[Any] = {
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "bloom"
lowercase__ = ["past_key_values"]
lowercase__ = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
}
def __init__( self : int ,lowercase_ : Dict=2_5_0_8_8_0 ,lowercase_ : Dict=6_4 ,lowercase_ : Dict=2 ,lowercase_ : Union[str, Any]=8 ,lowercase_ : Tuple=1E-5 ,lowercase_ : str=0.02 ,lowercase_ : List[str]=True ,lowercase_ : Optional[Any]=1 ,lowercase_ : int=2 ,lowercase_ : List[str]=False ,lowercase_ : List[str]=0.0 ,lowercase_ : int=0.0 ,lowercase_ : Dict=1 ,lowercase_ : Optional[int]=False ,**lowercase_ : List[Any] ,):
lowerCAmelCase__ : Union[str, Any] = vocab_size
# Backward compatibility with n_embed kwarg
lowerCAmelCase__ : Optional[int] = kwargs.pop('''n_embed''' ,lowercase_ )
lowerCAmelCase__ : Union[str, Any] = hidden_size if n_embed is None else n_embed
lowerCAmelCase__ : List[str] = n_layer
lowerCAmelCase__ : Any = n_head
lowerCAmelCase__ : Union[str, Any] = layer_norm_epsilon
lowerCAmelCase__ : Any = initializer_range
lowerCAmelCase__ : Union[str, Any] = use_cache
lowerCAmelCase__ : Union[str, Any] = pretraining_tp
lowerCAmelCase__ : Optional[Any] = apply_residual_connection_post_layernorm
lowerCAmelCase__ : Tuple = hidden_dropout
lowerCAmelCase__ : Optional[int] = attention_dropout
lowerCAmelCase__ : Tuple = bos_token_id
lowerCAmelCase__ : Dict = eos_token_id
lowerCAmelCase__ : Dict = slow_but_exact
super().__init__(bos_token_id=lowercase_ ,eos_token_id=lowercase_ ,**lowercase_ )
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = version.parse("1.12" )
def __init__( self : Optional[Any] ,lowercase_ : PretrainedConfig ,lowercase_ : str = "default" ,lowercase_ : List[PatchingSpec] = None ,lowercase_ : bool = False ,):
super().__init__(lowercase_ ,task=lowercase_ ,patching_specs=lowercase_ ,use_past=lowercase_ )
if not getattr(self._config ,'''pad_token_id''' ,lowercase_ ):
# TODO: how to do that better?
lowerCAmelCase__ : Union[str, Any] = 0
@property
def __lowerCAmelCase ( self : int ):
lowerCAmelCase__ : Union[str, Any] = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(lowercase_ ,direction='''inputs''' ,inverted_values_shape=lowercase_ )
lowerCAmelCase__ : Dict = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowerCAmelCase__ : int = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def __lowerCAmelCase ( self : int ):
return self._config.n_layer
@property
def __lowerCAmelCase ( self : Optional[int] ):
return self._config.n_head
@property
def __lowerCAmelCase ( self : Union[str, Any] ):
return 1E-3
def __lowerCAmelCase ( self : Any ,lowercase_ : "PreTrainedTokenizer" ,lowercase_ : int = -1 ,lowercase_ : int = -1 ,lowercase_ : bool = False ,lowercase_ : Optional["TensorType"] = None ,):
lowerCAmelCase__ : Optional[int] = super(lowercase_ ,self ).generate_dummy_inputs(
lowercase_ ,batch_size=lowercase_ ,seq_length=lowercase_ ,is_pair=lowercase_ ,framework=lowercase_ )
# We need to order the input in the way they appears in the forward()
lowerCAmelCase__ : Dict = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowerCAmelCase__ ,lowerCAmelCase__ : Union[str, Any] = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowerCAmelCase__ : List[str] = seqlen + 2
lowerCAmelCase__ : List[Any] = self._config.hidden_size // self.num_attention_heads
lowerCAmelCase__ : int = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
lowerCAmelCase__ : Tuple = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
lowerCAmelCase__ : List[str] = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(self.num_layers )
]
lowerCAmelCase__ : Optional[int] = common_inputs['''attention_mask''']
if self.use_past:
lowerCAmelCase__ : List[str] = ordered_inputs['''attention_mask'''].dtype
lowerCAmelCase__ : Dict = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(lowercase_ ,lowercase_ ,dtype=lowercase_ )] ,dim=1 )
return ordered_inputs
@property
def __lowerCAmelCase ( self : List[str] ):
return 1_3
| 450
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Any ,lowercase_ : Dict ,lowercase_ : List[str]=7 ,lowercase_ : Tuple=3 ,lowercase_ : List[str]=1_8 ,lowercase_ : Optional[Any]=3_0 ,lowercase_ : List[Any]=4_0_0 ,lowercase_ : List[Any]=True ,lowercase_ : Any=None ,lowercase_ : Optional[Any]=True ,lowercase_ : str=None ,lowercase_ : List[Any]=True ,lowercase_ : Dict=[0.5, 0.5, 0.5] ,lowercase_ : Dict=[0.5, 0.5, 0.5] ,):
lowerCAmelCase__ : Any = size if size is not None else {'''shortest_edge''': 1_8}
lowerCAmelCase__ : Union[str, Any] = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
lowerCAmelCase__ : Dict = parent
lowerCAmelCase__ : Dict = batch_size
lowerCAmelCase__ : List[str] = num_channels
lowerCAmelCase__ : Any = image_size
lowerCAmelCase__ : Union[str, Any] = min_resolution
lowerCAmelCase__ : Dict = max_resolution
lowerCAmelCase__ : List[str] = do_resize
lowerCAmelCase__ : Optional[Any] = size
lowerCAmelCase__ : Tuple = do_center_crop
lowerCAmelCase__ : Optional[int] = crop_size
lowerCAmelCase__ : List[str] = do_normalize
lowerCAmelCase__ : Tuple = image_mean
lowerCAmelCase__ : int = image_std
def __lowerCAmelCase ( self : List[str] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE ( a_ , unittest.TestCase ):
"""simple docstring"""
lowercase__ = LevitImageProcessor if is_vision_available() else None
def __lowerCAmelCase ( self : List[str] ):
lowerCAmelCase__ : Optional[Any] = LevitImageProcessingTester(self )
@property
def __lowerCAmelCase ( self : Any ):
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCAmelCase ( self : str ):
lowerCAmelCase__ : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase_ ,'''image_mean''' ) )
self.assertTrue(hasattr(lowercase_ ,'''image_std''' ) )
self.assertTrue(hasattr(lowercase_ ,'''do_normalize''' ) )
self.assertTrue(hasattr(lowercase_ ,'''do_resize''' ) )
self.assertTrue(hasattr(lowercase_ ,'''do_center_crop''' ) )
self.assertTrue(hasattr(lowercase_ ,'''size''' ) )
def __lowerCAmelCase ( self : Any ):
lowerCAmelCase__ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{'''shortest_edge''': 1_8} )
self.assertEqual(image_processor.crop_size ,{'''height''': 1_8, '''width''': 1_8} )
lowerCAmelCase__ : List[str] = self.image_processing_class.from_dict(self.image_processor_dict ,size=4_2 ,crop_size=8_4 )
self.assertEqual(image_processor.size ,{'''shortest_edge''': 4_2} )
self.assertEqual(image_processor.crop_size ,{'''height''': 8_4, '''width''': 8_4} )
def __lowerCAmelCase ( self : Union[str, Any] ):
pass
def __lowerCAmelCase ( self : Optional[int] ):
# Initialize image_processing
lowerCAmelCase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase__ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ ,Image.Image )
# Test not batched input
lowerCAmelCase__ : Any = image_processing(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Optional[int] = image_processing(lowercase_ ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
def __lowerCAmelCase ( self : Union[str, Any] ):
# Initialize image_processing
lowerCAmelCase__ : str = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase__ : Optional[Any] = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowercase_ ,numpify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ ,np.ndarray )
# Test not batched input
lowerCAmelCase__ : Tuple = image_processing(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Union[str, Any] = image_processing(lowercase_ ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
def __lowerCAmelCase ( self : int ):
# Initialize image_processing
lowerCAmelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase__ : Dict = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowercase_ ,torchify=lowercase_ )
for image in image_inputs:
self.assertIsInstance(lowercase_ ,torch.Tensor )
# Test not batched input
lowerCAmelCase__ : Dict = image_processing(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
# Test batched
lowerCAmelCase__ : Optional[int] = image_processing(lowercase_ ,return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) ,)
| 450
| 1
|
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__lowercase : Optional[int] = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def lowerCamelCase_ ( ):
lowerCamelCase_ = Github(os.environ['''GITHUB_TOKEN'''] )
lowerCamelCase_ = g.get_repo('''huggingface/diffusers''' )
lowerCamelCase_ = repo.get_issues(state='''open''' )
for issue in open_issues:
lowerCamelCase_ = sorted(issue.get_comments() , key=lambda _lowerCamelCase : i.created_at , reverse=_lowerCamelCase )
lowerCamelCase_ = comments[0] if len(_lowerCamelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 3_0
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Closes the issue after 7 days of inactivity since the Stalebot notification.
issue.edit(state='''closed''' )
elif (
"stale" in issue.get_labels()
and last_comment is not None
and last_comment.user.login != "github-actions[bot]"
):
# Opens the issue if someone other than Stalebot commented.
issue.edit(state='''open''' )
issue.remove_from_labels('''stale''' )
elif (
(dt.utcnow() - issue.updated_at).days > 2_3
and (dt.utcnow() - issue.created_at).days >= 3_0
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# Post a Stalebot notification after 23 days of inactivity.
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
issue.add_to_labels('''stale''' )
if __name__ == "__main__":
main()
| 66
|
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCamelCase_ ( _lowerCamelCase : int = 8 ):
lowerCamelCase_ = ascii_letters + digits + punctuation
return "".join(secrets.choice(_lowerCamelCase ) for _ in range(_lowerCamelCase ) )
def lowerCamelCase_ ( _lowerCamelCase : str , _lowerCamelCase : int ):
# Password Generator = full boot with random_number, random_letters, and
# random_character FUNCTIONS
# Put your code here...
i -= len(_lowerCamelCase )
lowerCamelCase_ = i // 3
lowerCamelCase_ = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
lowerCamelCase_ = (
chars_incl
+ random(_lowerCamelCase , quotient + remainder )
+ random(_lowerCamelCase , _lowerCamelCase )
+ random(_lowerCamelCase , _lowerCamelCase )
)
lowerCamelCase_ = list(_lowerCamelCase )
shuffle(_lowerCamelCase )
return "".join(_lowerCamelCase )
# random is a generalised function for letters, characters and numbers
def lowerCamelCase_ ( _lowerCamelCase : str , _lowerCamelCase : int ):
return "".join(secrets.choice(_lowerCamelCase ) for _ in range(_lowerCamelCase ) )
def lowerCamelCase_ ( _lowerCamelCase : Dict , _lowerCamelCase : str ):
pass # Put your code here...
def lowerCamelCase_ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Optional[Any] ):
pass # Put your code here...
def lowerCamelCase_ ( _lowerCamelCase : int , _lowerCamelCase : str ):
pass # Put your code here...
def lowerCamelCase_ ( _lowerCamelCase : str , _lowerCamelCase : int = 8 ):
if len(_lowerCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
lowerCamelCase_ = any(char in ascii_uppercase for char in password )
lowerCamelCase_ = any(char in ascii_lowercase for char in password )
lowerCamelCase_ = any(char in digits for char in password )
lowerCamelCase_ = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def lowerCamelCase_ ( ):
lowerCamelCase_ = int(input('''Please indicate the max length of your password: ''' ).strip() )
lowerCamelCase_ = input(
'''Please indicate the characters that must be in your password: ''' ).strip()
print('''Password generated:''' , password_generator(_lowerCamelCase ) )
print(
'''Alternative Password generated:''' , alternative_password_generator(_lowerCamelCase , _lowerCamelCase ) , )
print('''[If you are thinking of using this passsword, You better save it.]''' )
if __name__ == "__main__":
main()
| 66
| 1
|
'''simple docstring'''
def _A ( snake_case , snake_case , snake_case , snake_case ) -> int:
_lowercase , _lowercase : Optional[Any] = len(snake_case ), len(grid[0] )
if (
min(snake_case , snake_case ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_lowercase : Union[str, Any] = 0
count += depth_first_search(snake_case , row + 1 , snake_case , snake_case )
count += depth_first_search(snake_case , row - 1 , snake_case , snake_case )
count += depth_first_search(snake_case , snake_case , col + 1 , snake_case )
count += depth_first_search(snake_case , snake_case , col - 1 , snake_case )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 245
|
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( snake_case ) -> str:
_lowercase : Dict = torch.load(snake_case , map_location="cpu" )
if "model" in sd.keys():
_lowercase : Tuple = torch.load(snake_case , map_location="cpu" )["model"]
# pop unnecessary weights
_lowercase : Any = [
"decoder.version",
"decoder.output_projection.weight",
]
for key in keys_to_delete:
if key in sd:
sd.pop(snake_case )
_lowercase : List[Any] = {
"decoder.project_in_dim.weight": "decoder.project_in.weight",
"decoder.project_out_dim.weight": "decoder.project_out.weight",
"decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
"decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
_lowercase : Dict = sd.pop(snake_case )
_lowercase : List[str] = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
_lowercase : List[Any] = sd[key]
# We split QKV in separate Q,K,V
_lowercase : str = key.replace(".qkv_proj." , ".q_proj." )
_lowercase : List[str] = key.replace(".qkv_proj." , ".k_proj." )
_lowercase : Optional[Any] = key.replace(".qkv_proj." , ".v_proj." )
_lowercase : Union[str, Any] = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
_lowercase , _lowercase , _lowercase : Dict = torch.split(snake_case , depth // 3 , dim=0 )
_lowercase : Optional[int] = q
_lowercase : str = k
_lowercase : List[str] = v
del sd[key]
return sd
@torch.no_grad()
def _A ( snake_case , snake_case , snake_case=None ) -> Any:
_lowercase : Union[str, Any] = load_checkpoint(snake_case )
if config is not None:
_lowercase : Tuple = OPTConfig.from_pretrained(snake_case )
else:
_lowercase : Optional[int] = OPTConfig()
_lowercase : List[Any] = OPTModel(snake_case ).half().eval()
model.load_state_dict(snake_case )
# Check results
Path(snake_case ).mkdir(exist_ok=snake_case )
model.save_pretrained(snake_case )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--fairseq_path',
type=str,
help=(
'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
' https://huggingface.co/models?other=opt_metasq'
),
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
_snake_case = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 245
| 1
|
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
SCREAMING_SNAKE_CASE : List[Any] = '''bart'''
SCREAMING_SNAKE_CASE : Union[str, Any] = True
@st.cache(allow_output_mutation=lowerCAmelCase__ )
def __lowerCamelCase ( ):
if LOAD_DENSE_INDEX:
A__ = AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased' )
A__ = AutoModel.from_pretrained('yjernite/retribert-base-uncased' ).to('cuda:0' )
A__ = qar_model.eval()
else:
A__ , A__ = (None, None)
if MODEL_TYPE == "bart":
A__ = AutoTokenizer.from_pretrained('yjernite/bart_eli5' )
A__ = AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5' ).to('cuda:0' )
A__ = torch.load('seq2seq_models/eli5_bart_model_blm_2.pth' )
sas_model.load_state_dict(save_dict['model'] )
A__ = sas_model.eval()
else:
A__ , A__ = make_qa_sas_model(
model_name='t5-small' ,from_file='seq2seq_models/eli5_t5_model_1024_4.pth' ,device='cuda:0' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=lowerCAmelCase__ )
def __lowerCamelCase ( ):
if LOAD_DENSE_INDEX:
A__ = faiss.StandardGpuResources()
A__ = datasets.load_dataset(path='wiki_snippets' ,name='wiki40b_en_100_0' )['train']
A__ = np.memmap(
'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat' ,dtype='float32' ,mode='r' ,shape=(wikiaab_passages.num_rows, 128) ,)
A__ = faiss.IndexFlatIP(128 )
A__ = faiss.index_cpu_to_gpu(lowerCAmelCase__ ,1 ,lowerCAmelCase__ )
wikiaab_gpu_index_flat.add(lowerCAmelCase__ ) # TODO fix for larger GPU
else:
A__ , A__ = (None, None)
A__ = Elasticsearch([{'host': 'localhost', 'port': '9200'}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=lowerCAmelCase__ )
def __lowerCamelCase ( ):
A__ = datasets.load_dataset('eli5' ,name='LFQA_reddit' )
A__ = elia['train_eli5']
A__ = np.memmap(
'eli5_questions_reps.dat' ,dtype='float32' ,mode='r' ,shape=(elia_train.num_rows, 128) )
A__ = faiss.IndexFlatIP(128 )
eli5_train_q_index.add(lowerCAmelCase__ )
return (elia_train, eli5_train_q_index)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = load_indexes()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = load_models()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = load_train_data()
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__=10 ):
A__ = embed_questions_for_retrieval([question] ,lowerCAmelCase__ ,lowerCAmelCase__ )
A__ , A__ = eli5_train_q_index.search(lowerCAmelCase__ ,lowerCAmelCase__ )
A__ = [elia_train[int(lowerCAmelCase__ )] for i in I[0]]
return nn_examples
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__="wiki40b" ,lowerCAmelCase__="dense" ,lowerCAmelCase__=10 ):
if source == "none":
A__ , A__ = (' <P> '.join(['' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
A__ , A__ = query_qa_dense_index(
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ )
else:
A__ , A__ = query_es_index(
lowerCAmelCase__ ,lowerCAmelCase__ ,index_name='english_wiki40b_snippets_100w' ,n_results=lowerCAmelCase__ ,)
A__ = [
(res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
]
A__ = 'question: {} context: {}'.format(lowerCAmelCase__ ,lowerCAmelCase__ )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda lowerCAmelCase__ : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCAmelCase__ : None),
} )
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__=64 ,lowerCAmelCase__=256 ,lowerCAmelCase__=False ,lowerCAmelCase__=2 ,lowerCAmelCase__=0.9_5 ,lowerCAmelCase__=0.8 ):
with torch.no_grad():
A__ = qa_sas_generate(
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,num_answers=1 ,num_beams=lowerCAmelCase__ ,min_len=lowerCAmelCase__ ,max_len=lowerCAmelCase__ ,do_sample=lowerCAmelCase__ ,temp=lowerCAmelCase__ ,top_p=lowerCAmelCase__ ,top_k=lowerCAmelCase__ ,max_input_length=1024 ,device='cuda:0' ,)[0]
return (answer, support_list)
st.title('''Long Form Question Answering with ELI5''')
# Start sidebar
SCREAMING_SNAKE_CASE : str = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
SCREAMING_SNAKE_CASE : List[Any] = '''
<html>
<head>
<style>
.img-container {
padding-left: 90px;
padding-right: 90px;
padding-top: 50px;
padding-bottom: 50px;
background-color: #f0f3f9;
}
</style>
</head>
<body>
<span class="img-container"> <!-- Inline parent element -->
%s
</span>
</body>
</html>
''' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
SCREAMING_SNAKE_CASE : List[Any] = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)
SCREAMING_SNAKE_CASE : Dict = [
'''Answer the question''',
'''View the retrieved document only''',
'''View the most similar ELI5 question and answer''',
'''Show me everything, please!''',
]
SCREAMING_SNAKE_CASE : Union[str, Any] = st.sidebar.checkbox('''Demo options''')
if demo_options:
SCREAMING_SNAKE_CASE : Union[str, Any] = st.sidebar.selectbox(
'''''',
action_list,
index=3,
)
SCREAMING_SNAKE_CASE : Dict = action_list.index(action_st)
SCREAMING_SNAKE_CASE : Optional[int] = st.sidebar.selectbox(
'''''',
['''Show full text of passages''', '''Show passage section titles'''],
index=0,
)
SCREAMING_SNAKE_CASE : Union[str, Any] = show_type == '''Show full text of passages'''
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = 3
SCREAMING_SNAKE_CASE : Any = True
SCREAMING_SNAKE_CASE : Dict = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
SCREAMING_SNAKE_CASE : int = '''
### Information retriever options
The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
'''
st.sidebar.markdown(retriever_info)
SCREAMING_SNAKE_CASE : Dict = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
SCREAMING_SNAKE_CASE : Optional[Any] = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
SCREAMING_SNAKE_CASE : Tuple = '''wiki40b'''
SCREAMING_SNAKE_CASE : List[str] = '''dense'''
SCREAMING_SNAKE_CASE : str = '''beam'''
SCREAMING_SNAKE_CASE : Tuple = 2
SCREAMING_SNAKE_CASE : Union[str, Any] = 64
SCREAMING_SNAKE_CASE : Tuple = 256
SCREAMING_SNAKE_CASE : Any = None
SCREAMING_SNAKE_CASE : Dict = None
SCREAMING_SNAKE_CASE : Dict = st.sidebar.checkbox('''Generation options''')
if generate_options:
SCREAMING_SNAKE_CASE : Optional[Any] = '''
### Answer generation options
The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
**beam** search, or **sample** from the decoder\'s output probabilities.
'''
st.sidebar.markdown(generate_info)
SCREAMING_SNAKE_CASE : Dict = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
SCREAMING_SNAKE_CASE : Any = st.sidebar.slider(
'''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
SCREAMING_SNAKE_CASE : int = st.sidebar.slider(
'''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
SCREAMING_SNAKE_CASE : Any = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = st.sidebar.slider(
'''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
)
SCREAMING_SNAKE_CASE : Tuple = st.sidebar.slider(
'''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
)
SCREAMING_SNAKE_CASE : List[str] = None
# start main text
SCREAMING_SNAKE_CASE : Dict = [
'''<MY QUESTION>''',
'''How do people make chocolate?''',
'''Why do we get a fever when we are sick?''',
'''How can different animals perceive different colors?''',
'''What is natural language processing?''',
'''What\'s the best way to treat a sunburn?''',
'''What exactly are vitamins ?''',
'''How does nuclear energy provide electricity?''',
'''What\'s the difference between viruses and bacteria?''',
'''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
'''Why do people like drinking coffee even though it tastes so bad?''',
'''What happens when wine ages? How does it make the wine taste better?''',
'''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
'''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
'''How does New Zealand have so many large bird predators?''',
]
SCREAMING_SNAKE_CASE : Tuple = st.selectbox(
'''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
SCREAMING_SNAKE_CASE : Tuple = st.text_input('''Enter your question here:''', '''''')
else:
SCREAMING_SNAKE_CASE : List[Any] = question_s
if st.button('''Show me!'''):
if action in [0, 1, 3]:
if index_type == "mixed":
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = make_support(question, source=wiki_source, method='''dense''', n_results=10)
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Union[str, Any] = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
SCREAMING_SNAKE_CASE : Any = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
SCREAMING_SNAKE_CASE : int = support_list[:10]
SCREAMING_SNAKE_CASE : str = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
else:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == '''sampled'''),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('''### The model generated answer is:''')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
for i, res in enumerate(support_list):
SCREAMING_SNAKE_CASE : str = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
SCREAMING_SNAKE_CASE : str = res[1].strip()
if sec_titles == "":
SCREAMING_SNAKE_CASE : List[Any] = '''[{}]({})'''.format(res[0], wiki_url)
else:
SCREAMING_SNAKE_CASE : int = sec_titles.split(''' & ''')
SCREAMING_SNAKE_CASE : List[Any] = ''' & '''.join(
['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
)
st.markdown(
'''{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'''.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
)
if action in [2, 3]:
SCREAMING_SNAKE_CASE : Tuple = find_nearest_training(question)
SCREAMING_SNAKE_CASE : Dict = nn_train_list[0]
st.markdown(
'''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
)
SCREAMING_SNAKE_CASE : List[Any] = [
'''{}. {}'''.format(i + 1, ''' \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
if i == 0 or sc > 2
]
st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))
SCREAMING_SNAKE_CASE : str = '''
---
**Disclaimer**
*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 554
|
"""simple docstring"""
class snake_case_ :
"""simple docstring"""
def __init__( self , __a , __a ):
"""simple docstring"""
A__ = name
A__ = val
def __str__( self ):
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self , __a ):
"""simple docstring"""
return self.val < other.val
class snake_case_ :
"""simple docstring"""
def __init__( self , __a ):
"""simple docstring"""
A__ = {}
A__ = {}
A__ = self.build_heap(__a )
def __getitem__( self , __a ):
"""simple docstring"""
return self.get_value(__a )
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
return (idx - 1) // 2
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
return idx * 2 + 1
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
return idx * 2 + 2
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
return self.heap_dict[key]
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = len(__a ) - 1
A__ = self.get_parent_idx(__a )
for idx, i in enumerate(__a ):
A__ = idx
A__ = i.val
for i in range(__a , -1 , -1 ):
self.sift_down(__a , __a )
return array
def _UpperCAmelCase ( self , __a , __a ):
"""simple docstring"""
while True:
A__ = self.get_left_child_idx(__a ) # noqa: E741
A__ = self.get_right_child_idx(__a )
A__ = idx
if l < len(__a ) and array[l] < array[idx]:
A__ = l
if r < len(__a ) and array[r] < array[smallest]:
A__ = r
if smallest != idx:
A__ , A__ = array[smallest], array[idx]
(
(
A__
) , (
A__
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
A__ = smallest
else:
break
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
A__ = self.get_parent_idx(__a )
while p >= 0 and self.heap[p] > self.heap[idx]:
A__ , A__ = self.heap[idx], self.heap[p]
A__ , A__ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
A__ = p
A__ = self.get_parent_idx(__a )
def _UpperCAmelCase ( self ):
"""simple docstring"""
return self.heap[0]
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ , A__ = self.heap[-1], self.heap[0]
A__ , A__ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
A__ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def _UpperCAmelCase ( self , __a ):
"""simple docstring"""
self.heap.append(__a )
A__ = len(self.heap ) - 1
A__ = node.val
self.sift_up(len(self.heap ) - 1 )
def _UpperCAmelCase ( self ):
"""simple docstring"""
return len(self.heap ) == 0
def _UpperCAmelCase ( self , __a , __a ):
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
A__ = new_value
A__ = new_value
self.sift_up(self.idx_of_element[node] )
SCREAMING_SNAKE_CASE : List[str] = Node('''R''', -1)
SCREAMING_SNAKE_CASE : Tuple = Node('''B''', 6)
SCREAMING_SNAKE_CASE : List[Any] = Node('''A''', 3)
SCREAMING_SNAKE_CASE : int = Node('''X''', 1)
SCREAMING_SNAKE_CASE : Union[str, Any] = Node('''E''', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
SCREAMING_SNAKE_CASE : str = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
print(i)
print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 554
| 1
|
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCamelCase__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : str=() , __lowerCamelCase : List[str]=None , __lowerCamelCase : List[str]="no" , __lowerCamelCase : Dict="29500" ):
__UpperCAmelCase : int = False
__UpperCAmelCase : List[Any] = False
if any(key.startswith("""KAGGLE""" ) for key in os.environ.keys() ):
__UpperCAmelCase : Any = True
elif "IPython" in sys.modules:
__UpperCAmelCase : Optional[Any] = """google.colab""" in str(sys.modules["""IPython"""].get_ipython() )
try:
__UpperCAmelCase : Dict = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
f"""Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.""" )
if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME""" , __lowerCamelCase ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"""To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
"""your training function. Restart your notebook and make sure no cells initializes an """
"""`Accelerator`.""" )
if num_processes is None:
__UpperCAmelCase : int = 8
__UpperCAmelCase : Optional[Any] = PrepareForLaunch(__lowerCamelCase , distributed_type="""TPU""" )
print(f"""Launching a training on {num_processes} TPU cores.""" )
xmp.spawn(__lowerCamelCase , args=__lowerCamelCase , nprocs=__lowerCamelCase , start_method="""fork""" )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print("""Launching training on one GPU.""" )
else:
print("""Launching training on one CPU.""" )
function(*__lowerCamelCase )
else:
if num_processes is None:
raise ValueError(
"""You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.""" )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
"""To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
"""inside your training function. Restart your notebook and make sure no cells initializes an """
"""`Accelerator`.""" )
if torch.cuda.is_initialized():
raise ValueError(
"""To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
"""using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
"""function.""" )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__lowerCamelCase , master_addr="""127.0.01""" , master_port=__lowerCamelCase , mixed_precision=__lowerCamelCase ):
__UpperCAmelCase : Dict = PrepareForLaunch(__lowerCamelCase , distributed_type="""MULTI_GPU""" )
print(f"""Launching training on {num_processes} GPUs.""" )
try:
start_processes(__lowerCamelCase , args=__lowerCamelCase , nprocs=__lowerCamelCase , start_method="""fork""" )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
"""CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
"""This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
"""Please review your imports and test them when running the `notebook_launcher()` to identify """
"""which one is problematic.""" ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
__UpperCAmelCase : Union[str, Any] = """1"""
print("""Launching training on MPS.""" )
elif torch.cuda.is_available():
print("""Launching training on one GPU.""" )
else:
print("""Launching training on CPU.""" )
function(*__lowerCamelCase )
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : List[Any]=() , __lowerCamelCase : Dict=2 ):
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=__lowerCamelCase , master_addr="""127.0.01""" , master_port="""29500""" , accelerate_mixed_precision="""no""" , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu="""yes""" , ):
__UpperCAmelCase : Tuple = PrepareForLaunch(__lowerCamelCase , debug=__lowerCamelCase )
start_processes(__lowerCamelCase , args=__lowerCamelCase , nprocs=__lowerCamelCase , start_method="""fork""" )
| 63
|
"""simple docstring"""
import requests
_A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowercase (_snake_case ) -> None:
'''simple docstring'''
__UpperCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["articles"] ,1 ):
print(f"""{i}.) {article["title"]}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")
| 505
| 0
|
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def UpperCamelCase_ ( *_lowerCAmelCase ,**_lowerCAmelCase ):
pass
@is_pipeline_test
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
@require_torch
def UpperCamelCase_ ( self ):
lowerCamelCase__ = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" ,)
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase__ = image_classifier(UpperCAmelCase__ ,candidate_labels=["""a""", """b""", """c"""] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(UpperCAmelCase__ ) ,[
[{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """b"""}, {"""score""": 0.333, """label""": """c"""}],
[{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """c"""}, {"""score""": 0.333, """label""": """b"""}],
] ,)
lowerCamelCase__ = image_classifier([image] * 5 ,candidate_labels=["""A""", """B""", """C"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
] ,)
@require_tf
def UpperCamelCase_ ( self ):
lowerCamelCase__ = pipeline(
model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification""" ,framework="""tf""" )
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase__ = image_classifier(UpperCAmelCase__ ,candidate_labels=["""a""", """b""", """c"""] )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[{"""score""": 0.333, """label""": """a"""}, {"""score""": 0.333, """label""": """b"""}, {"""score""": 0.333, """label""": """c"""}] ,)
lowerCamelCase__ = image_classifier([image] * 5 ,candidate_labels=["""A""", """B""", """C"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
[
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
{"""score""": 0.333, """label""": ANY(UpperCAmelCase__ )},
],
] ,)
@slow
@require_torch
def UpperCamelCase_ ( self ):
lowerCamelCase__ = pipeline(
task="""zero-shot-image-classification""" ,model="""openai/clip-vit-base-patch32""" ,)
# This is an image of 2 cats with remotes and no planes
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase__ = image_classifier(UpperCAmelCase__ ,candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
] ,)
lowerCamelCase__ = image_classifier([image] * 5 ,candidate_labels=["""cat""", """plane""", """remote"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[
[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
],
]
* 5 ,)
@slow
@require_tf
def UpperCamelCase_ ( self ):
lowerCamelCase__ = pipeline(
task="""zero-shot-image-classification""" ,model="""openai/clip-vit-base-patch32""" ,framework="""tf""" )
# This is an image of 2 cats with remotes and no planes
lowerCamelCase__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
lowerCamelCase__ = image_classifier(UpperCAmelCase__ ,candidate_labels=["""cat""", """plane""", """remote"""] )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
] ,)
lowerCamelCase__ = image_classifier([image] * 5 ,candidate_labels=["""cat""", """plane""", """remote"""] ,batch_size=2 )
self.assertEqual(
nested_simplify(UpperCAmelCase__ ) ,[
[
{"""score""": 0.511, """label""": """remote"""},
{"""score""": 0.485, """label""": """cat"""},
{"""score""": 0.004, """label""": """plane"""},
],
]
* 5 ,)
| 720
|
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ):
lowerCamelCase__ = tempfile.mkdtemp()
lowerCamelCase__ = BlipImageProcessor()
lowerCamelCase__ = BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel""" )
lowerCamelCase__ = BlipProcessor(_lowerCAmelCase ,_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname ,**_lowerCAmelCase ).tokenizer
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
return AutoProcessor.from_pretrained(self.tmpdirname ,**_lowerCAmelCase ).image_processor
def UpperCamelCase_ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = [np.random.randint(2_55 ,size=(3, 30, 4_00) ,dtype=np.uinta )]
lowerCamelCase__ = [Image.fromarray(np.moveaxis(_lowerCAmelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def UpperCamelCase_ ( self ):
lowerCamelCase__ = BlipProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCamelCase__ = self.get_tokenizer(bos_token="""(BOS)""" ,eos_token="""(EOS)""" )
lowerCamelCase__ = self.get_image_processor(do_normalize=_lowerCAmelCase ,padding_value=1.0 )
lowerCamelCase__ = BlipProcessor.from_pretrained(
self.tmpdirname ,bos_token="""(BOS)""" ,eos_token="""(EOS)""" ,do_normalize=_lowerCAmelCase ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,_lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = BlipProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = image_processor(_lowerCAmelCase ,return_tensors="""np""" )
lowerCamelCase__ = processor(images=_lowerCAmelCase ,return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = BlipProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = """lower newer"""
lowerCamelCase__ = processor(text=_lowerCAmelCase )
lowerCamelCase__ = tokenizer(_lowerCAmelCase ,return_token_type_ids=_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = BlipProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = """lower newer"""
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = processor(text=_lowerCAmelCase ,images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) ,["""pixel_values""", """input_ids""", """attention_mask"""] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = BlipProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCamelCase__ = processor.batch_decode(_lowerCAmelCase )
lowerCamelCase__ = tokenizer.batch_decode(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ):
lowerCamelCase__ = self.get_image_processor()
lowerCamelCase__ = self.get_tokenizer()
lowerCamelCase__ = BlipProcessor(tokenizer=_lowerCAmelCase ,image_processor=_lowerCAmelCase )
lowerCamelCase__ = """lower newer"""
lowerCamelCase__ = self.prepare_image_inputs()
lowerCamelCase__ = processor(text=_lowerCAmelCase ,images=_lowerCAmelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) ,["""pixel_values""", """input_ids""", """attention_mask"""] )
| 9
| 0
|
from collections.abc import Generator
def UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ ,__magic_name__ : Any = 0, 1
while True:
__magic_name__ ,__magic_name__ : Dict = b, a + b
yield b
def UpperCamelCase ( _A = 1000 ):
"""simple docstring"""
__magic_name__ : Optional[Any] = 1
__magic_name__ : Optional[int] = fibonacci_generator()
while len(str(next(__a ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 324
|
from sklearn.metrics import mean_squared_error
import datasets
lowerCamelCase__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
'''
lowerCamelCase__ = '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''
lowerCamelCase__ = '''
Args:
predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
references: array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
sample_weight: array-like of shape (n_samples,), default=None
Sample weights.
multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
"raw_values" : Returns a full set of errors in case of multioutput input.
"uniform_average" : Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.
Returns:
mse : mean squared error.
Examples:
>>> mse_metric = datasets.load_metric("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.375}
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{\'mse\': 0.6123724356957945}
If you\'re using multi-dimensional lists, then set the config as follows :
>>> mse_metric = datasets.load_metric("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{\'mse\': 0.7083333333333334}
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
>>> print(results) # doctest: +NORMALIZE_WHITESPACE
{\'mse\': array([0.41666667, 1. ])}
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __magic_name__ (datasets.Metric ):
def __a ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[
"https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
] , )
def __a ( self ) -> Union[str, Any]:
if self.config_name == "multilist":
return {
"predictions": datasets.Sequence(datasets.Value("float" ) ),
"references": datasets.Sequence(datasets.Value("float" ) ),
}
else:
return {
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
}
def __a ( self , _a , _a , _a=None , _a="uniform_average" , _a=True ) -> Dict:
lowerCAmelCase_ = mean_squared_error(
_a , _a , sample_weight=_a , multioutput=_a , squared=_a )
return {"mse": mse}
| 122
| 0
|
def _lowerCAmelCase (_lowerCAmelCase = 10_00):
UpperCamelCase_ = 2**power
UpperCamelCase_ = 0
while n:
UpperCamelCase_ , UpperCamelCase_ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 504
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : str =logging.get_logger(__name__)
UpperCAmelCase : Tuple ={
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class _lowercase (a_ ):
'''simple docstring'''
lowercase__ = """pegasus"""
lowercase__ = ["""past_key_values"""]
lowercase__ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , snake_case__=5_0265 , snake_case__=1024 , snake_case__=12 , snake_case__=4096 , snake_case__=16 , snake_case__=12 , snake_case__=4096 , snake_case__=16 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=True , snake_case__=True , snake_case__="gelu" , snake_case__=1024 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=0 , snake_case__=False , snake_case__=0 , snake_case__=1 , snake_case__=1 , **snake_case__ , ):
'''simple docstring'''
UpperCamelCase_ = vocab_size
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = d_model
UpperCamelCase_ = encoder_ffn_dim
UpperCamelCase_ = encoder_layers
UpperCamelCase_ = encoder_attention_heads
UpperCamelCase_ = decoder_ffn_dim
UpperCamelCase_ = decoder_layers
UpperCamelCase_ = decoder_attention_heads
UpperCamelCase_ = dropout
UpperCamelCase_ = attention_dropout
UpperCamelCase_ = activation_dropout
UpperCamelCase_ = activation_function
UpperCamelCase_ = init_std
UpperCamelCase_ = encoder_layerdrop
UpperCamelCase_ = decoder_layerdrop
UpperCamelCase_ = use_cache
UpperCamelCase_ = encoder_layers
UpperCamelCase_ = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=snake_case__ , eos_token_id=snake_case__ , is_encoder_decoder=snake_case__ , decoder_start_token_id=snake_case__ , forced_eos_token_id=snake_case__ , **snake_case__ , )
@property
def _lowerCamelCase ( self ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def _lowerCamelCase ( self ):
'''simple docstring'''
return self.d_model
| 504
| 1
|
def a (_lowerCAmelCase , _lowerCAmelCase ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 234
|
from __future__ import annotations
_lowercase : Optional[int] =1.6021E-19 # units = C
def lowerCAmelCase_ ( _lowercase : float , _lowercase : float , _lowercase : float , ) -> tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobility).count(0) != 1:
raise ValueError("""You cannot supply more or less than 2 values""")
elif conductivity < 0:
raise ValueError("""Conductivity cannot be negative""")
elif electron_conc < 0:
raise ValueError("""Electron concentration cannot be negative""")
elif mobility < 0:
raise ValueError("""mobility cannot be negative""")
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 136
| 0
|
'''simple docstring'''
import heapq
import sys
import numpy as np
UpperCamelCase__ = tuple[int, int]
class _UpperCAmelCase :
def __init__( self : Optional[int] ):
'''simple docstring'''
lowercase_ : List[str] = []
lowercase_ : List[str] = set()
def lowerCAmelCase__ ( self : List[str] ):
'''simple docstring'''
if not self.empty():
return self.elements[0][0]
else:
return float("inf" )
def lowerCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return len(self.elements ) == 0
def lowerCAmelCase__ ( self : Any , a : List[Any] , a : Tuple ):
'''simple docstring'''
if item not in self.set:
heapq.heappush(self.elements , (priority, item) )
self.set.add(a )
else:
# update
# print("update", item)
lowercase_ : int = []
(lowercase_) : Any = heapq.heappop(self.elements )
while x != item:
temp.append((pri, x) )
(lowercase_) : List[Any] = heapq.heappop(self.elements )
temp.append((priority, item) )
for pro, xxx in temp:
heapq.heappush(self.elements , (pro, xxx) )
def lowerCAmelCase__ ( self : Optional[int] , a : List[str] ):
'''simple docstring'''
if item in self.set:
self.set.remove(a )
lowercase_ : Optional[Any] = []
(lowercase_) : List[Any] = heapq.heappop(self.elements )
while x != item:
temp.append((pro, x) )
(lowercase_) : Dict = heapq.heappop(self.elements )
for prito, yyy in temp:
heapq.heappush(self.elements , (prito, yyy) )
def lowerCAmelCase__ ( self : str ):
'''simple docstring'''
return self.elements[0][1]
def lowerCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
(lowercase_) : Optional[Any] = heapq.heappop(self.elements )
self.set.remove(a )
return (priority, item)
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : int = np.array(_UpperCamelCase )
lowercase_ : Optional[int] = np.array(_UpperCamelCase )
return np.linalg.norm(a - b )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
return consistent_heuristic(_UpperCamelCase , _UpperCamelCase ) // t
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
return abs(p[0] - goal[0] ) + abs(p[1] - goal[1] )
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : List[Any] = g_function[start] + Wa * heuristics[i](_UpperCamelCase , _UpperCamelCase )
return ans
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Dict = np.chararray((n, n) )
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
lowercase_ : List[str] = "*"
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if (j, (n - 1) - i) in blocks:
lowercase_ : int = "#"
lowercase_ : Optional[int] = "-"
lowercase_ : List[Any] = back_pointer[goal]
while x != start:
(lowercase_) : Any = x
# print(x)
lowercase_ : Dict = "-"
lowercase_ : str = back_pointer[x]
lowercase_ : Any = "-"
for i in range(_UpperCamelCase ):
for j in range(_UpperCamelCase ):
if (i, j) == (0, n - 1):
print(grid[i][j] , end=" " )
print("<-- End position" , end=" " )
else:
print(grid[i][j] , end=" " )
print()
print("^" )
print("Start position" )
print()
print("# is an obstacle" )
print("- is the path taken by algorithm" )
print("PATH TAKEN BY THE ALGORITHM IS:-" )
lowercase_ : Dict = back_pointer[goal]
while x != start:
print(_UpperCamelCase , end=" " )
lowercase_ : Dict = back_pointer[x]
print(_UpperCamelCase )
sys.exit()
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
if p[0] < 0 or p[0] > n - 1:
return False
if p[1] < 0 or p[1] > n - 1:
return False
return True
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
"""simple docstring"""
for itera in range(_UpperCamelCase ):
open_list[itera].remove_element(_UpperCamelCase )
# print("s", s)
# print("j", j)
(lowercase_) : Dict = s
lowercase_ : Union[str, Any] = (x - 1, y)
lowercase_ : Tuple = (x + 1, y)
lowercase_ : str = (x, y + 1)
lowercase_ : Dict = (x, y - 1)
for neighbours in [left, right, up, down]:
if neighbours not in blocks:
if valid(_UpperCamelCase ) and neighbours not in visited:
# print("neighbour", neighbours)
visited.add(_UpperCamelCase )
lowercase_ : Union[str, Any] = -1
lowercase_ : List[str] = float("inf" )
if valid(_UpperCamelCase ) and g_function[neighbours] > g_function[s] + 1:
lowercase_ : Dict = g_function[s] + 1
lowercase_ : Optional[Any] = s
if neighbours not in close_list_anchor:
open_list[0].put(_UpperCamelCase , key(_UpperCamelCase , 0 , _UpperCamelCase , _UpperCamelCase ) )
if neighbours not in close_list_inad:
for var in range(1 , _UpperCamelCase ):
if key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) <= Wa * key(
_UpperCamelCase , 0 , _UpperCamelCase , _UpperCamelCase ):
open_list[j].put(
_UpperCamelCase , key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) )
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowercase_ : Any = []
for x in range(1 , 5 ):
for y in range(1 , 6 ):
some_list.append((x, y) )
for x in range(15 , 20 ):
some_list.append((x, 17) )
for x in range(10 , 19 ):
for y in range(1 , 15 ):
some_list.append((x, y) )
# L block
for x in range(1 , 4 ):
for y in range(12 , 19 ):
some_list.append((x, y) )
for x in range(3 , 13 ):
for y in range(16 , 19 ):
some_list.append((x, y) )
return some_list
UpperCamelCase__ = {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}
UpperCamelCase__ = [
(0, 1),
(1, 1),
(2, 1),
(3, 1),
(4, 1),
(5, 1),
(6, 1),
(7, 1),
(8, 1),
(9, 1),
(10, 1),
(11, 1),
(12, 1),
(13, 1),
(14, 1),
(15, 1),
(16, 1),
(17, 1),
(18, 1),
(19, 1),
]
UpperCamelCase__ = make_common_ground()
UpperCamelCase__ = blocks_blk
# hyper parameters
UpperCamelCase__ = 1
UpperCamelCase__ = 1
UpperCamelCase__ = 20
UpperCamelCase__ = 3 # one consistent and two other inconsistent
# start and end destination
UpperCamelCase__ = (0, 0)
UpperCamelCase__ = (n - 1, n - 1)
UpperCamelCase__ = 1
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple docstring"""
lowercase_ : List[str] = {start: 0, goal: float("inf" )}
lowercase_ : Any = {start: -1, goal: -1}
lowercase_ : List[Any] = []
lowercase_ : Dict = set()
for i in range(_UpperCamelCase ):
open_list.append(PriorityQueue() )
open_list[i].put(_UpperCamelCase , key(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) )
lowercase_ : list[int] = []
lowercase_ : list[int] = []
while open_list[0].minkey() < float("inf" ):
for i in range(1 , _UpperCamelCase ):
# print(open_list[0].minkey(), open_list[i].minkey())
if open_list[i].minkey() <= Wa * open_list[0].minkey():
global t
t += 1
if g_function[goal] <= open_list[i].minkey():
if g_function[goal] < float("inf" ):
do_something(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
lowercase_ : int = open_list[i].top_show()
visited.add(_UpperCamelCase )
expand_state(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , )
close_list_inad.append(_UpperCamelCase )
else:
if g_function[goal] <= open_list[0].minkey():
if g_function[goal] < float("inf" ):
do_something(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
lowercase_ : List[str] = open_list[0].top_show()
visited.add(_UpperCamelCase )
expand_state(
_UpperCamelCase , 0 , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , )
close_list_anchor.append(_UpperCamelCase )
print("No path found to goal" )
print()
for i in range(n - 1 , -1 , -1 ):
for j in range(_UpperCamelCase ):
if (j, i) in blocks:
print("#" , end=" " )
elif (j, i) in back_pointer:
if (j, i) == (n - 1, n - 1):
print("*" , end=" " )
else:
print("-" , end=" " )
else:
print("*" , end=" " )
if (j, i) == (n - 1, n - 1):
print("<-- End position" , end=" " )
print()
print("^" )
print("Start position" )
print()
print("# is an obstacle" )
print("- is the path taken by algorithm" )
if __name__ == "__main__":
multi_a_star(start, goal, n_heuristic)
| 709
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _UpperCAmelCase ( snake_case , unittest.TestCase ):
__lowerCamelCase: Dict = KandinskyVaaPriorPipeline
__lowerCamelCase: Optional[int] = ['prompt']
__lowerCamelCase: Any = ['prompt', 'negative_prompt']
__lowerCamelCase: List[Any] = [
'num_images_per_prompt',
'generator',
'num_inference_steps',
'latents',
'negative_prompt',
'guidance_scale',
'output_type',
'return_dict',
]
__lowerCamelCase: List[Any] = False
@property
def lowerCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
return 3_2
@property
def lowerCAmelCase__ ( self : Any ):
'''simple docstring'''
return 3_2
@property
def lowerCAmelCase__ ( self : Any ):
'''simple docstring'''
return self.time_input_dim
@property
def lowerCAmelCase__ ( self : str ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
return 1_0_0
@property
def lowerCAmelCase__ ( self : List[Any] ):
'''simple docstring'''
lowercase_ : Any = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def lowerCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , 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 , )
return CLIPTextModelWithProjection(a )
@property
def lowerCAmelCase__ ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ : List[str] = {
"num_attention_heads": 2,
"attention_head_dim": 1_2,
"embedding_dim": self.text_embedder_hidden_size,
"num_layers": 1,
}
lowercase_ : Union[str, Any] = PriorTransformer(**a )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
lowercase_ : List[Any] = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def lowerCAmelCase__ ( self : Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase_ : Dict = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=2_2_4 , projection_dim=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1_4 , )
lowercase_ : Optional[Any] = CLIPVisionModelWithProjection(a )
return model
@property
def lowerCAmelCase__ ( self : List[str] ):
'''simple docstring'''
lowercase_ : List[str] = CLIPImageProcessor(
crop_size=2_2_4 , do_center_crop=a , do_normalize=a , do_resize=a , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=2_2_4 , )
return image_processor
def lowerCAmelCase__ ( self : List[str] ):
'''simple docstring'''
lowercase_ : Any = self.dummy_prior
lowercase_ : Optional[Any] = self.dummy_image_encoder
lowercase_ : List[Any] = self.dummy_text_encoder
lowercase_ : Any = self.dummy_tokenizer
lowercase_ : Optional[Any] = self.dummy_image_processor
lowercase_ : List[str] = UnCLIPScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1_0_0_0 , clip_sample=a , clip_sample_range=10.0 , )
lowercase_ : List[Any] = {
"prior": prior,
"image_encoder": image_encoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
"image_processor": image_processor,
}
return components
def lowerCAmelCase__ ( self : Any , a : Dict , a : Dict=0 ):
'''simple docstring'''
if str(a ).startswith("mps" ):
lowercase_ : int = torch.manual_seed(a )
else:
lowercase_ : Optional[Any] = torch.Generator(device=a ).manual_seed(a )
lowercase_ : Any = {
"prompt": "horse",
"generator": generator,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def lowerCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase_ : str = "cpu"
lowercase_ : Any = self.get_dummy_components()
lowercase_ : int = self.pipeline_class(**a )
lowercase_ : Any = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
lowercase_ : Any = pipe(**self.get_dummy_inputs(a ) )
lowercase_ : List[Any] = output.image_embeds
lowercase_ : str = pipe(
**self.get_dummy_inputs(a ) , return_dict=a , )[0]
lowercase_ : Any = image[0, -1_0:]
lowercase_ : Dict = image_from_tuple[0, -1_0:]
assert image.shape == (1, 3_2)
lowercase_ : int = np.array(
[-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def lowerCAmelCase__ ( self : Optional[Any] ):
'''simple docstring'''
lowercase_ : int = torch_device == "cpu"
lowercase_ : Tuple = True
lowercase_ : str = False
self._test_inference_batch_single_identical(
test_max_difference=a , relax_max_difference=a , test_mean_pixel_difference=a , )
@skip_mps
def lowerCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase_ : Any = torch_device == "cpu"
lowercase_ : int = False
self._test_attention_slicing_forward_pass(
test_max_difference=a , test_mean_pixel_difference=a , )
| 640
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__lowerCAmelCase : Dict ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase : Union[str, Any] =["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__lowerCAmelCase : Union[str, Any] =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 440
|
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : int ="https://openaipublic.azureedge.net/jukebox/models/"
__lowerCAmelCase : Any ={
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def UpperCamelCase ( _lowerCamelCase : str ):
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.1.bias" , ".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.1.weight" , ".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.3.bias" , ".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
A__ = key.replace(".model.3.weight" , ".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
A__ = key.replace("conditioner_blocks.0" , "conditioner_blocks" )
if "prime_prior" in key:
A__ = key.replace("prime_prior" , "encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
A__ = key.replace(".emb." , "." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" , ".codebook" )
if "y_emb." in key:
return key.replace("y_emb." , "metadata_embedding." )
if "x_emb.emb." in key:
A__ = key.replace("0.x_emb.emb" , "embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" , "encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" , ".layer_norm" )
if "_ln" in key:
return key.replace("_ln" , "_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" , "encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" , "encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" , "fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" , "embed_tokens" )
return key
def UpperCamelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : Optional[int] , _lowerCamelCase : Dict , _lowerCamelCase : str ):
A__ = {}
import re
A__ = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
A__ = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
A__ = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
A__ = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
A__ = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_lowerCamelCase ):
A__ = re_encoder_block_conv_in.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] )
A__ = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
A__ = re_encoder_block_conv_in.sub(_lowerCamelCase , _lowerCamelCase )
elif re_encoder_block_resnet.fullmatch(_lowerCamelCase ):
A__ = re_encoder_block_resnet.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] )
A__ = {"1": 1, "3": 2}[groups[-2]]
A__ = F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
A__ = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
A__ = prefix + resnet_block
A__ = re_encoder_block_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase ):
A__ = re_encoder_block_proj_out.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
A__ = re_encoder_block_proj_out.sub(_lowerCamelCase , _lowerCamelCase )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase ):
A__ = re_decoder_block_conv_out.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] ) - 2
A__ = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
A__ = re_decoder_block_conv_out.sub(_lowerCamelCase , _lowerCamelCase )
elif re_decoder_block_resnet.fullmatch(_lowerCamelCase ):
A__ = re_decoder_block_resnet.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[2] ) * 2 + int(groups[3] ) - 2
A__ = {"1": 1, "3": 2}[groups[-2]]
A__ = F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
A__ = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
A__ = prefix + resnet_block
A__ = re_decoder_block_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase ):
A__ = re_decoder_block_proj_in.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
A__ = re_decoder_block_proj_in.sub(_lowerCamelCase , _lowerCamelCase )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_lowerCamelCase ):
A__ = re_prior_cond_conv_out.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[1] ) * 2 + int(groups[2] ) - 2
A__ = F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
A__ = re_prior_cond_conv_out.sub(_lowerCamelCase , _lowerCamelCase )
elif re_prior_cond_resnet.fullmatch(_lowerCamelCase ):
A__ = re_prior_cond_resnet.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = int(groups[1] ) * 2 + int(groups[2] ) - 2
A__ = {"1": 1, "3": 2}[groups[-2]]
A__ = F"conditioner_blocks.upsampler.upsample_block.{block_index}."
A__ = F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
A__ = prefix + resnet_block
A__ = re_prior_cond_resnet.sub(_lowerCamelCase , _lowerCamelCase )
elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase ):
A__ = re_prior_cond_proj_in.match(_lowerCamelCase )
A__ = regex_match.groups()
A__ = F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
A__ = re_prior_cond_proj_in.sub(_lowerCamelCase , _lowerCamelCase )
# keep original key
else:
A__ = original_key
A__ = replace_key(_lowerCamelCase )
if F"{key_prefix}.{key}" not in model_state_dict or key is None:
print(F"failed converting {original_key} to {key}, does not match" )
# handle missmatched shape
elif value.shape != model_state_dict[F"{key_prefix}.{key}"].shape:
A__ = model_state_dict[F"{key_prefix}.{key}"]
print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
A__ = original_key
A__ = original_key
A__ = value
return new_dict
@torch.no_grad()
def UpperCamelCase ( _lowerCamelCase : str=None , _lowerCamelCase : Dict=None ):
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" ):
A__ = requests.get(F"{PREFIX}{file}" , allow_redirects=_lowerCamelCase )
os.makedirs(F"{pytorch_dump_folder_path}/" , exist_ok=_lowerCamelCase )
open(F"{pytorch_dump_folder_path}/{file.split('/' )[-1]}" , "wb" ).write(r.content )
A__ = MODEL_MAPPING[model_name.split("/" )[-1]]
A__ = JukeboxConfig.from_pretrained(_lowerCamelCase )
A__ = JukeboxModel(_lowerCamelCase )
A__ = []
A__ = {}
for i, dict_name in enumerate(_lowerCamelCase ):
A__ = torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )["model"]
A__ = {}
for k in old_dic.keys():
if k.endswith(".b" ):
A__ = old_dic[k]
elif k.endswith(".w" ):
A__ = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
A__ = old_dic[k]
else:
A__ = old_dic[k]
A__ = "vqvae" if i == 0 else F"priors.{3 - i}"
A__ = fix_jukebox_keys(_lowerCamelCase , model.state_dict() , _lowerCamelCase , _lowerCamelCase )
weight_dict.append(_lowerCamelCase )
A__ = weight_dict.pop(0 )
model.vqvae.load_state_dict(_lowerCamelCase )
for i in range(len(_lowerCamelCase ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
with open(F"{pytorch_dump_folder_path}/mapping.json" , "w" ) as txtfile:
json.dump(_lowerCamelCase , _lowerCamelCase )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
return weight_dict
if __name__ == "__main__":
__lowerCAmelCase : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="jukebox-5b-lyrics",
type=str,
help="Name of the model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default="jukebox-5b-lyrics-converted",
type=str,
help="Path to the output PyTorch model directory.",
)
__lowerCAmelCase : int =parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 440
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, 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 a_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
a : Dict = StableDiffusionInpaintPipeline
a : Any = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
a : List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
a : Optional[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
a : List[str] = frozenset([] )
def _snake_case ( self : Optional[Any] ) ->Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__UpperCamelCase , )
_UpperCAmelCase = PNDMScheduler(skip_prk_steps=__UpperCamelCase )
torch.manual_seed(0 )
_UpperCAmelCase = 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 , sample_size=1_28 , )
torch.manual_seed(0 )
_UpperCAmelCase = 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=10_00 , hidden_act="""gelu""" , projection_dim=5_12 , )
_UpperCAmelCase = CLIPTextModel(__UpperCamelCase )
_UpperCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_UpperCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _snake_case ( self : Tuple , __UpperCamelCase : List[Any] , __UpperCamelCase : Optional[int]=0 ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = floats_tensor((1, 3, 32, 32) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("""RGB""" ).resize((64, 64) )
_UpperCAmelCase = Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
if str(__UpperCamelCase ).startswith("""mps""" ):
_UpperCAmelCase = torch.manual_seed(__UpperCamelCase )
else:
_UpperCAmelCase = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
_UpperCAmelCase = {
"""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 _snake_case ( self : List[str] ) ->int:
'''simple docstring'''
_UpperCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = StableDiffusionInpaintPipeline(**__UpperCamelCase )
_UpperCAmelCase = sd_pipe.to(__UpperCamelCase )
sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )
_UpperCAmelCase = self.get_dummy_inputs(__UpperCamelCase )
_UpperCAmelCase = sd_pipe(**__UpperCamelCase ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_UpperCAmelCase = np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _snake_case ( self : str ) ->List[Any]:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
def _snake_case ( self : Tuple ) ->Optional[Any]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _snake_case ( self : Tuple ) ->Dict:
'''simple docstring'''
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
_UpperCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench.npy""" )
_UpperCAmelCase = """stabilityai/stable-diffusion-2-inpainting"""
_UpperCAmelCase = StableDiffusionInpaintPipeline.from_pretrained(__UpperCamelCase , safety_checker=__UpperCamelCase )
pipe.to(__UpperCamelCase )
pipe.set_progress_bar_config(disable=__UpperCamelCase )
pipe.enable_attention_slicing()
_UpperCAmelCase = """Face of a yellow cat, high resolution, sitting on a park bench"""
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , generator=__UpperCamelCase , output_type="""np""" , )
_UpperCAmelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def _snake_case ( self : List[Any] ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
_UpperCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )
_UpperCAmelCase = """stabilityai/stable-diffusion-2-inpainting"""
_UpperCAmelCase = 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()
_UpperCAmelCase = """Face of a yellow cat, high resolution, sitting on a park bench"""
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , generator=__UpperCamelCase , output_type="""np""" , )
_UpperCAmelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def _snake_case ( self : str ) ->Any:
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
_UpperCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
_UpperCAmelCase = """stabilityai/stable-diffusion-2-inpainting"""
_UpperCAmelCase = PNDMScheduler.from_pretrained(__UpperCamelCase , subfolder="""scheduler""" )
_UpperCAmelCase = 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()
_UpperCAmelCase = """Face of a yellow cat, high resolution, sitting on a park bench"""
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(
prompt=__UpperCamelCase , image=__UpperCamelCase , mask_image=__UpperCamelCase , generator=__UpperCamelCase , num_inference_steps=2 , output_type="""np""" , )
_UpperCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.6_5 * 10**9
| 19
|
"""simple docstring"""
import argparse
import os
import re
import packaging.version
a : str = '''examples/'''
a : List[str] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
a : Tuple = {
'''init''': '''src/diffusers/__init__.py''',
'''setup''': '''setup.py''',
}
a : List[str] = '''README.md'''
def _UpperCamelCase ( _A , _A , _A ) -> Dict:
"""simple docstring"""
with open(_A , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_UpperCAmelCase = f.read()
_UpperCAmelCase ,_UpperCAmelCase = REPLACE_PATTERNS[pattern]
_UpperCAmelCase = replace.replace("""VERSION""" , _A )
_UpperCAmelCase = re_pattern.sub(_A , _A )
with open(_A , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(_A )
def _UpperCamelCase ( _A ) -> Union[str, Any]:
"""simple docstring"""
for folder, directories, fnames in os.walk(_A ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("""research_projects""" )
if "legacy" in directories:
directories.remove("""legacy""" )
for fname in fnames:
if fname.endswith(""".py""" ):
update_version_in_file(os.path.join(_A , _A ) , _A , pattern="""examples""" )
def _UpperCamelCase ( _A , _A=False ) -> int:
"""simple docstring"""
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(_A , _A , _A )
if not patch:
update_version_in_examples(_A )
def _UpperCamelCase ( ) -> Any:
"""simple docstring"""
_UpperCAmelCase = """🤗 Transformers currently provides the following architectures"""
_UpperCAmelCase = """1. Want to contribute a new model?"""
with open(_A , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
_UpperCAmelCase = f.readlines()
# Find the start of the list.
_UpperCAmelCase = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
_UpperCAmelCase = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("""1.""" ):
_UpperCAmelCase = lines[index].replace(
"""https://huggingface.co/docs/diffusers/main/model_doc""" , """https://huggingface.co/docs/diffusers/model_doc""" , )
index += 1
with open(_A , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(_A )
def _UpperCamelCase ( ) -> Optional[int]:
"""simple docstring"""
with open(REPLACE_FILES["""init"""] , """r""" ) as f:
_UpperCAmelCase = f.read()
_UpperCAmelCase = REPLACE_PATTERNS["""init"""][0].search(_A ).groups()[0]
return packaging.version.parse(_A )
def _UpperCamelCase ( _A=False ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = get_version()
if patch and default_version.is_devrelease:
raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""" )
if default_version.is_devrelease:
_UpperCAmelCase = default_version.base_version
elif patch:
_UpperCAmelCase = F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
else:
_UpperCAmelCase = F"""{default_version.major}.{default_version.minor + 1}.0"""
# Now let's ask nicely if that's the right one.
_UpperCAmelCase = input(F"""Which version are you releasing? [{default_version}]""" )
if len(_A ) == 0:
_UpperCAmelCase = default_version
print(F"""Updating version to {version}.""" )
global_version_update(_A , patch=_A )
def _UpperCamelCase ( ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = get_version()
_UpperCAmelCase = F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
_UpperCAmelCase = current_version.base_version
# Check with the user we got that right.
_UpperCAmelCase = input(F"""Which version are we developing now? [{dev_version}]""" )
if len(_A ) == 0:
_UpperCAmelCase = dev_version
print(F"""Updating version to {version}.""" )
global_version_update(_A )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
a : Dict = argparse.ArgumentParser()
parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
a : Tuple = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('''Nothing to do after a patch :-)''')
else:
post_release_work()
| 19
| 1
|
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Any):
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
A_ : Dict = flax_key_tuple[:-1] + ("""weight""",)
A_ : Optional[int] = torch.permute(_UpperCAmelCase , (0, 2, 1))
elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase):
# linear layer
A_ : Any = flax_key_tuple[:-1] + ("""weight""",)
A_ : int = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
A_ : List[Any] = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : List[str] , lowerCamelCase : List[Any]):
if "metadata" in layer:
A_ : str = layer.split("""metadata""")
A_ : List[str] = """""".join(split_layer[0])[:-1]
A_ : List[str] = [tuple(("""metadata""" + split_layer[1]).split("""/"""))]
elif "kvstore" in layer:
A_ : List[Any] = layer.split("""kvstore""")
A_ : Dict = """""".join(split_layer[0])[:-1]
A_ : Dict = [tuple(("""kvstore""" + split_layer[1]).split("""/"""))]
else:
A_ : Dict = layer.split("""/""")
A_ : List[str] = """/""".join(split_layer[:-1])
A_ : List[Any] = (split_layer[-1],)
if "kvstore/path" in layer:
A_ : str = F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
A_ : Union[str, Any] = """file"""
else:
A_ : str = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : List[str]):
A_ : Optional[int] = rename_keys(_UpperCAmelCase)
A_ : int = {}
for k, v in current_block.items():
A_ : Dict = v
A_ : Any = new_current_block
torch.save(_UpperCAmelCase , _UpperCAmelCase)
def lowerCamelCase ( lowerCamelCase : Tuple , lowerCamelCase : Any , lowerCamelCase : int , lowerCamelCase : Any , lowerCamelCase : str = WEIGHTS_NAME):
A_ : str = convert_file_size_to_int(_UpperCAmelCase)
A_ : Tuple = []
A_ : List[Any] = {}
A_ : Union[str, Any] = 0
A_ : Dict = 0
os.makedirs(_UpperCAmelCase , exist_ok=_UpperCAmelCase)
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""") as fp:
A_ : Union[str, Any] = serialization.msgpack_restore(fp.read())["""optimizer"""]["""target"""]
A_ : Union[str, Any] = flatten_dict(_UpperCAmelCase , sep="""/""")
A_ : int = {}
for layer in checkpoint_info.keys():
A_ , A_ , A_ : List[str] = get_key_and_tensorstore_dict(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase)
if curr_real_layer_name in all_layers:
A_ : Dict = content
else:
A_ : List[str] = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
A_ : int = ts.open(unflatten_dict(all_layers[key])).result().read().result()
A_ : List[Any] = torch.tensor(_UpperCAmelCase)
A_ : Optional[int] = raw_weights.numel() * dtype_byte_size(raw_weights.dtype)
# use the renaming pattern from the small conversion scripts
A_ , A_ : Any = rename_base_flax_keys(tuple(key.split("""/""")) , _UpperCAmelCase)
A_ : Optional[int] = """/""".join(_UpperCAmelCase)
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
A_ : Dict = os.path.join(
_UpperCAmelCase , weights_name.replace(""".bin""" , F'-{len(_UpperCAmelCase)+1:05d}-of-???.bin'))
rename_and_save_block(_UpperCAmelCase , _UpperCAmelCase)
sharded_state_dicts.append(current_block.keys())
del current_block
A_ : Optional[Any] = {}
A_ : List[Any] = 0
A_ : Tuple = raw_weights.to(getattr(_UpperCAmelCase , _UpperCAmelCase))
current_block_size += weight_size
total_size += weight_size
# Add the last block
A_ : int = os.path.join(_UpperCAmelCase , weights_name.replace(""".bin""" , F'-{len(_UpperCAmelCase)+1:05d}-of-???.bin'))
rename_and_save_block(_UpperCAmelCase , _UpperCAmelCase)
sharded_state_dicts.append(current_block.keys())
# If we only have one shard, we return it
if len(_UpperCAmelCase) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
A_ : Any = {}
A_ : Tuple = {}
for idx, shard in enumerate(_UpperCAmelCase):
A_ : List[Any] = weights_name.replace(
""".bin""" , F'-{idx+1:05d}-of-{len(_UpperCAmelCase):05d}.bin') # len(sharded_state_dicts):05d}
A_ : List[Any] = os.path.join(_UpperCAmelCase , weights_name.replace(""".bin""" , F'-{idx+1:05d}-of-???.bin'))
os.rename(_UpperCAmelCase , os.path.join(_UpperCAmelCase , _UpperCAmelCase))
A_ : Dict = shard
for key in shard:
A_ : str = shard_file
# Add the metadata
A_ : Optional[int] = {"""total_size""": total_size}
A_ : Union[str, Any] = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(_UpperCAmelCase , _UpperCAmelCase) , """w""" , encoding="""utf-8""") as f:
A_ : Any = json.dumps(_UpperCAmelCase , indent=2 , sort_keys=_UpperCAmelCase) + """\n"""
f.write(_UpperCAmelCase)
return metadata, index
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--switch_t5x_checkpoint_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
type=str,
required=False,
help='Path to the output pytorch model.',
)
__magic_name__ = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCamelCase ( ):
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
A_ : List[str] = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""")
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""")
A_ : List[str] = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""")
A_ : List[str] = TaTokenizer.from_pretrained("""t5-small""")
A_ : str = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
A_ : str = tokenizer(_UpperCAmelCase , return_tensors="""pt""").input_ids
A_ : str = model.generate(_UpperCAmelCase , decoder_start_token_id=0)
print(tokenizer.decode(out[0]))
| 665
|
'''simple docstring'''
import math
def a ( _UpperCAmelCase ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( _UpperCAmelCase = 1_0_0_0_1 ) -> int:
"""simple docstring"""
try:
a_ = int(_UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError('Parameter nth must be int or castable to int.' ) from None
if nth <= 0:
raise ValueError('Parameter nth must be greater than or equal to one.' )
a_ = []
a_ = 2
while len(_UpperCAmelCase ) < nth:
if is_prime(_UpperCAmelCase ):
primes.append(_UpperCAmelCase )
num += 1
else:
num += 1
return primes[len(_UpperCAmelCase ) - 1]
if __name__ == "__main__":
print(f'''{solution() = }''')
| 697
| 0
|
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class __A ( lowerCamelCase__ ):
"""simple docstring"""
UpperCAmelCase__ = """deberta-v2"""
def __init__( self , a__=12_8100 , a__=1536 , a__=24 , a__=24 , a__=6144 , a__="gelu" , a__=0.1 , a__=0.1 , a__=512 , a__=0 , a__=0.02 , a__=1e-7 , a__=False , a__=-1 , a__=0 , a__=True , a__=None , a__=0 , a__="gelu" , **a__ , ):
"""simple docstring"""
super().__init__(**a__)
_lowerCamelCase : List[str] = hidden_size
_lowerCamelCase : Any = num_hidden_layers
_lowerCamelCase : Tuple = num_attention_heads
_lowerCamelCase : List[Any] = intermediate_size
_lowerCamelCase : Dict = hidden_act
_lowerCamelCase : List[str] = hidden_dropout_prob
_lowerCamelCase : Tuple = attention_probs_dropout_prob
_lowerCamelCase : str = max_position_embeddings
_lowerCamelCase : List[Any] = type_vocab_size
_lowerCamelCase : Tuple = initializer_range
_lowerCamelCase : Optional[int] = relative_attention
_lowerCamelCase : List[str] = max_relative_positions
_lowerCamelCase : List[str] = pad_token_id
_lowerCamelCase : Tuple = position_biased_input
# Backwards compatibility
if type(a__) == str:
_lowerCamelCase : Union[str, Any] = [x.strip() for x in pos_att_type.lower().split('''|''')]
_lowerCamelCase : int = pos_att_type
_lowerCamelCase : Optional[int] = vocab_size
_lowerCamelCase : str = layer_norm_eps
_lowerCamelCase : Optional[Any] = kwargs.get('''pooler_hidden_size''' , a__)
_lowerCamelCase : Any = pooler_dropout
_lowerCamelCase : Union[str, Any] = pooler_hidden_act
class __A ( lowerCamelCase__ ):
"""simple docstring"""
@property
def __snake_case ( self):
"""simple docstring"""
if self.task == "multiple-choice":
_lowerCamelCase : int = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_lowerCamelCase : Union[str, Any] = {0: '''batch''', 1: '''sequence'''}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)])
else:
return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)])
@property
def __snake_case ( self):
"""simple docstring"""
return 12
def __snake_case ( self , a__ , a__ = -1 , a__ = -1 , a__ = -1 , a__ = False , a__ = None , a__ = 3 , a__ = 40 , a__ = 40 , a__ = None , ):
"""simple docstring"""
_lowerCamelCase : Optional[int] = super().generate_dummy_inputs(preprocessor=a__ , framework=a__)
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 613
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
'OPTForCausalLM',
'OPTModel',
'OPTPreTrainedModel',
'OPTForSequenceClassification',
'OPTForQuestionAnswering',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'FlaxOPTForCausalLM',
'FlaxOPTModel',
'FlaxOPTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 613
| 1
|
'''simple docstring'''
def _snake_case ( A_ : str , A_ : int ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(_A , x % y )
def _snake_case ( A_ : Optional[Any] , A_ : List[str] ):
"""simple docstring"""
return (x * y) // greatest_common_divisor(_A , _A )
def _snake_case ( A_ : Any = 20 ):
"""simple docstring"""
a_ : List[Any] = 1
for i in range(1 , n + 1 ):
a_ : str = lcm(_A , _A )
return g
if __name__ == "__main__":
print(F"""{solution() = }""")
| 577
|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( a_ ):
_SCREAMING_SNAKE_CASE = ["""image_processor""", """tokenizer"""]
_SCREAMING_SNAKE_CASE = """ChineseCLIPImageProcessor"""
_SCREAMING_SNAKE_CASE = ("""BertTokenizer""", """BertTokenizerFast""")
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Any=None , __SCREAMING_SNAKE_CASE : Tuple=None , **__SCREAMING_SNAKE_CASE : List[Any] ):
__a = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __SCREAMING_SNAKE_CASE , )
__a = kwargs.pop("feature_extractor" )
__a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__a = self.image_processor
def __call__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Dict=None , __SCREAMING_SNAKE_CASE : Tuple=None , **__SCREAMING_SNAKE_CASE : List[Any] ):
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
__a = self.tokenizer(__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if images is not None:
__a = self.image_processor(__SCREAMING_SNAKE_CASE , return_tensors=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
if text is not None and images is not None:
__a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE ) , tensor_type=__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : int , *__SCREAMING_SNAKE_CASE : Optional[Any] , **__SCREAMING_SNAKE_CASE : str ):
return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _UpperCAmelCase ( self : List[Any] , *__SCREAMING_SNAKE_CASE : Optional[int] , **__SCREAMING_SNAKE_CASE : Optional[int] ):
return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
@property
def _UpperCAmelCase ( self : Union[str, Any] ):
__a = self.tokenizer.model_input_names
__a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _UpperCAmelCase ( self : Any ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __SCREAMING_SNAKE_CASE , )
return self.image_processor_class
| 197
| 0
|
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer
class lowerCAmelCase__ ( __magic_name__ ):
'''simple docstring'''
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =tempfile.mkdtemp()
__A =5
# Realm tok
__A =[
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''test''',
'''question''',
'''this''',
'''is''',
'''the''',
'''first''',
'''second''',
'''third''',
'''fourth''',
'''fifth''',
'''record''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
__A =os.path.join(self.tmpdirname , '''realm_tokenizer''' )
os.makedirs(lowercase__ , exist_ok=lowercase__ )
__A =os.path.join(lowercase__ , 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] ) )
__A =os.path.join(self.tmpdirname , '''realm_block_records''' )
os.makedirs(lowercase__ , exist_ok=lowercase__ )
def __UpperCamelCase ( self ):
'''simple docstring'''
return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname , '''realm_tokenizer''' ) )
def __UpperCamelCase ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =RealmConfig(num_block_records=self.num_block_records )
return config
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =Dataset.from_dict(
{
'''id''': ['''0''', '''1'''],
'''question''': ['''foo''', '''bar'''],
'''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
} )
return dataset
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =np.array(
[
b'''This is the first record''',
b'''This is the second record''',
b'''This is the third record''',
b'''This is the fourth record''',
b'''This is the fifth record''',
b'''This is a longer longer longer record''',
] , dtype=lowercase__ , )
return block_records
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =RealmRetriever(
block_records=self.get_dummy_block_records() , tokenizer=self.get_tokenizer() , )
return retriever
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =self.get_config()
__A =self.get_dummy_retriever()
__A =retriever.tokenizer
__A =np.array([0, 3] , dtype='''long''' )
__A =tokenizer(['''Test question'''] ).input_ids
__A =tokenizer(
['''the fourth'''] , add_special_tokens=lowercase__ , return_token_type_ids=lowercase__ , return_attention_mask=lowercase__ , ).input_ids
__A =config.reader_seq_len
__A , __A , __A , __A =retriever(
lowercase__ , lowercase__ , answer_ids=lowercase__ , max_length=lowercase__ , return_tensors='''np''' )
self.assertEqual(len(lowercase__ ) , 2 )
self.assertEqual(len(lowercase__ ) , 2 )
self.assertEqual(len(lowercase__ ) , 2 )
self.assertEqual(concat_inputs.input_ids.shape , (2, 1_0) )
self.assertEqual(concat_inputs.attention_mask.shape , (2, 1_0) )
self.assertEqual(concat_inputs.token_type_ids.shape , (2, 1_0) )
self.assertEqual(concat_inputs.special_tokens_mask.shape , (2, 1_0) )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) , ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]'''] , )
self.assertEqual(
tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) , ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]'''] , )
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =self.get_config()
__A =self.get_dummy_retriever()
__A =retriever.tokenizer
__A =np.array([0, 3, 5] , dtype='''long''' )
__A =tokenizer(['''Test question'''] ).input_ids
__A =tokenizer(
['''the fourth''', '''longer longer'''] , add_special_tokens=lowercase__ , return_token_type_ids=lowercase__ , return_attention_mask=lowercase__ , ).input_ids
__A =config.reader_seq_len
__A , __A , __A , __A =retriever(
lowercase__ , lowercase__ , answer_ids=lowercase__ , max_length=lowercase__ , return_tensors='''np''' )
self.assertEqual([False, True, True] , lowercase__ )
self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] , lowercase__ )
self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] , lowercase__ )
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =self.get_dummy_retriever()
retriever.save_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
# Test local path
__A =retriever.from_pretrained(os.path.join(self.tmpdirname , '''realm_block_records''' ) )
self.assertEqual(retriever.block_records[0] , b'''This is the first record''' )
# Test mocked remote path
with patch('''transformers.models.realm.retrieval_realm.hf_hub_download''' ) as mock_hf_hub_download:
__A =os.path.join(
os.path.join(self.tmpdirname , '''realm_block_records''' ) , _REALM_BLOCK_RECORDS_FILENAME )
__A =RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )
self.assertEqual(retriever.block_records[0] , b'''This is the first record''' )
| 708
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : Dict = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = ['''ViTFeatureExtractor''']
_lowerCamelCase : int = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : List[str] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Union[str, Any] = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase : Optional[int] = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
_lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 516
| 0
|
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
return image
elif isinstance(_SCREAMING_SNAKE_CASE , PIL.Image.Image ):
__lowercase = [image]
if isinstance(image[0] , PIL.Image.Image ):
__lowercase = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"] ) )[None, :] for i in image]
__lowercase = np.concatenate(_SCREAMING_SNAKE_CASE , axis=0 )
__lowercase = np.array(_SCREAMING_SNAKE_CASE ).astype(np.floataa ) / 2_5_5.0
__lowercase = image.transpose(0 , 3 , 1 , 2 )
__lowercase = 2.0 * image - 1.0
__lowercase = torch.from_numpy(_SCREAMING_SNAKE_CASE )
elif isinstance(image[0] , torch.Tensor ):
__lowercase = torch.cat(_SCREAMING_SNAKE_CASE , dim=0 )
return image
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0.9_9_9_5 ):
if not isinstance(_SCREAMING_SNAKE_CASE , np.ndarray ):
__lowercase = True
__lowercase = va.device
__lowercase = va.cpu().numpy()
__lowercase = va.cpu().numpy()
__lowercase = np.sum(va * va / (np.linalg.norm(_SCREAMING_SNAKE_CASE ) * np.linalg.norm(_SCREAMING_SNAKE_CASE )) )
if np.abs(_SCREAMING_SNAKE_CASE ) > DOT_THRESHOLD:
__lowercase = (1 - t) * va + t * va
else:
__lowercase = np.arccos(_SCREAMING_SNAKE_CASE )
__lowercase = np.sin(_SCREAMING_SNAKE_CASE )
__lowercase = theta_a * t
__lowercase = np.sin(_SCREAMING_SNAKE_CASE )
__lowercase = np.sin(theta_a - theta_t ) / sin_theta_a
__lowercase = sin_theta_t / sin_theta_a
__lowercase = sa * va + sa * va
if inputs_are_torch:
__lowercase = torch.from_numpy(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
return va
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowercase = F.normalize(_SCREAMING_SNAKE_CASE , dim=-1 )
__lowercase = F.normalize(_SCREAMING_SNAKE_CASE , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for param in model.parameters():
__lowercase = value
class _A ( _lowercase ):
'''simple docstring'''
def __init__( self : int , lowerCamelCase : AutoencoderKL , lowerCamelCase : CLIPTextModel , lowerCamelCase : CLIPModel , lowerCamelCase : CLIPTokenizer , lowerCamelCase : UNetaDConditionModel , lowerCamelCase : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , lowerCamelCase : CLIPFeatureExtractor , lowerCamelCase : Dict=None , lowerCamelCase : Optional[int]=None , lowerCamelCase : str=None , ):
'''simple docstring'''
super().__init__()
self.register_modules(
vae=lowerCamelCase , text_encoder=lowerCamelCase , clip_model=lowerCamelCase , tokenizer=lowerCamelCase , unet=lowerCamelCase , scheduler=lowerCamelCase , feature_extractor=lowerCamelCase , coca_model=lowerCamelCase , coca_tokenizer=lowerCamelCase , coca_transform=lowerCamelCase , )
__lowercase = (
feature_extractor.size
if isinstance(feature_extractor.size , lowerCamelCase )
else feature_extractor.size["shortest_edge"]
)
__lowercase = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , lowerCamelCase )
set_requires_grad(self.clip_model , lowerCamelCase )
def _snake_case ( self : Any , lowerCamelCase : Optional[Union[str, int]] = "auto" ):
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__lowercase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
self.enable_attention_slicing(lowerCamelCase )
def _snake_case ( self : List[str] ):
'''simple docstring'''
set_requires_grad(self.vae , lowerCamelCase )
def _snake_case ( self : List[Any] ):
'''simple docstring'''
set_requires_grad(self.vae , lowerCamelCase )
def _snake_case ( self : Tuple ):
'''simple docstring'''
set_requires_grad(self.unet , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
set_requires_grad(self.unet , lowerCamelCase )
def _snake_case ( self : str , lowerCamelCase : List[Any] , lowerCamelCase : Any , lowerCamelCase : Optional[int] ):
'''simple docstring'''
__lowercase = min(int(num_inference_steps * strength ) , lowerCamelCase )
__lowercase = max(num_inference_steps - init_timestep , 0 )
__lowercase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def _snake_case ( self : List[Any] , lowerCamelCase : int , lowerCamelCase : Optional[Any] , lowerCamelCase : str , lowerCamelCase : List[str] , lowerCamelCase : int , lowerCamelCase : str=None ):
'''simple docstring'''
if not isinstance(lowerCamelCase , torch.Tensor ):
raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase )}""" )
__lowercase = image.to(device=lowerCamelCase , dtype=lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCamelCase )
]
__lowercase = torch.cat(lowerCamelCase , dim=0 )
else:
__lowercase = self.vae.encode(lowerCamelCase ).latent_dist.sample(lowerCamelCase )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__lowercase = 0.1_8215 * init_latents
__lowercase = init_latents.repeat_interleave(lowerCamelCase , dim=0 )
__lowercase = randn_tensor(init_latents.shape , generator=lowerCamelCase , device=lowerCamelCase , dtype=lowerCamelCase )
# get latents
__lowercase = self.scheduler.add_noise(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = init_latents
return latents
def _snake_case ( self : Union[str, Any] , lowerCamelCase : List[Any] ):
'''simple docstring'''
__lowercase = self.coca_transform(lowerCamelCase ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
__lowercase = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
__lowercase = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," )
def _snake_case ( self : Any , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[Any] ):
'''simple docstring'''
__lowercase = self.feature_extractor.preprocess(lowerCamelCase )
__lowercase = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
__lowercase = self.clip_model.get_image_features(lowerCamelCase )
__lowercase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowerCamelCase )
__lowercase = image_embeddings_clip.repeat_interleave(lowerCamelCase , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def _snake_case ( self : Union[str, Any] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Any , lowerCamelCase : List[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Tuple , lowerCamelCase : Tuple , ):
'''simple docstring'''
__lowercase = latents.detach().requires_grad_()
__lowercase = self.scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
# predict the noise residual
__lowercase = self.unet(lowerCamelCase , lowerCamelCase , encoder_hidden_states=lowerCamelCase ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
__lowercase = self.scheduler.alphas_cumprod[timestep]
__lowercase = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__lowercase = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
__lowercase = torch.sqrt(lowerCamelCase )
__lowercase = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , lowerCamelCase ):
__lowercase = self.scheduler.sigmas[index]
__lowercase = latents - sigma * noise_pred
else:
raise ValueError(f"""scheduler type {type(self.scheduler )} not supported""" )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__lowercase = 1 / 0.1_8215 * sample
__lowercase = self.vae.decode(lowerCamelCase ).sample
__lowercase = (image / 2 + 0.5).clamp(0 , 1 )
__lowercase = transforms.Resize(self.feature_extractor_size )(lowerCamelCase )
__lowercase = self.normalize(lowerCamelCase ).to(latents.dtype )
__lowercase = self.clip_model.get_image_features(lowerCamelCase )
__lowercase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowerCamelCase )
__lowercase = spherical_dist_loss(lowerCamelCase , lowerCamelCase ).mean() * clip_guidance_scale
__lowercase = -torch.autograd.grad(lowerCamelCase , lowerCamelCase )[0]
if isinstance(self.scheduler , lowerCamelCase ):
__lowercase = latents.detach() + grads * (sigma**2)
__lowercase = noise_pred_original
else:
__lowercase = noise_pred_original - torch.sqrt(lowerCamelCase ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self : List[str] , lowerCamelCase : Union[torch.FloatTensor, PIL.Image.Image] , lowerCamelCase : Union[torch.FloatTensor, PIL.Image.Image] , lowerCamelCase : Optional[str] = None , lowerCamelCase : Optional[str] = None , lowerCamelCase : Optional[int] = 512 , lowerCamelCase : Optional[int] = 512 , lowerCamelCase : float = 0.6 , lowerCamelCase : Optional[int] = 50 , lowerCamelCase : Optional[float] = 7.5 , lowerCamelCase : Optional[int] = 1 , lowerCamelCase : float = 0.0 , lowerCamelCase : Optional[float] = 100 , lowerCamelCase : Optional[torch.Generator] = None , lowerCamelCase : Optional[str] = "pil" , lowerCamelCase : bool = True , lowerCamelCase : float = 0.8 , lowerCamelCase : float = 0.1 , lowerCamelCase : float = 0.1 , ):
'''simple docstring'''
if isinstance(lowerCamelCase , lowerCamelCase ) and len(lowerCamelCase ) != batch_size:
raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(lowerCamelCase )} generators.""" )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )
if isinstance(lowerCamelCase , torch.Generator ) and batch_size > 1:
__lowercase = [generator] + [None] * (batch_size - 1)
__lowercase = [
("model", self.coca_model is None),
("tokenizer", self.coca_tokenizer is None),
("transform", self.coca_transform is None),
]
__lowercase = [x[0] for x in coca_is_none if x[1]]
__lowercase = ", ".join(lowerCamelCase )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(lowerCamelCase ):
raise ValueError(
f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
__lowercase = self.get_image_description(lowerCamelCase )
if style_prompt is None:
if len(lowerCamelCase ):
raise ValueError(
f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" )
__lowercase = self.get_image_description(lowerCamelCase )
# get prompt text embeddings for content and style
__lowercase = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowerCamelCase , return_tensors="pt" , )
__lowercase = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
__lowercase = self.tokenizer(
lowerCamelCase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowerCamelCase , return_tensors="pt" , )
__lowercase = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
__lowercase = slerp(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# duplicate text embeddings for each generation per prompt
__lowercase = text_embeddings.repeat_interleave(lowerCamelCase , dim=0 )
# set timesteps
__lowercase = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
__lowercase = {}
if accepts_offset:
__lowercase = 1
self.scheduler.set_timesteps(lowerCamelCase , **lowerCamelCase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
__lowercase , __lowercase = self.get_timesteps(lowerCamelCase , lowerCamelCase , self.device )
__lowercase = timesteps[:1].repeat(lowerCamelCase )
# Preprocess image
__lowercase = preprocess(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = self.prepare_latents(
lowerCamelCase , lowerCamelCase , lowerCamelCase , text_embeddings.dtype , self.device , lowerCamelCase )
__lowercase = preprocess(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = self.prepare_latents(
lowerCamelCase , lowerCamelCase , lowerCamelCase , text_embeddings.dtype , self.device , lowerCamelCase )
__lowercase = slerp(lowerCamelCase , lowerCamelCase , lowerCamelCase )
if clip_guidance_scale > 0:
__lowercase = self.get_clip_image_embeddings(lowerCamelCase , lowerCamelCase )
__lowercase = self.get_clip_image_embeddings(lowerCamelCase , lowerCamelCase )
__lowercase = slerp(
lowerCamelCase , lowerCamelCase , lowerCamelCase )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
__lowercase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
__lowercase = content_text_input.input_ids.shape[-1]
__lowercase = self.tokenizer([""] , padding="max_length" , max_length=lowerCamelCase , return_tensors="pt" )
__lowercase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
__lowercase = uncond_embeddings.repeat_interleave(lowerCamelCase , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
__lowercase = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
__lowercase = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
__lowercase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
__lowercase = torch.randn(lowerCamelCase , generator=lowerCamelCase , device="cpu" , dtype=lowerCamelCase ).to(
self.device )
else:
__lowercase = torch.randn(lowerCamelCase , generator=lowerCamelCase , device=self.device , dtype=lowerCamelCase )
else:
if latents.shape != latents_shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
__lowercase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
__lowercase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
__lowercase = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
__lowercase = {}
if accepts_eta:
__lowercase = eta
# check if the scheduler accepts generator
__lowercase = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
__lowercase = generator
with self.progress_bar(total=lowerCamelCase ):
for i, t in enumerate(lowerCamelCase ):
# expand the latents if we are doing classifier free guidance
__lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
__lowercase = self.scheduler.scale_model_input(lowerCamelCase , lowerCamelCase )
# predict the noise residual
__lowercase = self.unet(lowerCamelCase , lowerCamelCase , encoder_hidden_states=lowerCamelCase ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
__lowercase , __lowercase = noise_pred.chunk(2 )
__lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
__lowercase = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
__lowercase , __lowercase = self.cond_fn(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
# compute the previous noisy sample x_t -> x_t-1
__lowercase = self.scheduler.step(lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
__lowercase = 1 / 0.1_8215 * latents
__lowercase = self.vae.decode(lowerCamelCase ).sample
__lowercase = (image / 2 + 0.5).clamp(0 , 1 )
__lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
__lowercase = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=lowerCamelCase , nsfw_content_detected=lowerCamelCase )
| 402
|
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 ( _lowercase , unittest.TestCase ):
'''simple docstring'''
_snake_case : int = RoFormerTokenizer
_snake_case : Optional[Any] = RoFormerTokenizerFast
_snake_case : int = True
_snake_case : Tuple = True
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
super().setUp()
def _snake_case ( self : Optional[int] , **lowerCamelCase : int ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **lowerCamelCase )
def _snake_case ( self : List[Any] , **lowerCamelCase : List[str] ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base" , **lowerCamelCase )
def _snake_case ( self : Dict ):
'''simple docstring'''
__lowercase = "永和服装饰品有限公司,今天天气非常好"
__lowercase = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
return input_text, output_text
def _snake_case ( self : int ):
'''simple docstring'''
__lowercase = self.get_tokenizer()
__lowercase , __lowercase = self.get_chinese_input_output_texts()
__lowercase = tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , output_text.split() )
__lowercase = tokens + [tokenizer.unk_token]
__lowercase = [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , lowerCamelCase )
def _snake_case ( self : str ):
'''simple docstring'''
__lowercase = self.get_rust_tokenizer()
__lowercase , __lowercase = self.get_chinese_input_output_texts()
__lowercase = tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , output_text.split() )
__lowercase = tokens + [tokenizer.unk_token]
__lowercase = [22_943, 21_332, 34_431, 45_904, 117, 306, 1_231, 1_231, 2_653, 33_994, 1_266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , lowerCamelCase )
def _snake_case ( self : int ):
'''simple docstring'''
pass
def _snake_case ( self : Union[str, Any] ):
'''simple docstring'''
pass
def _snake_case ( self : str ):
'''simple docstring'''
pass
| 402
| 1
|
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
snake_case_ : Optional[int] = [2, 2, 6, 2] if "tiny" in model_name else [2, 2, 18, 2]
snake_case_ : List[Any] = True if "large" in model_name or "huge" in model_name else False
snake_case_ : List[Any] = True if "large" in model_name or "huge" in model_name else False
snake_case_ : int = True if "large" in model_name or "huge" in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
snake_case_ : List[Any] = [3, 3, 3, 3]
snake_case_ : Any = [5, 5, 5, 5]
elif "fl4" in model_name:
snake_case_ : int = [4, 4, 4, 4]
snake_case_ : Optional[Any] = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
snake_case_ : str = [3, 3, 3, 3]
if "lrf" in model_name:
snake_case_ : List[Any] = [3, 3, 3, 3]
else:
snake_case_ : List[Any] = [2, 2, 2, 2]
if "tiny" in model_name:
snake_case_ : List[Any] = 96
elif "small" in model_name:
snake_case_ : Any = 96
elif "base" in model_name:
snake_case_ : str = 128
elif "large" in model_name:
snake_case_ : List[Any] = 192
elif "xlarge" in model_name:
snake_case_ : int = 256
elif "huge" in model_name:
snake_case_ : str = 352
# set label information
snake_case_ : int = "huggingface/label-files"
if "large" in model_name or "huge" in model_name:
snake_case_ : List[Any] = "imagenet-22k-id2label.json"
else:
snake_case_ : List[str] = "imagenet-1k-id2label.json"
snake_case_ : Union[str, Any] = json.load(open(hf_hub_download(__magic_name__ ,__magic_name__ ,repo_type="dataset" ) ,"r" ) )
snake_case_ : str = {int(__magic_name__ ): v for k, v in idalabel.items()}
snake_case_ : str = {v: k for k, v in idalabel.items()}
snake_case_ : int = FocalNetConfig(
embed_dim=__magic_name__ ,depths=__magic_name__ ,focal_levels=__magic_name__ ,focal_windows=__magic_name__ ,use_conv_embed=__magic_name__ ,idalabel=__magic_name__ ,labelaid=__magic_name__ ,use_post_layernorm=__magic_name__ ,use_layerscale=__magic_name__ ,)
return config
def __UpperCAmelCase ( __magic_name__ )-> List[str]:
"""simple docstring"""
if "patch_embed.proj" in name:
snake_case_ : Union[str, Any] = name.replace("patch_embed.proj" ,"embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
snake_case_ : Tuple = name.replace("patch_embed.norm" ,"embeddings.norm" )
if "layers" in name:
snake_case_ : Optional[int] = "encoder." + name
if "encoder.layers" in name:
snake_case_ : Tuple = name.replace("encoder.layers" ,"encoder.stages" )
if "downsample.proj" in name:
snake_case_ : Tuple = name.replace("downsample.proj" ,"downsample.projection" )
if "blocks" in name:
snake_case_ : int = name.replace("blocks" ,"layers" )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
snake_case_ : List[Any] = name.replace("modulation.f" ,"modulation.projection_in" )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
snake_case_ : str = name.replace("modulation.h" ,"modulation.projection_context" )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
snake_case_ : int = name.replace("modulation.proj" ,"modulation.projection_out" )
if name == "norm.weight":
snake_case_ : Any = "layernorm.weight"
if name == "norm.bias":
snake_case_ : Any = "layernorm.bias"
if "head" in name:
snake_case_ : Optional[Any] = name.replace("head" ,"classifier" )
else:
snake_case_ : List[str] = "focalnet." + name
return name
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__=False )-> Dict:
"""simple docstring"""
snake_case_ : Tuple = {
"focalnet-tiny": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth",
"focalnet-tiny-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth",
"focalnet-small": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth",
"focalnet-small-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth",
"focalnet-base": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth",
"focalnet-base-lrf": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth",
"focalnet-large-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth",
"focalnet-large-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth",
"focalnet-xlarge-lrf-fl3": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth",
"focalnet-xlarge-lrf-fl4": "https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth",
}
# fmt: on
snake_case_ : Any = model_name_to_url[model_name]
print("Checkpoint URL: " ,__magic_name__ )
snake_case_ : Union[str, Any] = torch.hub.load_state_dict_from_url(__magic_name__ ,map_location="cpu" )["model"]
# rename keys
for key in state_dict.copy().keys():
snake_case_ : Optional[int] = state_dict.pop(__magic_name__ )
snake_case_ : List[str] = val
snake_case_ : str = get_focalnet_config(__magic_name__ )
snake_case_ : List[Any] = FocalNetForImageClassification(__magic_name__ )
model.eval()
# load state dict
model.load_state_dict(__magic_name__ )
# verify conversion
snake_case_ : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case_ : Any = BitImageProcessor(
do_resize=__magic_name__ ,size={"shortest_edge": 256} ,resample=PILImageResampling.BILINEAR ,do_center_crop=__magic_name__ ,crop_size=224 ,do_normalize=__magic_name__ ,image_mean=__magic_name__ ,image_std=__magic_name__ ,)
snake_case_ : Any = Image.open(requests.get(__magic_name__ ,stream=__magic_name__ ).raw )
snake_case_ : Tuple = processor(images=__magic_name__ ,return_tensors="pt" )
snake_case_ : Union[str, Any] = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406] ,std=[0.229, 0.224, 0.225] ),
] )
snake_case_ : Optional[Any] = image_transforms(__magic_name__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values ,__magic_name__ ,atol=1E-4 )
snake_case_ : int = model(**__magic_name__ )
snake_case_ : Dict = outputs.logits.argmax(-1 ).item()
print("Predicted class:" ,model.config.idalabel[predicted_class_idx] )
print("First values of logits:" ,outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
snake_case_ : Tuple = torch.tensor([0.2_166, -0.4_368, 0.2_191] )
elif model_name == "focalnet-tiny-lrf":
snake_case_ : Optional[Any] = torch.tensor([1.1_669, 0.0_125, -0.1_695] )
elif model_name == "focalnet-small":
snake_case_ : List[Any] = torch.tensor([0.4_917, -0.0_430, 0.1_341] )
elif model_name == "focalnet-small-lrf":
snake_case_ : Optional[int] = torch.tensor([-0.2_588, -0.5_342, -0.2_331] )
elif model_name == "focalnet-base":
snake_case_ : List[Any] = torch.tensor([-0.1_655, -0.4_090, -0.1_730] )
elif model_name == "focalnet-base-lrf":
snake_case_ : List[str] = torch.tensor([0.5_306, -0.0_483, -0.3_928] )
assert torch.allclose(outputs.logits[0, :3] ,__magic_name__ ,atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and processor of {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__magic_name__ )
processor.save_pretrained(__magic_name__ )
if push_to_hub:
print(F'''Pushing model and processor of {model_name} to the hub...''' )
model.push_to_hub(F'''{model_name}''' )
processor.push_to_hub(F'''{model_name}''' )
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''focalnet-tiny''',
type=str,
help='''Name of the FocalNet model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub.''',
)
__lowerCamelCase : List[Any] = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 656
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__lowerCamelCase : Dict = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : int = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
__lowerCamelCase : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 656
| 1
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
"""self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
"""self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
"""self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
"""self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
"""self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
"""self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
"""self_attn.rotary_emb""": """encoder.embed_positions""",
"""self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
"""conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
"""conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
"""conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
"""conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
"""conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
"""ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
"""ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
"""ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
"""ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
"""ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
"""ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
__lowerCAmelCase = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def UpperCAmelCase_ (__a : int , __a : int , __a : Dict , __a : List[str] , __a : List[Any] ):
"""simple docstring"""
for attribute in key.split('.' ):
_a : List[str] = getattr(__a , __a )
if weight_type is not None:
_a : Any = getattr(__a , __a ).shape
else:
_a : Optional[int] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
f""" {value.shape} for {full_name}""" )
if weight_type == "weight":
_a : Dict = value
elif weight_type == "weight_g":
_a : Tuple = value
elif weight_type == "weight_v":
_a : List[str] = value
elif weight_type == "bias":
_a : List[str] = value
elif weight_type == "running_mean":
_a : Any = value
elif weight_type == "running_var":
_a : Optional[int] = value
elif weight_type == "num_batches_tracked":
_a : int = value
elif weight_type == "inv_freq":
_a : Dict = value
else:
_a : int = value
logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def UpperCAmelCase_ (__a : int , __a : Dict , __a : Optional[Any] ):
"""simple docstring"""
_a : Tuple = []
_a : Optional[Any] = fairseq_model.state_dict()
_a : Optional[int] = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
_a : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
__a , __a , __a , __a , hf_model.config.feat_extract_norm == 'group' , )
_a : Any = True
else:
for key, mapped_key in MAPPING.items():
_a : List[Any] = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
_a : Union[str, Any] = True
if "*" in mapped_key:
_a : int = name.split(__a )[0].split('.' )[-2]
_a : Union[str, Any] = mapped_key.replace('*' , __a )
if "pos_bias_u" in name:
_a : Tuple = None
elif "pos_bias_v" in name:
_a : Optional[Any] = None
elif "weight_g" in name:
_a : Tuple = 'weight_g'
elif "weight_v" in name:
_a : List[str] = 'weight_v'
elif "bias" in name:
_a : List[str] = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_a : Union[str, Any] = 'weight'
elif "running_mean" in name:
_a : List[str] = 'running_mean'
elif "inv_freq" in name:
_a : List[str] = 'inv_freq'
elif "running_var" in name:
_a : Optional[Any] = 'running_var'
elif "num_batches_tracked" in name:
_a : List[Any] = 'num_batches_tracked'
else:
_a : Any = None
set_recursively(__a , __a , __a , __a , __a )
continue
if not is_used:
unused_weights.append(__a )
logger.warning(f"""Unused weights: {unused_weights}""" )
def UpperCAmelCase_ (__a : str , __a : Optional[Any] , __a : Tuple , __a : Any , __a : Optional[int] ):
"""simple docstring"""
_a : int = full_name.split('conv_layers.' )[-1]
_a : Union[str, Any] = name.split('.' )
_a : List[str] = int(items[0] )
_a : str = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
_a : Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
_a : int = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
_a : Optional[Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
_a : Tuple = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(__a )
@torch.no_grad()
def UpperCAmelCase_ (__a : Tuple , __a : Optional[Any] , __a : List[Any]=None , __a : int=None , __a : Optional[int]=True ):
"""simple docstring"""
if config_path is not None:
_a : Union[str, Any] = WavaVecaConformerConfig.from_pretrained(__a , hidden_act='swish' )
else:
_a : Dict = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
_a : List[Any] = 'rotary'
if is_finetuned:
if dict_path:
_a : Union[str, Any] = Dictionary.load(__a )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_a : Optional[int] = target_dict.pad_index
_a : Optional[Any] = target_dict.bos_index
_a : List[str] = target_dict.eos_index
_a : Tuple = len(target_dict.symbols )
_a : Union[str, Any] = os.path.join(__a , 'vocab.json' )
if not os.path.isdir(__a ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__a ) )
return
os.makedirs(__a , exist_ok=__a )
_a : str = target_dict.indices
# fairseq has the <pad> and <s> switched
_a : Optional[int] = 0
_a : Dict = 1
with open(__a , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(__a , __a )
_a : Optional[int] = WavaVecaCTCTokenizer(
__a , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=__a , )
_a : Tuple = True if config.feat_extract_norm == 'layer' else False
_a : List[Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0 , do_normalize=__a , return_attention_mask=__a , )
_a : Tuple = WavaVecaProcessor(feature_extractor=__a , tokenizer=__a )
processor.save_pretrained(__a )
_a : Tuple = WavaVecaConformerForCTC(__a )
else:
_a : int = WavaVecaConformerForPreTraining(__a )
if is_finetuned:
_a, _a, _a : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
_a : Optional[int] = argparse.Namespace(task='audio_pretraining' )
_a : Dict = fairseq.tasks.setup_task(__a )
_a, _a, _a : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__a )
_a : str = model[0].eval()
recursively_load_weights(__a , __a , not is_finetuned )
hf_wavavec.save_pretrained(__a )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
__lowerCAmelCase = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 229
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : str = StableDiffusionLDMaDPipeline
__UpperCAmelCase : Optional[int] = TEXT_TO_IMAGE_PARAMS
__UpperCAmelCase : str = TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCAmelCase : Any = TEXT_TO_IMAGE_IMAGE_PARAMS
def __lowercase ( self : str ):
'''simple docstring'''
torch.manual_seed(0 )
_a : Optional[int] = 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 ,)
_a : Optional[int] = DDIMScheduler(
beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule='scaled_linear' ,clip_sample=_a ,set_alpha_to_one=_a ,)
torch.manual_seed(0 )
_a : Tuple = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=6 ,out_channels=6 ,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,latent_channels=4 ,)
torch.manual_seed(0 )
_a : List[str] = 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 ,)
_a : List[str] = CLIPTextModel(_a )
_a : List[str] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_a : List[Any] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __lowercase ( self : List[str] ,_a : Optional[Any] ,_a : Tuple=0 ):
'''simple docstring'''
if str(_a ).startswith('mps' ):
_a : Optional[Any] = torch.manual_seed(_a )
else:
_a : Dict = torch.Generator(device=_a ).manual_seed(_a )
_a : Tuple = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self : List[str] ):
'''simple docstring'''
_a : Optional[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
_a : Optional[Any] = self.get_dummy_components()
_a : int = StableDiffusionLDMaDPipeline(**_a )
_a : str = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Optional[int] = self.get_dummy_inputs(_a )
_a : Union[str, Any] = ldmad_pipe(**_a )
_a, _a : str = output.rgb, output.depth
_a : int = rgb[0, -3:, -3:, -1]
_a : List[str] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
_a : Dict = np.array(
[0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262] )
_a : Union[str, Any] = np.array([103.4_6727, 85.81_2004, 87.84_9236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2
def __lowercase ( self : int ):
'''simple docstring'''
_a : Any = self.get_dummy_components()
_a : int = StableDiffusionLDMaDPipeline(**_a )
_a : List[Any] = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Any = self.get_dummy_inputs(_a )
_a : List[str] = 3 * [inputs['prompt']]
# forward
_a : int = ldmad_pipe(**_a )
_a, _a : Union[str, Any] = output.rgb, output.depth
_a : Optional[Any] = rgb_slice_a[0, -3:, -3:, -1]
_a : Tuple = depth_slice_a[0, -3:, -1]
_a : Tuple = self.get_dummy_inputs(_a )
_a : Tuple = 3 * [inputs.pop('prompt' )]
_a : List[Any] = ldmad_pipe.tokenizer(
_a ,padding='max_length' ,max_length=ldmad_pipe.tokenizer.model_max_length ,truncation=_a ,return_tensors='pt' ,)
_a : Union[str, Any] = text_inputs['input_ids'].to(_a )
_a : List[str] = ldmad_pipe.text_encoder(_a )[0]
_a : int = prompt_embeds
# forward
_a : str = ldmad_pipe(**_a )
_a, _a : str = output.rgb, output.depth
_a : Tuple = rgb_slice_a[0, -3:, -3:, -1]
_a : Optional[Any] = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
_a : Tuple = 'cpu' # ensure determinism for the device-dependent torch.Generator
_a : Dict = self.get_dummy_components()
_a : Dict = PNDMScheduler(skip_prk_steps=_a )
_a : str = StableDiffusionLDMaDPipeline(**_a )
_a : str = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = self.get_dummy_inputs(_a )
_a : List[Any] = 'french fries'
_a : Dict = ldmad_pipe(**_a ,negative_prompt=_a )
_a, _a : Any = output.rgb, output.depth
_a : Tuple = rgb[0, -3:, -3:, -1]
_a : List[Any] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
_a : str = np.array(
[0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217] )
_a : Optional[int] = np.array([107.8_4738, 84.6_2802, 89.96_2135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2
@slow
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : Tuple ,_a : Union[str, Any] ,_a : int="cpu" ,_a : Union[str, Any]=torch.floataa ,_a : str=0 ):
'''simple docstring'''
_a : str = torch.Generator(device=_a ).manual_seed(_a )
_a : List[str] = np.random.RandomState(_a ).standard_normal((1, 4, 64, 64) )
_a : int = torch.from_numpy(_a ).to(device=_a ,dtype=_a )
_a : Union[str, Any] = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
_a : Any = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' )
_a : Optional[int] = ldmad_pipe.to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : Tuple = self.get_inputs(_a )
_a : Tuple = ldmad_pipe(**_a )
_a, _a : Dict = output.rgb, output.depth
_a : Union[str, Any] = rgb[0, -3:, -3:, -1].flatten()
_a : Union[str, Any] = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
_a : int = np.array(
[0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706] )
_a : Optional[Any] = np.array(
[0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3
@nightly
@require_torch_gpu
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self : Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : str ,_a : Union[str, Any] ,_a : Any="cpu" ,_a : Union[str, Any]=torch.floataa ,_a : Tuple=0 ):
'''simple docstring'''
_a : Optional[Any] = torch.Generator(device=_a ).manual_seed(_a )
_a : Optional[Any] = np.random.RandomState(_a ).standard_normal((1, 4, 64, 64) )
_a : Any = torch.from_numpy(_a ).to(device=_a ,dtype=_a )
_a : Dict = {
'prompt': 'a photograph of an astronaut riding a horse',
'latents': latents,
'generator': generator,
'num_inference_steps': 50,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def __lowercase ( self : int ):
'''simple docstring'''
_a : str = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d' ).to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : List[Any] = self.get_inputs(_a )
_a : Union[str, Any] = ldmad_pipe(**_a )
_a, _a : Optional[Any] = output.rgb, output.depth
_a : Dict = 0.49_5586
_a : Optional[Any] = 0.3379_5515
_a : str = 112.4_8518
_a : Optional[Any] = 98.48_9746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
assert np.abs(expected_depth_std - depth.std() ) < 1E-3
def __lowercase ( self : int ):
'''simple docstring'''
_a : List[str] = StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c' ).to(_a )
ldmad_pipe.set_progress_bar_config(disable=_a )
_a : int = self.get_inputs(_a )
_a : Tuple = ldmad_pipe(**_a )
_a, _a : Optional[int] = output.rgb, output.depth
_a : Union[str, Any] = 0.419_4127
_a : Tuple = 0.3537_5586
_a : Tuple = 0.563_8502
_a : Tuple = 0.3468_6103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
assert np.abs(expected_depth_std - depth.std() ) < 1E-3
| 229
| 1
|
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
snake_case = {
"return_dict": False,
"output_hidden_states": True,
"output_attentions": True,
"torchscript": True,
"torch_dtype": "float16",
"use_bfloat16": True,
"tf_legacy_loss": True,
"pruned_heads": {"a": 1},
"tie_word_embeddings": False,
"is_decoder": True,
"cross_attention_hidden_size": 128,
"add_cross_attention": True,
"tie_encoder_decoder": True,
"max_length": 50,
"min_length": 3,
"do_sample": True,
"early_stopping": True,
"num_beams": 3,
"num_beam_groups": 3,
"diversity_penalty": 0.5,
"temperature": 2.0,
"top_k": 10,
"top_p": 0.7,
"typical_p": 0.2,
"repetition_penalty": 0.8,
"length_penalty": 0.8,
"no_repeat_ngram_size": 5,
"encoder_no_repeat_ngram_size": 5,
"bad_words_ids": [1, 2, 3],
"num_return_sequences": 3,
"chunk_size_feed_forward": 5,
"output_scores": True,
"return_dict_in_generate": True,
"forced_bos_token_id": 2,
"forced_eos_token_id": 3,
"remove_invalid_values": True,
"architectures": ["BertModel"],
"finetuning_task": "translation",
"id2label": {0: "label"},
"label2id": {"label": "0"},
"tokenizer_class": "BertTokenizerFast",
"prefix": "prefix",
"bos_token_id": 6,
"pad_token_id": 7,
"eos_token_id": 8,
"sep_token_id": 9,
"decoder_start_token_id": 10,
"exponential_decay_length_penalty": (5, 1.01),
"suppress_tokens": [0, 1],
"begin_suppress_tokens": 2,
"task_specific_params": {"translation": "some_params"},
"problem_type": "regression",
}
@is_staging_test
class __A ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls ):
_lowerCAmelCase : Optional[Any] = TOKEN
HfFolder.save_token(_snake_case )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls ):
try:
delete_repo(token=cls._token , repo_id="test-config" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-config-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-config" )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Optional[Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("test-config" , use_auth_token=self._token )
_lowerCAmelCase : List[Any] = BertConfig.from_pretrained(F"""{USER}/test-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-config" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(_snake_case , repo_id="test-config" , push_to_hub=_snake_case , use_auth_token=self._token )
_lowerCAmelCase : Union[str, Any] = BertConfig.from_pretrained(F"""{USER}/test-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("valid_org/test-config-org" , use_auth_token=self._token )
_lowerCAmelCase : Tuple = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-config-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
_snake_case , repo_id="valid_org/test-config-org" , push_to_hub=_snake_case , use_auth_token=self._token )
_lowerCAmelCase : Any = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(_snake_case , getattr(_snake_case , _snake_case ) )
def SCREAMING_SNAKE_CASE__ ( self ):
CustomConfig.register_for_auto_class()
_lowerCAmelCase : Optional[Any] = CustomConfig(attribute=42 )
config.push_to_hub("test-dynamic-config" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"AutoConfig": "custom_configuration.CustomConfig"} )
_lowerCAmelCase : Any = AutoConfig.from_pretrained(F"""{USER}/test-dynamic-config""" , trust_remote_code=_snake_case )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , "CustomConfig" )
self.assertEqual(new_config.attribute , 42 )
class __A ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Union[str, Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
_lowerCAmelCase : Tuple = c.n_embd + 1 # int
_lowerCAmelCase : Dict = c.resid_pdrop + 1.0 # float
_lowerCAmelCase : Dict = not c.scale_attn_weights # bool
_lowerCAmelCase : int = c.summary_type + "foo" # str
c.update_from_string(
F"""n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}""" )
self.assertEqual(_snake_case , c.n_embd , "mismatch for key: n_embd" )
self.assertEqual(_snake_case , c.resid_pdrop , "mismatch for key: resid_pdrop" )
self.assertEqual(_snake_case , c.scale_attn_weights , "mismatch for key: scale_attn_weights" )
self.assertEqual(_snake_case , c.summary_type , "mismatch for key: summary_type" )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : Dict = PretrainedConfig()
_lowerCAmelCase : int = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
_snake_case , ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"] )
_lowerCAmelCase : List[str] = [key for key, value in config_common_kwargs.items() if value == getattr(_snake_case , _snake_case )]
if len(_snake_case ) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
F""" {', '.join(_snake_case )}.""" )
def SCREAMING_SNAKE_CASE__ ( self ):
with self.assertRaises(_snake_case ):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : Optional[Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" )
_lowerCAmelCase : Optional[Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" , subfolder="bert" )
self.assertIsNotNone(_snake_case )
def SCREAMING_SNAKE_CASE__ ( self ):
# A mock response for an HTTP head request to emulate server down
_lowerCAmelCase : Tuple = mock.Mock()
_lowerCAmelCase : Any = 500
_lowerCAmelCase : Any = {}
_lowerCAmelCase : Any = HTTPError
_lowerCAmelCase : List[str] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Optional[int] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=_snake_case ) as mock_head:
_lowerCAmelCase : Optional[Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
def SCREAMING_SNAKE_CASE__ ( self ):
# This test is for deprecated behavior and can be removed in v5
_lowerCAmelCase : Any = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json" )
def SCREAMING_SNAKE_CASE__ ( self ):
_lowerCAmelCase : List[str] = AutoConfig.from_pretrained("bert-base-cased" )
_lowerCAmelCase : Tuple = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(_snake_case )
_lowerCAmelCase : Optional[int] = 2
json.dump(configuration.to_dict() , open(os.path.join(_snake_case , "config.4.0.0.json" ) , "w" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
_lowerCAmelCase : int = AutoConfig.from_pretrained(_snake_case )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
_lowerCAmelCase : List[Any] = ["config.42.0.0.json"]
_lowerCAmelCase : str = 768
configuration.save_pretrained(_snake_case )
shutil.move(os.path.join(_snake_case , "config.4.0.0.json" ) , os.path.join(_snake_case , "config.42.0.0.json" ) )
_lowerCAmelCase : str = AutoConfig.from_pretrained(_snake_case )
self.assertEqual(new_configuration.hidden_size , 768 )
def SCREAMING_SNAKE_CASE__ ( self ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
_lowerCAmelCase : List[Any] = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
_lowerCAmelCase : str = "v4.0.0"
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = new_transformers.models.auto.AutoConfig.from_pretrained(
_snake_case , return_unused_kwargs=_snake_case )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(_snake_case , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
_lowerCAmelCase : Union[str, Any] = "v3.0.0"
_lowerCAmelCase : Any = old_transformers.models.auto.AutoConfig.from_pretrained(_snake_case )
self.assertEqual(old_configuration.hidden_size , 768 )
| 587
|
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
snake_case = logging.get_logger(__name__)
snake_case = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
# See all LED models at https://huggingface.co/models?filter=LED
snake_case = {
"vocab_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
},
"merges_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
},
"tokenizer_file": {
"allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
},
}
snake_case = {
"allenai/led-base-16384": 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def UpperCamelCase_ ( ):
"""simple docstring"""
_lowerCAmelCase : Any = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
_lowerCAmelCase : Dict = bs[:]
_lowerCAmelCase : str = 0
for b in range(2**8 ):
if b not in bs:
bs.append(lowerCAmelCase__ )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase : int = [chr(lowerCAmelCase__ ) for n in cs]
return dict(zip(lowerCAmelCase__ , lowerCAmelCase__ ) )
def UpperCamelCase_ ( lowerCAmelCase__ ):
"""simple docstring"""
_lowerCAmelCase : Optional[Any] = set()
_lowerCAmelCase : str = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase : Union[str, Any] = char
return pairs
class __A ( snake_case__ ):
'''simple docstring'''
a_ = VOCAB_FILES_NAMES
a_ = PRETRAINED_VOCAB_FILES_MAP
a_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a_ = ['''input_ids''', '''attention_mask''']
def __init__( self , _snake_case , _snake_case , _snake_case="replace" , _snake_case="<s>" , _snake_case="</s>" , _snake_case="</s>" , _snake_case="<s>" , _snake_case="<unk>" , _snake_case="<pad>" , _snake_case="<mask>" , _snake_case=False , **_snake_case , ):
_lowerCAmelCase : Optional[Any] = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else bos_token
_lowerCAmelCase : Union[str, Any] = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else eos_token
_lowerCAmelCase : Optional[int] = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else sep_token
_lowerCAmelCase : str = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else cls_token
_lowerCAmelCase : Optional[Any] = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else unk_token
_lowerCAmelCase : Union[str, Any] = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase : str = AddedToken(_snake_case , lstrip=_snake_case , rstrip=_snake_case ) if isinstance(_snake_case , _snake_case ) else mask_token
super().__init__(
errors=_snake_case , bos_token=_snake_case , eos_token=_snake_case , unk_token=_snake_case , sep_token=_snake_case , cls_token=_snake_case , pad_token=_snake_case , mask_token=_snake_case , add_prefix_space=_snake_case , **_snake_case , )
with open(_snake_case , encoding="utf-8" ) as vocab_handle:
_lowerCAmelCase : Tuple = json.load(_snake_case )
_lowerCAmelCase : str = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase : Optional[int] = errors # how to handle errors in decoding
_lowerCAmelCase : Any = bytes_to_unicode()
_lowerCAmelCase : Dict = {v: k for k, v in self.byte_encoder.items()}
with open(_snake_case , encoding="utf-8" ) as merges_handle:
_lowerCAmelCase : List[str] = merges_handle.read().split("\n" )[1:-1]
_lowerCAmelCase : Optional[Any] = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCAmelCase : List[Any] = dict(zip(_snake_case , range(len(_snake_case ) ) ) )
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : List[Any] = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase : Any = re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
def SCREAMING_SNAKE_CASE__ ( self ):
return len(self.encoder )
def SCREAMING_SNAKE_CASE__ ( self ):
return dict(self.encoder , **self.added_tokens_encoder )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
if token in self.cache:
return self.cache[token]
_lowerCAmelCase : Optional[int] = tuple(_snake_case )
_lowerCAmelCase : Union[str, Any] = get_pairs(_snake_case )
if not pairs:
return token
while True:
_lowerCAmelCase : Optional[Any] = min(_snake_case , key=lambda _snake_case : self.bpe_ranks.get(_snake_case , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase , _lowerCAmelCase : Tuple = bigram
_lowerCAmelCase : int = []
_lowerCAmelCase : List[str] = 0
while i < len(_snake_case ):
try:
_lowerCAmelCase : Union[str, Any] = word.index(_snake_case , _snake_case )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCAmelCase : Optional[Any] = j
if word[i] == first and i < len(_snake_case ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCAmelCase : Optional[Any] = tuple(_snake_case )
_lowerCAmelCase : str = new_word
if len(_snake_case ) == 1:
break
else:
_lowerCAmelCase : List[str] = get_pairs(_snake_case )
_lowerCAmelCase : Tuple = " ".join(_snake_case )
_lowerCAmelCase : Optional[int] = word
return word
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : List[str] = []
for token in re.findall(self.pat , _snake_case ):
_lowerCAmelCase : List[str] = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_snake_case ).split(" " ) )
return bpe_tokens
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
return self.encoder.get(_snake_case , self.encoder.get(self.unk_token ) )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
return self.decoder.get(_snake_case )
def SCREAMING_SNAKE_CASE__ ( self , _snake_case ):
_lowerCAmelCase : str = "".join(_snake_case )
_lowerCAmelCase : int = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None ):
if not os.path.isdir(_snake_case ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCAmelCase : List[Any] = os.path.join(
_snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
_lowerCAmelCase : Any = os.path.join(
_snake_case , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(_snake_case , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_snake_case , ensure_ascii=_snake_case ) + "\n" )
_lowerCAmelCase : Any = 0
with open(_snake_case , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _snake_case : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
_lowerCAmelCase : Union[str, Any] = token_index
writer.write(" ".join(_snake_case ) + "\n" )
index += 1
return vocab_file, merge_file
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase : Union[str, Any] = [self.cls_token_id]
_lowerCAmelCase : Any = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None , _snake_case = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_snake_case , token_ids_a=_snake_case , already_has_special_tokens=_snake_case )
if token_ids_a is None:
return [1] + ([0] * len(_snake_case )) + [1]
return [1] + ([0] * len(_snake_case )) + [1, 1] + ([0] * len(_snake_case )) + [1]
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None ):
_lowerCAmelCase : str = [self.sep_token_id]
_lowerCAmelCase : Union[str, 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]
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case=False , **_snake_case ):
_lowerCAmelCase : Tuple = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(_snake_case ) > 0 and not text[0].isspace()):
_lowerCAmelCase : List[str] = " " + text
return (text, kwargs)
def SCREAMING_SNAKE_CASE__ ( self , _snake_case , _snake_case = None , _snake_case = PaddingStrategy.DO_NOT_PAD , _snake_case = None , _snake_case = None , ):
_lowerCAmelCase : Union[str, Any] = super()._pad(
encoded_inputs=_snake_case , max_length=_snake_case , padding_strategy=_snake_case , pad_to_multiple_of=_snake_case , return_attention_mask=_snake_case , )
# Load from model defaults
if return_attention_mask is None:
_lowerCAmelCase : Optional[int] = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
_lowerCAmelCase : int = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
_lowerCAmelCase : Optional[Any] = len(encoded_inputs["global_attention_mask"] ) != len(_snake_case )
if needs_to_be_padded:
_lowerCAmelCase : Any = len(_snake_case ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
_lowerCAmelCase : Optional[int] = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
_lowerCAmelCase : List[str] = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 587
| 1
|
from __future__ import annotations
def _A ( lowerCAmelCase_ : list , lowerCAmelCase_ : int | None = None , lowerCAmelCase_ : int | None = None ):
"""simple docstring"""
if start is None:
lowerCAmelCase__ = 0
if end is None:
lowerCAmelCase__ = len(lowerCAmelCase_ ) - 1
if start >= end:
return
lowerCAmelCase__ = (start + end) // 2
slowsort(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
slowsort(lowerCAmelCase_ , mid + 1 , lowerCAmelCase_ )
if sequence[end] < sequence[mid]:
lowerCAmelCase__ , lowerCAmelCase__ = sequence[mid], sequence[end]
slowsort(lowerCAmelCase_ , lowerCAmelCase_ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 61
|
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class __lowerCamelCase ( UpperCamelCase__ , unittest.TestCase ):
"""simple docstring"""
snake_case__ = VideoToVideoSDPipeline
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"} ) - {"image", "width", "height"}
snake_case__ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"} ) - {"image"}
snake_case__ = PipelineTesterMixin.required_optional_params - {"latents"}
snake_case__ = False
# No `output_type`.
snake_case__ = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def a ( self : int ) -> Optional[int]:
torch.manual_seed(0 )
lowerCAmelCase__ = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D") , up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D") , cross_attention_dim=32 , attention_head_dim=4 , )
lowerCAmelCase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=SCREAMING_SNAKE_CASE__ , set_alpha_to_one=SCREAMING_SNAKE_CASE__ , )
torch.manual_seed(0 )
lowerCAmelCase__ = 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 , sample_size=128 , )
torch.manual_seed(0 )
lowerCAmelCase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="gelu" , projection_dim=512 , )
lowerCAmelCase__ = CLIPTextModel(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
lowerCAmelCase__ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
}
return components
def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict=0 ) -> Tuple:
# 3 frames
lowerCAmelCase__ = floats_tensor((1, 3, 3, 32, 32) , rng=random.Random(SCREAMING_SNAKE_CASE__ ) ).to(SCREAMING_SNAKE_CASE__ )
if str(SCREAMING_SNAKE_CASE__ ).startswith("mps" ):
lowerCAmelCase__ = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
lowerCAmelCase__ = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = {
"prompt": "A painting of a squirrel eating a burger",
"video": video,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "pt",
}
return inputs
def a ( self : Union[str, Any] ) -> str:
lowerCAmelCase__ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCAmelCase__ = self.get_dummy_components()
lowerCAmelCase__ = VideoToVideoSDPipeline(**SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = sd_pipe.to(SCREAMING_SNAKE_CASE__ )
sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = "np"
lowerCAmelCase__ = sd_pipe(**SCREAMING_SNAKE_CASE__ ).frames
lowerCAmelCase__ = frames[0][-3:, -3:, -1]
assert frames[0].shape == (32, 32, 3)
lowerCAmelCase__ = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def a ( self : List[Any] ) -> str:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE__ , expected_max_diff=5e-3 )
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def a ( self : List[Any] ) -> str:
pass
@unittest.skip(reason="Batching needs to be properly figured out first for this pipeline." )
def a ( self : int ) -> Optional[Any]:
pass
@unittest.skip(reason="`num_images_per_prompt` argument is not supported for this pipeline." )
def a ( self : List[str] ) -> Optional[int]:
pass
def a ( self : Optional[Any] ) -> Tuple:
return super().test_progress_bar()
@slow
@skip_mps
class __lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def a ( self : str ) -> int:
lowerCAmelCase__ = VideoToVideoSDPipeline.from_pretrained("cerspense/zeroscope_v2_XL" , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
lowerCAmelCase__ = torch.Generator(device="cpu" ).manual_seed(0 )
lowerCAmelCase__ = torch.randn((1, 10, 3, 1_024, 576) , generator=SCREAMING_SNAKE_CASE__ )
lowerCAmelCase__ = video.to("cuda" )
lowerCAmelCase__ = "Spiderman is surfing"
lowerCAmelCase__ = pipe(SCREAMING_SNAKE_CASE__ , video=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=3 , output_type="pt" ).frames
lowerCAmelCase__ = np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1e-2
| 61
| 1
|
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def _lowerCAmelCase( UpperCAmelCase_ : Tuple ) -> Tuple:
return {key.lstrip("""-""" ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def _lowerCAmelCase( ) -> int:
lowerCAmelCase__ = ArgumentParser(
"""HuggingFace Datasets CLI tool""" , usage="""datasets-cli <command> [<args>]""" , allow_abbrev=UpperCAmelCase_ )
lowerCAmelCase__ = parser.add_subparsers(help="""datasets-cli command helpers""" )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(UpperCAmelCase_ )
EnvironmentCommand.register_subcommand(UpperCAmelCase_ )
TestCommand.register_subcommand(UpperCAmelCase_ )
RunBeamCommand.register_subcommand(UpperCAmelCase_ )
DummyDataCommand.register_subcommand(UpperCAmelCase_ )
# Parse args
lowerCAmelCase__ ,lowerCAmelCase__ = parser.parse_known_args()
if not hasattr(UpperCAmelCase_ , """func""" ):
parser.print_help()
exit(1 )
lowerCAmelCase__ = parse_unknown_args(UpperCAmelCase_ )
# Run
lowerCAmelCase__ = args.func(UpperCAmelCase_ , **UpperCAmelCase_ )
service.run()
if __name__ == "__main__":
main()
| 721
|
'''simple docstring'''
# Copyright 2023 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = ["""VivitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"""VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VivitModel""",
"""VivitPreTrainedModel""",
"""VivitForVideoClassification""",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 211
| 0
|
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 UpperCamelCase ( lowercase_ , lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
assert isinstance(lowercase_ , lowercase_ )
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 UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
'''simple docstring'''
lowercase__ : Tuple = tmp_path / """cache"""
lowercase__ : Union[str, Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase__ : Optional[int] = SqlDatasetReader(
"""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_sql_dataset(lowercase_ , lowercase_ )
@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 UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
lowercase__ : Optional[int] = tmp_path / """cache"""
lowercase__ : List[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
lowercase__ : Optional[int] = features.copy() if features else default_expected_features
lowercase__ : str = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase__ : Tuple = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_sql_dataset(lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ ) -> int:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(lowercase_ ) ) as con:
lowercase__ : str = con.cursor()
cur.execute("""SELECT * FROM dataset""" )
for row in cur:
yield row
@require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
lowercase__ : str = tmp_path / """cache"""
lowercase__ : Optional[Any] = os.path.join(lowercase_ , """tmp.sql""" )
lowercase__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ ).read()
SqlDatasetWriter(lowercase_ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=1 ).write()
lowercase__ : Optional[Any] = iter_sql_file(lowercase_ )
lowercase__ : int = iter_sql_file(lowercase_ )
for rowa, rowa in zip(lowercase_ , lowercase_ ):
assert rowa == rowa
@require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
lowercase__ : List[str] = tmp_path / """cache"""
lowercase__ : str = os.path.join(lowercase_ , """tmp.sql""" )
lowercase__ : List[str] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ ).read()
SqlDatasetWriter(lowercase_ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=2 ).write()
lowercase__ : List[Any] = iter_sql_file(lowercase_ )
lowercase__ : Tuple = iter_sql_file(lowercase_ )
for rowa, rowa in zip(lowercase_ , lowercase_ ):
assert rowa == rowa
@require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
lowercase__ : Tuple = tmp_path / """cache"""
lowercase__ : Union[str, Any] = os.path.join(lowercase_ , """tmp.sql""" )
lowercase__ : Dict = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ ).read()
with pytest.raises(lowercase_ ):
SqlDatasetWriter(lowercase_ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=0 ).write()
| 12
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
SCREAMING_SNAKE_CASE__ : Optional[int] = {
'b0': {
'hidden_dim': 1_280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1_280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1_408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1_536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1_792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2_048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2_304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2_560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def a__ ( snake_case__ : Optional[Any] ):
_UpperCAmelCase : Optional[int] = EfficientNetConfig()
_UpperCAmelCase : Tuple = CONFIG_MAP[model_name]["""hidden_dim"""]
_UpperCAmelCase : Any = CONFIG_MAP[model_name]["""width_coef"""]
_UpperCAmelCase : Optional[Any] = CONFIG_MAP[model_name]["""depth_coef"""]
_UpperCAmelCase : int = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase : List[str] = CONFIG_MAP[model_name]["""dropout_rate"""]
_UpperCAmelCase : Optional[int] = CONFIG_MAP[model_name]["""dw_padding"""]
_UpperCAmelCase : int = """huggingface/label-files"""
_UpperCAmelCase : Dict = """imagenet-1k-id2label.json"""
_UpperCAmelCase : Optional[Any] = 1000
_UpperCAmelCase : List[str] = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type="""dataset""" ) , """r""" ) )
_UpperCAmelCase : Union[str, Any] = {int(snake_case__ ): v for k, v in idalabel.items()}
_UpperCAmelCase : Any = idalabel
_UpperCAmelCase : List[str] = {v: k for k, v in idalabel.items()}
return config
def a__ ( ):
_UpperCAmelCase : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_UpperCAmelCase : str = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
return im
def a__ ( snake_case__ : Tuple ):
_UpperCAmelCase : List[str] = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase : str = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163] , do_center_crop=snake_case__ , )
return preprocessor
def a__ ( snake_case__ : Optional[Any] ):
_UpperCAmelCase : Optional[int] = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
_UpperCAmelCase : Dict = sorted(set(snake_case__ ) )
_UpperCAmelCase : int = len(snake_case__ )
_UpperCAmelCase : Optional[Any] = {b: str(snake_case__ ) for b, i in zip(snake_case__ , range(snake_case__ ) )}
_UpperCAmelCase : Union[str, Any] = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
_UpperCAmelCase : List[Any] = block_name_mapping[b]
rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
_UpperCAmelCase : Union[str, Any] = {}
for item in rename_keys:
if item[0] in original_param_names:
_UpperCAmelCase : Tuple = """efficientnet.""" + item[1]
_UpperCAmelCase : List[Any] = """classifier.weight"""
_UpperCAmelCase : List[Any] = """classifier.bias"""
return key_mapping
def a__ ( snake_case__ : Optional[int] , snake_case__ : int , snake_case__ : List[str] ):
for key, value in tf_params.items():
if "normalization" in key:
continue
_UpperCAmelCase : Optional[Any] = key_mapping[key]
if "_conv" in key and "kernel" in key:
_UpperCAmelCase : Optional[Any] = torch.from_numpy(snake_case__ ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
_UpperCAmelCase : Dict = torch.from_numpy(snake_case__ ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
_UpperCAmelCase : Any = torch.from_numpy(np.transpose(snake_case__ ) )
else:
_UpperCAmelCase : Optional[Any] = torch.from_numpy(snake_case__ )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(snake_case__ )
@torch.no_grad()
def a__ ( snake_case__ : str , snake_case__ : Dict , snake_case__ : int , snake_case__ : str ):
_UpperCAmelCase : List[str] = model_classes[model_name](
include_top=snake_case__ , weights="""imagenet""" , input_tensor=snake_case__ , input_shape=snake_case__ , pooling=snake_case__ , classes=1000 , classifier_activation="""softmax""" , )
_UpperCAmelCase : Any = original_model.trainable_variables
_UpperCAmelCase : List[Any] = original_model.non_trainable_variables
_UpperCAmelCase : List[Any] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_UpperCAmelCase : List[str] = param.numpy()
_UpperCAmelCase : List[Any] = list(tf_params.keys() )
# Load HuggingFace model
_UpperCAmelCase : Any = get_efficientnet_config(snake_case__ )
_UpperCAmelCase : List[str] = EfficientNetForImageClassification(snake_case__ ).eval()
_UpperCAmelCase : Any = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
_UpperCAmelCase : Optional[Any] = rename_keys(snake_case__ )
replace_params(snake_case__ , snake_case__ , snake_case__ )
# Initialize preprocessor and preprocess input image
_UpperCAmelCase : Dict = convert_image_processor(snake_case__ )
_UpperCAmelCase : List[Any] = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
_UpperCAmelCase : List[str] = hf_model(**snake_case__ )
_UpperCAmelCase : Any = outputs.logits.detach().numpy()
# Original model inference
_UpperCAmelCase : Dict = False
_UpperCAmelCase : List[Any] = CONFIG_MAP[model_name]["""image_size"""]
_UpperCAmelCase : Any = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
_UpperCAmelCase : Optional[Any] = image.img_to_array(snake_case__ )
_UpperCAmelCase : Any = np.expand_dims(snake_case__ , axis=0 )
_UpperCAmelCase : List[Any] = original_model.predict(snake_case__ )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(snake_case__ , snake_case__ , atol=1e-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(snake_case__ ):
os.mkdir(snake_case__ )
# Save converted model and image processor
hf_model.save_pretrained(snake_case__ )
preprocessor.save_pretrained(snake_case__ )
if push_to_hub:
# Push model and image processor to hub
print(f'''Pushing converted {model_name} to the hub...''' )
_UpperCAmelCase : int = f'''efficientnet-{model_name}'''
preprocessor.push_to_hub(snake_case__ )
hf_model.push_to_hub(snake_case__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
SCREAMING_SNAKE_CASE__ : Optional[Any] = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 643
| 0
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
A = logging.get_logger(__name__)
def lowerCamelCase ( UpperCamelCase : str , UpperCamelCase : Any=False , UpperCamelCase : Union[str, Any]=False , UpperCamelCase : List[Any]=False ) -> List[str]:
_lowerCamelCase = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""transformer.blocks.{i}.norm1.weight""", F"""vilt.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.norm1.bias""", F"""vilt.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""transformer.blocks.{i}.attn.proj.weight""", F"""vilt.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(F"""transformer.blocks.{i}.attn.proj.bias""", F"""vilt.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""transformer.blocks.{i}.norm2.weight""", F"""vilt.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.norm2.bias""", F"""vilt.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append(
(F"""transformer.blocks.{i}.mlp.fc1.weight""", F"""vilt.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.mlp.fc1.bias""", F"""vilt.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.weight""", F"""vilt.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""transformer.blocks.{i}.mlp.fc2.bias""", F"""vilt.encoder.layer.{i}.output.dense.bias""") )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def lowerCamelCase ( UpperCamelCase : str , UpperCamelCase : str ) -> Tuple:
for i in range(config.num_hidden_layers ):
_lowerCamelCase = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase = state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.weight""" )
_lowerCamelCase = state_dict.pop(F"""transformer.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase = in_proj_bias[: config.hidden_size]
_lowerCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase = in_proj_bias[-config.hidden_size :]
def lowerCamelCase ( UpperCamelCase : Tuple ) -> List[Any]:
_lowerCamelCase = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase , UpperCamelCase )
def lowerCamelCase ( UpperCamelCase : Tuple , UpperCamelCase : Optional[Any] , UpperCamelCase : str ) -> Any:
_lowerCamelCase = dct.pop(UpperCamelCase )
_lowerCamelCase = val
@torch.no_grad()
def lowerCamelCase ( UpperCamelCase : int , UpperCamelCase : int ) -> Optional[int]:
_lowerCamelCase = ViltConfig(image_size=3_84 , patch_size=32 , tie_word_embeddings=UpperCamelCase )
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
if "vqa" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = 31_29
_lowerCamelCase = 'huggingface/label-files'
_lowerCamelCase = 'vqa2-id2label.json'
_lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase , UpperCamelCase , repo_type='dataset' ) , 'r' ) )
_lowerCamelCase = {int(UpperCamelCase ): v for k, v in idalabel.items()}
_lowerCamelCase = idalabel
_lowerCamelCase = {v: k for k, v in idalabel.items()}
_lowerCamelCase = ViltForQuestionAnswering(UpperCamelCase )
elif "nlvr" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = 2
_lowerCamelCase = {0: 'False', 1: 'True'}
_lowerCamelCase = {v: k for k, v in config.idalabel.items()}
_lowerCamelCase = 3
_lowerCamelCase = ViltForImagesAndTextClassification(UpperCamelCase )
elif "irtr" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = ViltForImageAndTextRetrieval(UpperCamelCase )
elif "mlm_itm" in checkpoint_url:
_lowerCamelCase = True
_lowerCamelCase = ViltForMaskedLM(UpperCamelCase )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
_lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase , map_location='cpu' )['state_dict']
_lowerCamelCase = create_rename_keys(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
for src, dest in rename_keys:
rename_key(UpperCamelCase , UpperCamelCase , UpperCamelCase )
read_in_q_k_v(UpperCamelCase , UpperCamelCase )
if mlm_model or irtr_model:
_lowerCamelCase = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase , UpperCamelCase )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
_lowerCamelCase , _lowerCamelCase = model.load_state_dict(UpperCamelCase , strict=UpperCamelCase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(UpperCamelCase )
# Define processor
_lowerCamelCase = ViltImageProcessor(size=3_84 )
_lowerCamelCase = BertTokenizer.from_pretrained('bert-base-uncased' )
_lowerCamelCase = ViltProcessor(UpperCamelCase , UpperCamelCase )
# Forward pass on example inputs (image + text)
if nlvr_model:
_lowerCamelCase = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=UpperCamelCase ).raw )
_lowerCamelCase = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' , stream=UpperCamelCase ).raw )
_lowerCamelCase = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
_lowerCamelCase = processor(UpperCamelCase , UpperCamelCase , return_tensors='pt' )
_lowerCamelCase = processor(UpperCamelCase , UpperCamelCase , return_tensors='pt' )
_lowerCamelCase = model(
input_ids=encoding_a.input_ids , pixel_values=encoding_a.pixel_values , pixel_values_a=encoding_a.pixel_values , )
else:
_lowerCamelCase = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' , stream=UpperCamelCase ).raw )
if mlm_model:
_lowerCamelCase = 'a bunch of [MASK] laying on a [MASK].'
else:
_lowerCamelCase = 'How many cats are there?'
_lowerCamelCase = processor(UpperCamelCase , UpperCamelCase , return_tensors='pt' )
_lowerCamelCase = model(**UpperCamelCase )
# Verify outputs
if mlm_model:
_lowerCamelCase = torch.Size([1, 11, 3_05_22] )
_lowerCamelCase = torch.tensor([-12.5_061, -12.5_123, -12.5_174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , UpperCamelCase , atol=1e-4 )
# verify masked token prediction equals "cats"
_lowerCamelCase = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
_lowerCamelCase = torch.Size([1, 31_29] )
_lowerCamelCase = torch.tensor([-15.9_495, -18.1_472, -10.3_041] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] , UpperCamelCase , atol=1e-4 )
# verify vqa prediction equals "2"
_lowerCamelCase = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
_lowerCamelCase = torch.Size([1, 2] )
_lowerCamelCase = torch.tensor([-2.8_721, 2.1_291] )
assert torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 )
assert outputs.logits.shape == expected_shape
Path(UpperCamelCase ).mkdir(exist_ok=UpperCamelCase )
print(F"""Saving model and processor to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase )
processor.save_pretrained(UpperCamelCase )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
A = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 234
|
from itertools import product
def lowerCamelCase ( UpperCamelCase : int , UpperCamelCase : int ) -> list[int]:
_lowerCamelCase = sides_number
_lowerCamelCase = max_face_number * dice_number
_lowerCamelCase = [0] * (max_total + 1)
_lowerCamelCase = 1
_lowerCamelCase = range(UpperCamelCase , max_face_number + 1 )
for dice_numbers in product(UpperCamelCase , repeat=UpperCamelCase ):
_lowerCamelCase = sum(UpperCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def lowerCamelCase ( ) -> float:
_lowerCamelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCamelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCamelCase = 0
_lowerCamelCase = 9
_lowerCamelCase = 4 * 9
_lowerCamelCase = 6
for peter_total in range(UpperCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCamelCase = (4**9) * (6**6)
_lowerCamelCase = peter_wins_count / total_games_number
_lowerCamelCase = round(UpperCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F'''{solution() = }''')
| 234
| 1
|
import math
import sys
import cva
import numpy as np
def _A ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : float ):
# For applying gaussian function for each element in matrix.
UpperCamelCase :Tuple = math.sqrt(_lowercase )
UpperCamelCase :List[Any] = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def _A ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase :Optional[Any] = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def _A ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : float ):
# Creates a gaussian kernel of given dimension.
UpperCamelCase :List[Any] = np.zeros((kernel_size, kernel_size) )
for i in range(0 , _lowercase ):
for j in range(0 , _lowercase ):
UpperCamelCase :Optional[int] = math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(_lowercase , _lowercase )
def _A ( SCREAMING_SNAKE_CASE__ : np.ndarray , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : int , ):
UpperCamelCase :Any = np.zeros(img.shape )
UpperCamelCase :Optional[Any] = get_gauss_kernel(_lowercase , _lowercase )
UpperCamelCase :Tuple = img.shape
for i in range(kernel_size // 2 , size_x - kernel_size // 2 ):
for j in range(kernel_size // 2 , size_y - kernel_size // 2 ):
UpperCamelCase :Tuple = get_slice(_lowercase , _lowercase , _lowercase , _lowercase )
UpperCamelCase :Dict = img_s - img_s[kernel_size // 2, kernel_size // 2]
UpperCamelCase :Optional[int] = vec_gaussian(_lowercase , _lowercase )
UpperCamelCase :Tuple = np.multiply(_lowercase , _lowercase )
UpperCamelCase :int = np.multiply(_lowercase , _lowercase )
UpperCamelCase :Tuple = np.sum(_lowercase ) / np.sum(_lowercase )
UpperCamelCase :Optional[int] = val
return imga
def _A ( SCREAMING_SNAKE_CASE__ : list ):
UpperCamelCase :Tuple = args[1] if args[1:] else """../image_data/lena.jpg"""
UpperCamelCase :Optional[Any] = float(args[2] ) if args[2:] else 1.0
UpperCamelCase :str = float(args[3] ) if args[3:] else 1.0
if args[4:]:
UpperCamelCase :Optional[Any] = int(args[4] )
UpperCamelCase :Optional[int] = kernel_size + abs(kernel_size % 2 - 1 )
else:
UpperCamelCase :Any = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
__snake_case , __snake_case , __snake_case , __snake_case = parse_args(sys.argv)
__snake_case = cva.imread(filename, 0)
cva.imshow("""input image""", img)
__snake_case = img / 2_55
__snake_case = out.astype("""float32""")
__snake_case = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
__snake_case = out * 2_55
__snake_case = np.uinta(out)
cva.imshow("""output image""", out)
cva.waitKey(0)
cva.destroyAllWindows()
| 658
|
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def UpperCamelCase__ ( _lowercase : Any ) -> List[Any]:
__UpperCAmelCase: List[Any] = min(_lowercase ) # min() finds the minimum value
__UpperCAmelCase: List[str] = max(_lowercase ) # max() finds the maximum value
__UpperCAmelCase: Dict = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
__UpperCAmelCase: str = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(_lowercase , _lowercase ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
__UpperCAmelCase: List[str] = 0
for count in range(_lowercase ):
while holes[count] > 0:
holes[count] -= 1
__UpperCAmelCase: Optional[int] = count + min_val
i += 1
def UpperCamelCase__ ( ) -> Optional[int]:
__UpperCAmelCase: Union[str, Any] = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(_lowercase )
print("""Sorted order is:""" , """ """.join(_lowercase ) )
if __name__ == "__main__":
main()
| 523
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class A__ :
'''simple docstring'''
def __init__( self: Any , _SCREAMING_SNAKE_CASE: int) -> None:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = value
__lowerCAmelCase : Node | None = None
__lowerCAmelCase : Node | None = None
class A__ :
'''simple docstring'''
def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Node) -> None:
"""simple docstring"""
__lowerCAmelCase : Tuple = tree
def _SCREAMING_SNAKE_CASE ( self: List[str] , _SCREAMING_SNAKE_CASE: Node | None) -> int:
"""simple docstring"""
if node is None:
return 0
return node.value + (
self.depth_first_search(node.left) + self.depth_first_search(node.right)
)
def __iter__( self: Union[str, Any]) -> Iterator[int]:
"""simple docstring"""
yield self.depth_first_search(self.tree)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712
|
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
__snake_case : Any = True
except (ImportError, ModuleNotFoundError):
__snake_case : Optional[int] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _lowercase ( __snake_case ) -> str:
re.sub("<n>" ,"" ,__snake_case ) # 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(__snake_case ) )
| 615
| 0
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class UpperCamelCase__:
def __init__( self : List[str] , lowerCAmelCase : Dict , lowerCAmelCase : Tuple=13 , lowerCAmelCase : List[str]=64 , lowerCAmelCase : List[Any]=2 , lowerCAmelCase : str=3 , lowerCAmelCase : Optional[int]=True , lowerCAmelCase : Union[str, Any]=True , lowerCAmelCase : str=32 , lowerCAmelCase : Optional[Any]=5 , lowerCAmelCase : Tuple=4 , lowerCAmelCase : Optional[int]=37 , lowerCAmelCase : Union[str, Any]="gelu" , lowerCAmelCase : Optional[Any]=0.1 , lowerCAmelCase : str=0.1 , lowerCAmelCase : int=10 , lowerCAmelCase : Tuple=0.02 , lowerCAmelCase : str=[1, 16, 4, 4] , lowerCAmelCase : Optional[Any]=None , )-> Dict:
"""simple docstring"""
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = image_size
UpperCAmelCase = patch_size
UpperCAmelCase = num_channels
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = scope
UpperCAmelCase = backbone_featmap_shape
# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
# the number of patches is based on the feature map of the backbone, which by default uses an output stride
# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
UpperCAmelCase = (self.image_size // 32) ** 2
UpperCAmelCase = num_patches + 1
def a__( self : int )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def a__( self : List[Any] )-> List[str]:
"""simple docstring"""
UpperCAmelCase = {
'''global_padding''': '''same''',
'''layer_type''': '''bottleneck''',
'''depths''': [3, 4, 9],
'''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
'''embedding_dynamic_padding''': True,
'''hidden_sizes''': [4, 8, 16, 32],
'''num_groups''': 2,
}
return ViTHybridConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=lowerCAmelCase , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=lowerCAmelCase , )
def a__( self : Dict , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Union[str, Any] )-> List[Any]:
"""simple docstring"""
UpperCAmelCase = ViTHybridModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
UpperCAmelCase = model(lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__( self : int , lowerCAmelCase : Any , lowerCAmelCase : Optional[Any] , lowerCAmelCase : int )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.type_sequence_label_size
UpperCAmelCase = ViTHybridForImageClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
UpperCAmelCase = model(lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a__( self : Any )-> List[Any]:
"""simple docstring"""
UpperCAmelCase = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = config_and_inputs
UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class UpperCamelCase__( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
__magic_name__ : List[Any] = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
__magic_name__ : List[Any] = (
{"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
if is_torch_available()
else {}
)
__magic_name__ : Dict = False
__magic_name__ : Union[str, Any] = False
__magic_name__ : Optional[int] = False
def a__( self : Optional[int] )-> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = ViTHybridModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=lowerCAmelCase , has_text_modality=lowerCAmelCase , hidden_size=37 )
def a__( self : List[Any] )-> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='''ViT does not use inputs_embeds''' )
def a__( self : List[Any] )-> Optional[Any]:
"""simple docstring"""
pass
def a__( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase , nn.Linear ) )
def a__( self : Tuple )-> Dict:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(lowerCAmelCase )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase )
def a__( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def a__( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )
def a__( self : Any )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase = _config_zero_init(lowerCAmelCase )
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(config=lowerCAmelCase )
# Skip the check for the backbone
for name, module in model.named_modules():
if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
UpperCAmelCase = [F"""{name}.{key}""" for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@slow
def a__( self : Any )-> Union[str, Any]:
"""simple docstring"""
for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = ViTHybridModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class UpperCamelCase__( unittest.TestCase ):
@cached_property
def a__( self : Tuple )-> int:
"""simple docstring"""
return (
ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def a__( self : Dict )-> Dict:
"""simple docstring"""
UpperCAmelCase = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
lowerCAmelCase )
UpperCAmelCase = self.default_image_processor
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowerCAmelCase , return_tensors='''pt''' ).to(lowerCAmelCase )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**lowerCAmelCase )
# verify the logits
UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase )
UpperCAmelCase = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase , atol=1E-4 ) )
@slow
@require_accelerate
def a__( self : str )-> Tuple:
"""simple docstring"""
UpperCAmelCase = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' )
UpperCAmelCase = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''' )
UpperCAmelCase = prepare_img()
UpperCAmelCase = image_processor(images=lowerCAmelCase , return_tensors='''pt''' )
UpperCAmelCase = model(**lowerCAmelCase )
UpperCAmelCase = outputs.logits
# model predicts one of the 1000 ImageNet classes
UpperCAmelCase = logits.argmax(-1 ).item()
self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''' )
| 210
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__( unittest.TestCase ):
@property
def a__( self : List[str] )-> Dict:
"""simple docstring"""
torch.manual_seed(0 )
UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def a__( self : Tuple )-> int:
"""simple docstring"""
UpperCAmelCase = self.dummy_uncond_unet
UpperCAmelCase = PNDMScheduler()
UpperCAmelCase = PNDMPipeline(unet=lowerCAmelCase , scheduler=lowerCAmelCase )
pndm.to(lowerCAmelCase )
pndm.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pndm(generator=lowerCAmelCase , num_inference_steps=20 , output_type='''numpy''' ).images
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pndm(generator=lowerCAmelCase , num_inference_steps=20 , output_type='''numpy''' , return_dict=lowerCAmelCase )[0]
UpperCAmelCase = image[0, -3:, -3:, -1]
UpperCAmelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class UpperCamelCase__( unittest.TestCase ):
def a__( self : Dict )-> List[str]:
"""simple docstring"""
UpperCAmelCase = '''google/ddpm-cifar10-32'''
UpperCAmelCase = UNetaDModel.from_pretrained(lowerCAmelCase )
UpperCAmelCase = PNDMScheduler()
UpperCAmelCase = PNDMPipeline(unet=lowerCAmelCase , scheduler=lowerCAmelCase )
pndm.to(lowerCAmelCase )
pndm.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase = torch.manual_seed(0 )
UpperCAmelCase = pndm(generator=lowerCAmelCase , output_type='''numpy''' ).images
UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 210
| 1
|
"""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
lowerCamelCase = 16
lowerCamelCase = 32
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ = 16 , lowerCAmelCase__ = "bert-base-cased" ):
UpperCAmelCase_ = AutoTokenizer.from_pretrained(lowerCAmelCase__ )
UpperCAmelCase_ = load_dataset("glue" , "mrpc" )
def tokenize_function(lowerCAmelCase__ ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase_ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=lowerCAmelCase__ , max_length=lowerCAmelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase_ = datasets.map(
lowerCAmelCase__ , batched=lowerCAmelCase__ , remove_columns=["idx", "sentence1", "sentence2"] , load_from_cache_file=lowerCAmelCase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
UpperCAmelCase_ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(lowerCAmelCase__ ):
# 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(lowerCAmelCase__ , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(lowerCAmelCase__ , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
UpperCAmelCase_ = DataLoader(
tokenized_datasets["train"] , shuffle=lowerCAmelCase__ , collate_fn=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
UpperCAmelCase_ = DataLoader(
tokenized_datasets["validation"] , shuffle=lowerCAmelCase__ , collate_fn=lowerCAmelCase__ , batch_size=lowerCAmelCase__ )
return train_dataloader, eval_dataloader
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
model.eval()
UpperCAmelCase_ = 0
for step, batch in enumerate(lowerCAmelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCAmelCase_ = model(**lowerCAmelCase__ )
UpperCAmelCase_ = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
UpperCAmelCase_ , UpperCAmelCase_ = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCAmelCase__ ) - 1:
UpperCAmelCase_ = predictions[: len(eval_dataloader.dataset ) - samples_seen]
UpperCAmelCase_ = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCAmelCase__ , references=lowerCAmelCase__ , )
UpperCAmelCase_ = metric.compute()
return eval_metric["accuracy"]
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
# Initialize accelerator
UpperCAmelCase_ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCAmelCase_ = config["lr"]
UpperCAmelCase_ = int(config["num_epochs"] )
UpperCAmelCase_ = int(config["seed"] )
UpperCAmelCase_ = int(config["batch_size"] )
UpperCAmelCase_ = args.model_name_or_path
set_seed(lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_ = get_dataloaders(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCAmelCase_ = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__ , return_dict=lowerCAmelCase__ )
# Instantiate optimizer
UpperCAmelCase_ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
UpperCAmelCase_ = optimizer_cls(params=model.parameters() , lr=lowerCAmelCase__ )
if accelerator.state.deepspeed_plugin is not None:
UpperCAmelCase_ = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
UpperCAmelCase_ = 1
UpperCAmelCase_ = (len(lowerCAmelCase__ ) * 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_ = get_linear_schedule_with_warmup(
optimizer=lowerCAmelCase__ , num_warmup_steps=0 , num_training_steps=lowerCAmelCase__ , )
else:
UpperCAmelCase_ = DummyScheduler(lowerCAmelCase__ , total_num_steps=lowerCAmelCase__ , 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_ = accelerator.prepare(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# We need to keep track of how many total steps we have iterated over
UpperCAmelCase_ = 0
# We also need to keep track of the stating epoch so files are named properly
UpperCAmelCase_ = 0
UpperCAmelCase_ = evaluate.load("glue" , "mrpc" )
UpperCAmelCase_ = num_epochs
if args.partial_train_epoch is not None:
UpperCAmelCase_ = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
UpperCAmelCase_ = args.resume_from_checkpoint.split("epoch_" )[1]
UpperCAmelCase_ = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
UpperCAmelCase_ = int(lowerCAmelCase__ ) + 1
UpperCAmelCase_ = evaluation_loop(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
accelerator.print("resumed checkpoint performance:" , lowerCAmelCase__ )
accelerator.print("resumed checkpoint's scheduler's lr:" , lr_scheduler.get_lr()[0] )
accelerator.print("resumed optimizers's lr:" , optimizer.param_groups[0]["lr"] )
with open(os.path.join(args.output_dir , f"""state_{starting_epoch-1}.json""" ) , "r" ) as f:
UpperCAmelCase_ = json.load(lowerCAmelCase__ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
UpperCAmelCase_ = {}
for epoch in range(lowerCAmelCase__ , lowerCAmelCase__ ):
model.train()
for step, batch in enumerate(lowerCAmelCase__ ):
UpperCAmelCase_ = model(**lowerCAmelCase__ )
UpperCAmelCase_ = outputs.loss
UpperCAmelCase_ = loss / gradient_accumulation_steps
accelerator.backward(lowerCAmelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
UpperCAmelCase_ = f"""epoch_{epoch}"""
UpperCAmelCase_ = os.path.join(args.output_dir , lowerCAmelCase__ )
accelerator.save_state(lowerCAmelCase__ )
UpperCAmelCase_ = evaluation_loop(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase_ = accuracy
UpperCAmelCase_ = lr_scheduler.get_lr()[0]
UpperCAmelCase_ = optimizer.param_groups[0]["lr"]
UpperCAmelCase_ = epoch
UpperCAmelCase_ = overall_step
accelerator.print(f"""epoch {epoch}:""" , lowerCAmelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , f"""state_{epoch}.json""" ) , "w" ) as f:
json.dump(lowerCAmelCase__ , lowerCAmelCase__ )
def a__ ( ):
UpperCAmelCase_ = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path" , type=lowerCAmelCase__ , default="bert-base-cased" , help="Path to pretrained model or model identifier from huggingface.co/models." , required=lowerCAmelCase__ , )
parser.add_argument(
"--output_dir" , type=lowerCAmelCase__ , 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=lowerCAmelCase__ , default=lowerCAmelCase__ , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=lowerCAmelCase__ , default=lowerCAmelCase__ , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=lowerCAmelCase__ , default=2 , help="Number of train epochs." , )
UpperCAmelCase_ = parser.parse_args()
UpperCAmelCase_ = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
main()
| 14
|
"""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 a__ ( lowerCAmelCase__ , lowerCAmelCase__=False ):
UpperCAmelCase_ , UpperCAmelCase_ = 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 a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = r".*sequential.(\d+).*"
UpperCAmelCase_ = 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:
UpperCAmelCase_ = key.replace(lowerCAmelCase__ , lowerCAmelCase__ )
if re.match(lowerCAmelCase__ , lowerCAmelCase__ ):
# replace sequential layers with list
UpperCAmelCase_ = re.match(lowerCAmelCase__ , lowerCAmelCase__ ).group(1 )
UpperCAmelCase_ = key.replace(f"""sequential.{sequential_layer}.""" , f"""layers.{int(lowerCAmelCase__ )//3}.linear.""" )
elif re.match(lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = int(re.match(lowerCAmelCase__ , lowerCAmelCase__ ).group(1 ) )
# Because in CLAP they use `nn.Sequential`...
UpperCAmelCase_ = 1 if projecton_layer == 0 else 2
UpperCAmelCase_ = 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
UpperCAmelCase_ = value
UpperCAmelCase_ = mixed_qkv.size(0 ) // 3
UpperCAmelCase_ = mixed_qkv[:qkv_dim]
UpperCAmelCase_ = mixed_qkv[qkv_dim : qkv_dim * 2]
UpperCAmelCase_ = mixed_qkv[qkv_dim * 2 :]
UpperCAmelCase_ = query_layer
UpperCAmelCase_ = key_layer
UpperCAmelCase_ = value_layer
else:
UpperCAmelCase_ = value
return model_state_dict
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ):
UpperCAmelCase_ , UpperCAmelCase_ = init_clap(lowerCAmelCase__ , enable_fusion=lowerCAmelCase__ )
clap_model.eval()
UpperCAmelCase_ = clap_model.state_dict()
UpperCAmelCase_ = rename_state_dict(lowerCAmelCase__ )
UpperCAmelCase_ = ClapConfig()
UpperCAmelCase_ = enable_fusion
UpperCAmelCase_ = 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)
| 14
| 1
|
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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 (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Tuple=3 , UpperCAmelCase__ : List[Any]=7 , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : Any=True , UpperCAmelCase__ : Union[str, Any]=False , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : Any=99 , UpperCAmelCase__ : Optional[int]=32 , UpperCAmelCase__ : str=5 , UpperCAmelCase__ : Dict=4 , UpperCAmelCase__ : str=37 , UpperCAmelCase__ : int="gelu" , UpperCAmelCase__ : Dict=0.1 , UpperCAmelCase__ : List[str]=0.1 , UpperCAmelCase__ : Any=512 , UpperCAmelCase__ : Union[str, Any]=16 , UpperCAmelCase__ : Tuple=2 , UpperCAmelCase__ : Any=0.02 , UpperCAmelCase__ : List[Any]=3 , UpperCAmelCase__ : Tuple=4 , UpperCAmelCase__ : Union[str, Any]=None , ) ->Optional[Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = seq_length
A__ = is_training
A__ = use_input_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = type_sequence_label_size
A__ = initializer_range
A__ = num_labels
A__ = num_choices
A__ = scope
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Tuple:
'''simple docstring'''
A__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.seq_length])
A__ = None
A__ = None
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size)
A__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
A__ = ids_tensor([self.batch_size] , self.num_choices)
A__ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def SCREAMING_SNAKE_CASE ( self : Union[str, Any]) ->Optional[int]:
'''simple docstring'''
return FalconConfig(
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=UpperCAmelCase__ , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=UpperCAmelCase__ , )
def SCREAMING_SNAKE_CASE ( self : Any , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Any , UpperCAmelCase__ : List[str]) ->List[str]:
'''simple docstring'''
A__ = FalconModel(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__)
A__ = model(UpperCAmelCase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[int] , ) ->Optional[int]:
'''simple docstring'''
A__ = True
A__ = FalconModel(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , )
A__ = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , )
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Any , UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[Any] , ) ->int:
'''simple docstring'''
A__ = FalconForCausalLM(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def SCREAMING_SNAKE_CASE ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : int , ) ->Optional[int]:
'''simple docstring'''
A__ = True
A__ = True
A__ = FalconForCausalLM(config=UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
# first forward pass
A__ = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , use_cache=UpperCAmelCase__ , )
A__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
A__ = ids_tensor((self.batch_size, 3) , config.vocab_size)
A__ = ids_tensor((self.batch_size, 3) , vocab_size=2)
# append to next input_ids and
A__ = torch.cat([input_ids, next_tokens] , dim=-1)
A__ = torch.cat([input_mask, next_mask] , dim=-1)
A__ = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , )['''hidden_states'''][0]
A__ = model(
UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , encoder_hidden_states=UpperCAmelCase__ , encoder_attention_mask=UpperCAmelCase__ , past_key_values=UpperCAmelCase__ , output_hidden_states=UpperCAmelCase__ , )['''hidden_states'''][0]
# select random slice
A__ = ids_tensor((1,) , output_from_past.shape[-1]).item()
A__ = output_from_no_past[:, -3:, random_slice_idx].detach()
A__ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCAmelCase__ , UpperCAmelCase__ , atol=1e-3))
def SCREAMING_SNAKE_CASE ( self : int) ->List[str]:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
(
(
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) , (
A__
) ,
) = config_and_inputs
A__ = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
UpperCAmelCase__ = (FalconForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__ = (
{
'''feature-extraction''': FalconModel,
'''text-classification''': FalconForSequenceClassification,
'''text-generation''': FalconForCausalLM,
'''question-answering''': FalconForQuestionAnswering,
'''token-classification''': FalconForTokenClassification,
'''zero-shot''': FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Tuple:
'''simple docstring'''
A__ = FalconModelTester(self)
A__ = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37)
def SCREAMING_SNAKE_CASE ( self : Optional[Any]) ->Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : List[str]) ->Dict:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->Union[str, Any]:
'''simple docstring'''
A__ , *A__ = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
A__ = alibi
self.model_tester.create_and_check_model(UpperCAmelCase__ , *UpperCAmelCase__)
def SCREAMING_SNAKE_CASE ( self : int) ->Optional[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = 3
A__ = input_dict['''input_ids''']
A__ = input_ids.ne(1).to(UpperCAmelCase__)
A__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
A__ = FalconForSequenceClassification(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = 3
A__ = '''single_label_classification'''
A__ = input_dict['''input_ids''']
A__ = input_ids.ne(1).to(UpperCAmelCase__)
A__ = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size)
A__ = FalconForSequenceClassification(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def SCREAMING_SNAKE_CASE ( self : Tuple) ->Dict:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = input_dict['''input_ids''']
A__ = FalconForCausalLM(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , use_cache=UpperCAmelCase__)
A__ = input_ids.shape[0]
A__ = model._convert_to_rw_cache(result.past_key_values)
A__ = model._convert_cache_to_standard_format(UpperCAmelCase__ , UpperCAmelCase__)
for layer in range(len(UpperCAmelCase__)):
for tensor_idx in range(2):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]))
def SCREAMING_SNAKE_CASE ( self : Optional[int]) ->List[str]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = 3
A__ = '''multi_label_classification'''
A__ = input_dict['''input_ids''']
A__ = input_ids.ne(1).to(UpperCAmelCase__)
A__ = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size).to(torch.float)
A__ = FalconForSequenceClassification(UpperCAmelCase__)
model.to(UpperCAmelCase__)
model.eval()
A__ = model(UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , labels=UpperCAmelCase__)
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels))
def SCREAMING_SNAKE_CASE ( self : Tuple) ->List[str]:
'''simple docstring'''
for model_class in self.all_generative_model_classes:
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(UpperCAmelCase__ , '''use_cache'''):
return
A__ = model_class(UpperCAmelCase__).to(UpperCAmelCase__)
if "use_cache" not in inputs:
A__ = True
A__ = model(**UpperCAmelCase__)
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
A__ = (
getattr(UpperCAmelCase__ , '''decoder_layers''' , UpperCAmelCase__)
or getattr(UpperCAmelCase__ , '''num_decoder_layers''' , UpperCAmelCase__)
or config.num_hidden_layers
)
A__ = getattr(UpperCAmelCase__ , '''num_kv_heads''' , config.num_attention_heads)
A__ = getattr(UpperCAmelCase__ , '''d_model''' , config.hidden_size)
A__ = embed_dim // num_attention_heads
A__ = outputs['''past_key_values''']
self.assertEqual(len(UpperCAmelCase__) , UpperCAmelCase__)
A__ , A__ = inputs['''input_ids'''].shape
for i in range(UpperCAmelCase__):
if config.new_decoder_architecture:
A__ = config.num_attention_heads
elif config.multi_query:
A__ = 1
self.assertEqual(len(past_kv[0]) , 2) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim))
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim))
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE ( self : List[Any]) ->Union[str, Any]:
'''simple docstring'''
A__ = AutoTokenizer.from_pretrained('''Rocketknight1/falcon-rw-1b''')
A__ = FalconForCausalLM.from_pretrained('''Rocketknight1/falcon-rw-1b''')
model.eval()
model.to(UpperCAmelCase__)
A__ = tokenizer('''My favorite food is''' , return_tensors='''pt''').to(UpperCAmelCase__)
A__ = (
'''My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday.'''
)
A__ = model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=19)
A__ = tokenizer.batch_decode(UpperCAmelCase__)[0]
self.assertEqual(UpperCAmelCase__ , UpperCAmelCase__)
@slow
def SCREAMING_SNAKE_CASE ( self : List[str]) ->List[Any]:
'''simple docstring'''
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
A__ = AutoTokenizer.from_pretrained(UpperCAmelCase__)
A__ = FalconForCausalLM.from_pretrained(UpperCAmelCase__)
model.eval()
model.to(UpperCAmelCase__)
A__ = tokenizer('''My favorite food is''' , return_tensors='''pt''').to(UpperCAmelCase__)
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=4)
model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=4)
model.generate(**UpperCAmelCase__ , num_beams=2 , max_new_tokens=4)
@slow
def SCREAMING_SNAKE_CASE ( self : Dict) ->int:
'''simple docstring'''
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
A__ = AutoTokenizer.from_pretrained(UpperCAmelCase__)
A__ = FalconForCausalLM.from_pretrained(UpperCAmelCase__)
model.eval()
model.to(device=UpperCAmelCase__)
A__ = tokenizer('''My favorite food is''' , return_tensors='''pt''').to(UpperCAmelCase__)
# Test results are the same with and without cache
A__ = model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=20 , use_cache=UpperCAmelCase__)
A__ = model.generate(**UpperCAmelCase__ , do_sample=UpperCAmelCase__ , max_new_tokens=20 , use_cache=UpperCAmelCase__)
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)
| 87
|
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
from ..pytorch_utils import ConvaD
if is_accelerate_available():
from accelerate import init_empty_weights
from accelerate.utils import find_tied_parameters
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_=None , lowercase_=None ) -> Dict:
"""simple docstring"""
if "." in tensor_name:
A__ = tensor_name.split('''.''' )
for split in splits[:-1]:
A__ = getattr(lowercase_ , lowercase_ )
if new_module is None:
raise ValueError(f"""{module} has no attribute {split}.""" )
A__ = new_module
A__ = splits[-1]
if tensor_name not in module._parameters and tensor_name not in module._buffers:
raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}.""" )
A__ = tensor_name in module._buffers
A__ = getattr(lowercase_ , lowercase_ )
if old_value.device == torch.device('''meta''' ) and device not in ["meta", torch.device('''meta''' )] and value is None:
raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}.""" )
A__ = False
A__ = False
if is_buffer or not is_bitsandbytes_available():
A__ = False
A__ = False
else:
A__ = hasattr(bnb.nn , '''Params4bit''' ) and isinstance(module._parameters[tensor_name] , bnb.nn.Paramsabit )
A__ = isinstance(module._parameters[tensor_name] , bnb.nn.IntaParams )
if is_abit or is_abit:
A__ = module._parameters[tensor_name]
if param.device.type != "cuda":
if value is None:
A__ = old_value.to(lowercase_ )
elif isinstance(lowercase_ , torch.Tensor ):
A__ = value.to('''cpu''' )
if value.dtype == torch.inta:
A__ = version.parse(importlib.metadata.version('''bitsandbytes''' ) ) > version.parse(
'''0.37.2''' )
if not is_abit_serializable:
raise ValueError(
'''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
'''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.''' )
else:
A__ = torch.tensor(lowercase_ , device='''cpu''' )
# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
# Since weights are saved in the correct "orientation", we skip transposing when loading.
if issubclass(module.source_cls , lowercase_ ) and fpaa_statistics is None:
A__ = new_value.T
A__ = old_value.__dict__
if is_abit:
A__ = bnb.nn.IntaParams(lowercase_ , requires_grad=lowercase_ , **lowercase_ ).to(lowercase_ )
elif is_abit:
A__ = bnb.nn.Paramsabit(lowercase_ , requires_grad=lowercase_ , **lowercase_ ).to(lowercase_ )
A__ = new_value
if fpaa_statistics is not None:
setattr(module.weight , '''SCB''' , fpaa_statistics.to(lowercase_ ) )
else:
if value is None:
A__ = old_value.to(lowercase_ )
elif isinstance(lowercase_ , torch.Tensor ):
A__ = value.to(lowercase_ )
else:
A__ = torch.tensor(lowercase_ , device=lowercase_ )
if is_buffer:
A__ = new_value
else:
A__ = nn.Parameter(lowercase_ , requires_grad=old_value.requires_grad )
A__ = new_value
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=False ) -> Dict:
"""simple docstring"""
for name, module in model.named_children():
if current_key_name is None:
A__ = []
current_key_name.append(lowercase_ )
if (isinstance(lowercase_ , nn.Linear ) or isinstance(lowercase_ , lowercase_ )) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
if not any(key in '''.'''.join(lowercase_ ) for key in modules_to_not_convert ):
with init_empty_weights():
if isinstance(lowercase_ , lowercase_ ):
A__ , A__ = module.weight.shape
else:
A__ = module.in_features
A__ = module.out_features
if quantization_config.quantization_method() == "llm_int8":
A__ = bnb.nn.LinearabitLt(
lowercase_ , lowercase_ , module.bias is not None , has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight , threshold=quantization_config.llm_inta_threshold , )
A__ = True
else:
if (
quantization_config.llm_inta_skip_modules is not None
and name in quantization_config.llm_inta_skip_modules
):
pass
else:
A__ = bnb.nn.Linearabit(
lowercase_ , lowercase_ , module.bias is not None , quantization_config.bnb_abit_compute_dtype , compress_statistics=quantization_config.bnb_abit_use_double_quant , quant_type=quantization_config.bnb_abit_quant_type , )
A__ = True
# Store the module class in case we need to transpose the weight later
A__ = type(lowercase_ )
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(lowercase_ )
if len(list(module.children() ) ) > 0:
A__ , A__ = _replace_with_bnb_linear(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , has_been_replaced=lowercase_ , )
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_=None , lowercase_=None , lowercase_=None ) -> Tuple:
"""simple docstring"""
A__ = ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
A__ , A__ = _replace_with_bnb_linear(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def SCREAMING_SNAKE_CASE ( *lowercase_ , **lowercase_ ) -> Dict:
"""simple docstring"""
warnings.warn(
'''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead''' , lowercase_ , )
return replace_with_bnb_linear(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE ( *lowercase_ , **lowercase_ ) -> Optional[Any]:
"""simple docstring"""
warnings.warn(
'''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead''' , lowercase_ , )
return set_module_quantized_tensor_to_device(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> List[str]:
"""simple docstring"""
A__ = deepcopy(lowercase_ ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
tied_model.tie_weights()
A__ = find_tied_parameters(lowercase_ )
# For compatibility with Accelerate < 0.18
if isinstance(lowercase_ , lowercase_ ):
A__ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
A__ = sum(lowercase_ , [] )
A__ = len(lowercase_ ) > 0
# Check if it is a base model
A__ = not hasattr(lowercase_ , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
A__ = list(model.named_children() )
A__ = [list_modules[-1][0]]
# add last module together with tied weights
A__ = set(lowercase_ ) - set(lowercase_ )
A__ = list(set(lowercase_ ) ) + list(lowercase_ )
# remove ".weight" from the keys
A__ = ['''.weight''', '''.bias''']
A__ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
A__ = name.replace(lowercase_ , '''''' )
filtered_module_names.append(lowercase_ )
return filtered_module_names
| 87
| 1
|
'''simple docstring'''
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def A_ ( snake_case , snake_case , snake_case ):
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
SCREAMING_SNAKE_CASE:Tuple = (low + high) // 2
SCREAMING_SNAKE_CASE:Dict = max_subarray(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
SCREAMING_SNAKE_CASE:Optional[int] = max_subarray(_lowerCamelCase , mid + 1 , _lowerCamelCase )
SCREAMING_SNAKE_CASE:Dict = max_cross_sum(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def A_ ( snake_case , snake_case , snake_case , snake_case ):
SCREAMING_SNAKE_CASE:Union[str, Any] = float("-inf" ), -1
SCREAMING_SNAKE_CASE:Optional[int] = float("-inf" ), -1
SCREAMING_SNAKE_CASE:int | float = 0
for i in range(_lowerCamelCase , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
SCREAMING_SNAKE_CASE:Tuple = summ
SCREAMING_SNAKE_CASE:Optional[int] = i
SCREAMING_SNAKE_CASE:Union[str, Any] = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
SCREAMING_SNAKE_CASE:Union[str, Any] = summ
SCREAMING_SNAKE_CASE:Optional[int] = i
return max_left, max_right, (left_sum + right_sum)
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:Tuple = [randint(1 , _lowerCamelCase ) for _ in range(_lowerCamelCase )]
SCREAMING_SNAKE_CASE:Optional[Any] = time.time()
max_subarray(_lowerCamelCase , 0 , input_size - 1 )
SCREAMING_SNAKE_CASE:Any = time.time()
return end - start
def A_ ( ):
SCREAMING_SNAKE_CASE:Dict = [10, 100, 1000, 10000, 50000, 100000, 200000, 300000, 400000, 500000]
SCREAMING_SNAKE_CASE:Union[str, Any] = [time_max_subarray(_lowerCamelCase ) for input_size in input_sizes]
print("No of Inputs\t\tTime Taken" )
for input_size, runtime in zip(_lowerCamelCase , _lowerCamelCase ):
print(_lowerCamelCase , "\t\t" , _lowerCamelCase )
plt.plot(_lowerCamelCase , _lowerCamelCase )
plt.xlabel("Number of Inputs" )
plt.ylabel("Time taken in seconds" )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 706
|
'''simple docstring'''
from ....utils import logging
A_ = logging.get_logger(__name__)
class _snake_case ( _a ):
def __init__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any]=None ,SCREAMING_SNAKE_CASE__ : Optional[int]=2_048 ):
SCREAMING_SNAKE_CASE:int = config.__dict__
SCREAMING_SNAKE_CASE:List[str] = modal_hidden_size
if num_labels:
SCREAMING_SNAKE_CASE:str = num_labels
| 465
| 0
|
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
__UpperCAmelCase = 'python tqdm regex requests packaging filelock numpy tokenizers'.split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append('dataclasses')
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append('importlib_metadata')
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase=None ):
'''simple docstring'''
require_version(deps[pkg] , __UpperCamelCase )
| 65
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Any = (
"""This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
"""It takes two arguments named `image` which should be the original image, and `label` which should be a text """
"""describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
)
lowerCAmelCase__ : Tuple = """CIDAS/clipseg-rd64-refined"""
lowerCAmelCase__ : Optional[Any] = """image_segmenter"""
lowerCAmelCase__ : Optional[Any] = CLIPSegForImageSegmentation
lowerCAmelCase__ : Any = ["""image""", """text"""]
lowerCAmelCase__ : Optional[Any] = ["""image"""]
def __init__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
requires_backends(self , ["vision"] )
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def A ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
return self.pre_processor(text=[label] , images=[image] , padding=_SCREAMING_SNAKE_CASE , return_tensors="pt" )
def A ( self , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
with torch.no_grad():
a_ : List[Any] = self.model(**_SCREAMING_SNAKE_CASE ).logits
return logits
def A ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
a_ : List[Any] = outputs.cpu().detach().numpy()
a_ : Optional[Any] = 0
a_ : Optional[int] = 1
return Image.fromarray((array * 2_5_5).astype(np.uinta ) )
| 473
| 0
|
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase__ ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[Any] ) -> str:
'''simple docstring'''
if openai_config_file == "":
a__ : int = OpenAIGPTConfig()
else:
a__ : str = OpenAIGPTConfig.from_json_file(snake_case__ )
a__ : int = OpenAIGPTModel(snake_case__ )
# Load weights from numpy
load_tf_weights_in_openai_gpt(snake_case__ , snake_case__ , snake_case__ )
# Save pytorch-model
a__ : Any = pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
a__ : List[str] = pytorch_dump_folder_path + """/""" + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
torch.save(model.state_dict() , snake_case__ )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(snake_case__ , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--openai_checkpoint_folder_path''',
default=None,
type=str,
required=True,
help='''Path to the TensorFlow checkpoint path.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--openai_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
__UpperCAmelCase = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 706
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
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 (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class __UpperCAmelCase :
def __init__( self : Any , a_ : int , a_ : Any=13 , a_ : int=30 , a_ : int=2 , a_ : str=3 , a_ : List[Any]=True , a_ : Union[str, Any]=True , a_ : Any=32 , a_ : Union[str, Any]=2 , a_ : Union[str, Any]=4 , a_ : Optional[int]=37 , a_ : Any="gelu" , a_ : Optional[Any]=0.1 , a_ : str=0.1 , a_ : str=10 , a_ : str=0.02 , a_ : Dict=3 , a_ : Optional[Any]=None , a_ : str=2 , ) -> List[Any]:
'''simple docstring'''
a__ : Optional[Any] = parent
a__ : Optional[int] = batch_size
a__ : int = image_size
a__ : Optional[Any] = patch_size
a__ : List[Any] = num_channels
a__ : List[str] = is_training
a__ : List[Any] = use_labels
a__ : List[Any] = hidden_size
a__ : Tuple = num_hidden_layers
a__ : Dict = num_attention_heads
a__ : str = intermediate_size
a__ : Union[str, Any] = hidden_act
a__ : List[str] = hidden_dropout_prob
a__ : Dict = attention_probs_dropout_prob
a__ : Optional[int] = type_sequence_label_size
a__ : Optional[Any] = initializer_range
a__ : Any = scope
a__ : List[str] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
a__ : Any = (image_size // patch_size) ** 2
a__ : List[str] = num_patches + 2
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
a__ : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a__ : List[str] = None
if self.use_labels:
a__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : int = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self : Dict ) -> int:
'''simple docstring'''
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , 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 , is_decoder=a_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCAmelCase ( self : Any , a_ : Dict , a_ : Optional[Any] , a_ : Optional[int] ) -> List[Any]:
'''simple docstring'''
a__ : int = TFDeiTModel(config=a_ )
a__ : List[str] = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Any , a_ : int , a_ : Tuple , a_ : Union[str, Any] ) -> Dict:
'''simple docstring'''
a__ : Dict = TFDeiTForMaskedImageModeling(config=a_ )
a__ : str = model(a_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
a__ : List[Any] = 1
a__ : int = TFDeiTForMaskedImageModeling(a_ )
a__ : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a__ : Any = model(a_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCAmelCase ( self : List[str] , a_ : List[str] , a_ : str , a_ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
a__ : Any = self.type_sequence_label_size
a__ : Union[str, Any] = TFDeiTForImageClassification(a_ )
a__ : List[str] = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
a__ : Union[str, Any] = 1
a__ : Any = TFDeiTForImageClassification(a_ )
a__ : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a__ : int = model(a_ , labels=a_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self : str ) -> str:
'''simple docstring'''
a__ : List[Any] = self.prepare_config_and_inputs()
a__ , a__ , a__ : Optional[Any] = config_and_inputs
a__ : Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class __UpperCAmelCase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
__lowerCamelCase : Union[str, Any] = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
__lowerCamelCase : Dict = (
{
"feature-extraction": TFDeiTModel,
"image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
__lowerCamelCase : Any = False
__lowerCamelCase : Dict = False
__lowerCamelCase : int = False
__lowerCamelCase : Optional[Any] = False
def UpperCAmelCase ( self : str ) -> Optional[Any]:
'''simple docstring'''
a__ : int = TFDeiTModelTester(self )
a__ : str = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def UpperCAmelCase ( self : int ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def UpperCAmelCase ( self : str ) -> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
a__ , a__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Optional[Any] = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
a__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , tf.keras.layers.Dense ) )
def UpperCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
a__ , a__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a__ : Dict = model_class(a_ )
a__ : Any = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a__ : Dict = [*signature.parameters.keys()]
a__ : List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a_ )
def UpperCAmelCase ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
a__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def UpperCAmelCase ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*a_ )
def UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
a__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*a_ )
def UpperCAmelCase ( self : List[Any] , a_ : Union[str, Any] , a_ : Union[str, Any] , a_ : Optional[Any]=False ) -> Dict:
'''simple docstring'''
a__ : int = super()._prepare_for_class(a_ , a_ , return_labels=a_ )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def UpperCAmelCase ( self : List[Any] ) -> Tuple:
'''simple docstring'''
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a__ : Tuple = TFDeiTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def lowercase__ ( ) -> Optional[int]:
'''simple docstring'''
a__ : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self : Any ) -> Tuple:
'''simple docstring'''
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase ( self : Any ) -> Optional[int]:
'''simple docstring'''
a__ : Tuple = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" )
a__ : Optional[Any] = self.default_image_processor
a__ : Optional[Any] = prepare_img()
a__ : Tuple = image_processor(images=a_ , return_tensors="tf" )
# forward pass
a__ : int = model(**a_ )
# verify the logits
a__ : int = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , a_ )
a__ : Union[str, Any] = tf.constant([-1.0266, 0.1912, -1.2861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , a_ , atol=1E-4 ) )
| 251
| 0
|
'''simple docstring'''
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Any = '''EncodecFeatureExtractor'''
__lowercase : int = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> str:
super().__init__(__UpperCAmelCase ,__UpperCAmelCase )
lowerCAmelCase__ : Tuple = self.feature_extractor
lowerCAmelCase__ : Tuple = False
def UpperCAmelCase_ ( self ,__UpperCAmelCase=None ,__UpperCAmelCase=None ,__UpperCAmelCase=True ) -> List[str]:
return self.tokenizer.get_decoder_prompt_ids(task=__UpperCAmelCase ,language=__UpperCAmelCase ,no_timestamps=__UpperCAmelCase )
def __call__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> str:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__UpperCAmelCase ,**__UpperCAmelCase )
lowerCAmelCase__ : Optional[Any] = kwargs.pop("""audio""" ,__UpperCAmelCase )
lowerCAmelCase__ : Dict = kwargs.pop("""sampling_rate""" ,__UpperCAmelCase )
lowerCAmelCase__ : Tuple = kwargs.pop("""text""" ,__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
lowerCAmelCase__ : Optional[int] = args[0]
lowerCAmelCase__ : List[Any] = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if text is not None:
lowerCAmelCase__ : Optional[Any] = self.tokenizer(__UpperCAmelCase ,**__UpperCAmelCase )
if audio is not None:
lowerCAmelCase__ : Any = self.feature_extractor(__UpperCAmelCase ,*__UpperCAmelCase ,sampling_rate=__UpperCAmelCase ,**__UpperCAmelCase )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
lowerCAmelCase__ : List[str] = audio_inputs["""input_values"""]
if "padding_mask" in audio_inputs:
lowerCAmelCase__ : int = audio_inputs["""padding_mask"""]
return inputs
def UpperCAmelCase_ ( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> List[str]:
lowerCAmelCase__ : Union[str, Any] = kwargs.pop("""audio""" ,__UpperCAmelCase )
lowerCAmelCase__ : str = kwargs.pop("""padding_mask""" ,__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
lowerCAmelCase__ : Union[str, Any] = args[0]
lowerCAmelCase__ : Dict = args[1:]
if audio_values is not None:
return self._decode_audio(__UpperCAmelCase ,padding_mask=__UpperCAmelCase )
else:
return self.tokenizer.batch_decode(*__UpperCAmelCase ,**__UpperCAmelCase )
def UpperCAmelCase_ ( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> Any:
return self.tokenizer.decode(*__UpperCAmelCase ,**__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = None ) -> List[np.ndarray]:
lowerCAmelCase__ : Optional[Any] = to_numpy(__UpperCAmelCase )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : int = audio_values.shape
if padding_mask is None:
return list(__UpperCAmelCase )
lowerCAmelCase__ : Tuple = to_numpy(__UpperCAmelCase )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
lowerCAmelCase__ : Optional[int] = seq_len - padding_mask.shape[-1]
lowerCAmelCase__ : Tuple = 1 - self.feature_extractor.padding_value
lowerCAmelCase__ : int = np.pad(__UpperCAmelCase ,((0, 0), (0, difference)) ,"""constant""" ,constant_values=__UpperCAmelCase )
lowerCAmelCase__ : int = audio_values.tolist()
for i in range(__UpperCAmelCase ):
lowerCAmelCase__ : List[str] = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
lowerCAmelCase__ : List[Any] = sliced_audio.reshape(__UpperCAmelCase ,-1 )
return audio_values
| 565
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
'''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''OPTForCausalLM''',
'''OPTModel''',
'''OPTPreTrainedModel''',
'''OPTForSequenceClassification''',
'''OPTForQuestionAnswering''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
'''FlaxOPTForCausalLM''',
'''FlaxOPTModel''',
'''FlaxOPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_opt import (
OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OPTForCausalLM,
OPTForQuestionAnswering,
OPTForSequenceClassification,
OPTModel,
OPTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 565
| 1
|
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __lowerCamelCase ( _a ):
def __init__( self , snake_case_ , snake_case_ = None , snake_case_ = None , snake_case_ = True , snake_case_ = None , snake_case_ = False , snake_case_ = None , snake_case_ = True , snake_case_ = "arrow" , **snake_case_ , ) -> str:
super().__init__(
split=snake_case_ , features=snake_case_ , cache_dir=snake_case_ , keep_in_memory=snake_case_ , streaming=snake_case_ , **snake_case_ , )
UpperCamelCase__ = load_from_cache_file
UpperCamelCase__ = file_format
UpperCamelCase__ = Spark(
df=snake_case_ , features=snake_case_ , cache_dir=snake_case_ , working_dir=snake_case_ , **snake_case_ , )
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
UpperCamelCase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=snake_case_ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 20
|
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __lowerCamelCase ( _a ):
@staticmethod
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> Union[str, Any]:
UpperCamelCase__ = parser.add_parser('download' )
download_parser.add_argument(
'--cache-dir' , type=snake_case_ , default=snake_case_ , help='Path to location to store the models' )
download_parser.add_argument(
'--force' , action='store_true' , help='Force the model to be download even if already in cache-dir' )
download_parser.add_argument(
'--trust-remote-code' , action='store_true' , help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' , )
download_parser.add_argument('model' , type=snake_case_ , help='Name of the model to download' )
download_parser.set_defaults(func=snake_case_ )
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) -> str:
UpperCamelCase__ = model
UpperCamelCase__ = cache
UpperCamelCase__ = force
UpperCamelCase__ = trust_remote_code
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
| 20
| 1
|
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> str:
lowercase__ = []
for line in lines:
lowercase__ = re.sub(R'#.*' , '' , _SCREAMING_SNAKE_CASE ) # remove comments
if line:
filtered_lines.append(_SCREAMING_SNAKE_CASE )
lowercase__ = '\n'.join(_SCREAMING_SNAKE_CASE )
# Make a hash from all this code
lowercase__ = full_str.encode('utf-8' )
return shaaaa(_SCREAMING_SNAKE_CASE ).hexdigest()
# get importable module names and hash for caching
lowercase_ = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
lowercase_ = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowercase_ = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
lowercase_ = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
| 235
|
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> Tuple:
"""simple docstring"""
lowercase__ = inspect.getfile(accelerate.test_utils )
lowercase__ = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
lowercase__ = test_metrics
@require_cpu
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> Dict:
"""simple docstring"""
debug_launcher(self.test_metrics.main , num_processes=1 )
@require_cpu
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
debug_launcher(self.test_metrics.main )
@require_single_gpu
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
self.test_metrics.main()
@require_multi_gpu
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> List[Any]:
"""simple docstring"""
print(f"""Found {torch.cuda.device_count()} devices.""" )
lowercase__ = ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(a , env=os.environ.copy() )
| 235
| 1
|
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
from .features import FeatureType
_A : int = False, False, False
@dataclass
class a__ :
'''simple docstring'''
__lowerCAmelCase = None
__lowerCAmelCase = True
__lowerCAmelCase = True
__lowerCAmelCase = None
# Automatically constructed
__lowerCAmelCase = """dict"""
__lowerCAmelCase = pa.struct({"""bytes""": pa.binary(), """path""": pa.string()} )
__lowerCAmelCase = field(default="""Audio""", init=a_, repr=a_ )
def __call__( self ):
return self.pa_type
def __magic_name__ ( self , _a ):
try:
import soundfile as sf # soundfile is a dependency of librosa, needed to decode audio files.
except ImportError as err:
raise ImportError("To support encoding audio data, please install 'soundfile'." ) from err
if isinstance(_a , _a ):
return {"bytes": None, "path": value}
elif isinstance(_a , _a ):
return {"bytes": value, "path": None}
elif "array" in value:
# convert the audio array to wav bytes
lowercase : Tuple = BytesIO()
sf.write(_a , value["array"] , value["sampling_rate"] , format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
if value["path"].endswith("pcm" ):
# "PCM" only has raw audio bytes
if value.get("sampling_rate" ) is None:
# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
raise KeyError("To use PCM files, please specify a 'sampling_rate' in Audio object" )
if value.get("bytes" ):
# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
lowercase : Optional[int] = np.frombuffer(value["bytes"] , dtype=np.intaa ).astype(np.floataa ) / 32_767
else:
lowercase : Optional[Any] = np.memmap(value["path"] , dtype="h" , mode="r" ).astype(np.floataa ) / 32_767
lowercase : List[str] = BytesIO(bytes() )
sf.write(_a , _a , value["sampling_rate"] , format="wav" )
return {"bytes": buffer.getvalue(), "path": None}
else:
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the audio bytes, and path is used to infer the audio format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def __magic_name__ ( self , _a , _a = None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Audio(decode=True) instead." )
lowercase : Optional[int] = (value["path"], BytesIO(value["bytes"] )) if value["bytes"] is not None else (value["path"], None)
if path is None and file is None:
raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""" )
try:
import librosa
import soundfile as sf
except ImportError as err:
raise ImportError("To support decoding audio files, please install 'librosa' and 'soundfile'." ) from err
lowercase : Optional[int] = xsplitext(_a )[1][1:].lower() if path is not None else None
if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
raise RuntimeError(
"Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
raise RuntimeError(
"Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, "
"You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. " )
if file is None:
lowercase : Union[str, Any] = token_per_repo_id or {}
lowercase : Dict = path.split("::" )[-1]
try:
lowercase : Optional[int] = string_to_dict(_a , config.HUB_DATASETS_URL )["repo_id"]
lowercase : Union[str, Any] = token_per_repo_id[repo_id]
except (ValueError, KeyError):
lowercase : List[Any] = None
with xopen(_a , "rb" , use_auth_token=_a ) as f:
lowercase : int = sf.read(_a )
else:
lowercase : Any = sf.read(_a )
lowercase : List[Any] = array.T
if self.mono:
lowercase : List[Any] = librosa.to_mono(_a )
if self.sampling_rate and self.sampling_rate != sampling_rate:
lowercase : Union[str, Any] = librosa.resample(_a , orig_sr=_a , target_sr=self.sampling_rate )
lowercase : List[str] = self.sampling_rate
return {"path": path, "array": array, "sampling_rate": sampling_rate}
def __magic_name__ ( self ):
from .features import Value
if self.decode:
raise ValueError("Cannot flatten a decoded Audio feature." )
return {
"bytes": Value("binary" ),
"path": Value("string" ),
}
def __magic_name__ ( self , _a ):
if pa.types.is_string(storage.type ):
lowercase : List[Any] = pa.array([None] * len(_a ) , type=pa.binary() )
lowercase : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase : List[str] = pa.array([None] * len(_a ) , type=pa.string() )
lowercase : List[Any] = pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ) and storage.type.get_all_field_indices("array" ):
lowercase : str = pa.array([Audio().encode_example(_a ) if x is not None else None for x in storage.to_pylist()] )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
lowercase : Optional[Any] = storage.field("bytes" )
else:
lowercase : Optional[Any] = pa.array([None] * len(_a ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
lowercase : Dict = storage.field("path" )
else:
lowercase : List[Any] = pa.array([None] * len(_a ) , type=pa.string() )
lowercase : Tuple = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
return array_cast(_a , self.pa_type )
def __magic_name__ ( self , _a ):
@no_op_if_value_is_null
def path_to_bytes(_a ):
with xopen(_a , "rb" ) as f:
lowercase : List[str] = f.read()
return bytes_
lowercase : Dict = pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase : int = pa.array(
[os.path.basename(_a ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
lowercase : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(_a , self.pa_type )
| 716
|
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ):
lowercase : List[str] = AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
lowercase : Any = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip" )
model.to(_a )
from datasets import load_dataset
lowercase : Any = load_dataset("nielsr/rvlcdip-demo" )
lowercase : List[str] = dataset["train"][0]["image"].convert("RGB" )
lowercase : str = image_processor(_a , return_tensors="pt" ).to(_a )
# forward pass
with torch.no_grad():
lowercase : Tuple = model(**_a )
lowercase : Dict = outputs.logits
lowercase : Union[str, Any] = torch.Size((1, 16) )
self.assertEqual(logits.shape , _a )
lowercase : int = torch.tensor(
[-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7] , device=_a , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , _a , atol=1E-4 ) )
| 518
| 0
|
from collections.abc import Iterable
from typing import Any
class snake_case_ :
'''simple docstring'''
def __init__( self, A_ = None ) -> Union[str, Any]:
UpperCAmelCase__ =value
UpperCAmelCase__ =None # Added in order to delete a node easier
UpperCAmelCase__ =None
UpperCAmelCase__ =None
def __repr__( self ) -> Any:
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({f"""{self.value}""": (self.left, self.right)}, indent=1 )
class snake_case_ :
'''simple docstring'''
def __init__( self, A_ = None ) -> Optional[Any]:
UpperCAmelCase__ =root
def __str__( self ) -> Optional[int]:
return str(self.root )
def __UpperCAmelCase ( self, A_, A_ ) -> List[str]:
if new_children is not None: # reset its kids
UpperCAmelCase__ =node.parent
if node.parent is not None: # reset its parent
if self.is_right(_UpperCamelCase ): # If it is the right children
UpperCAmelCase__ =new_children
else:
UpperCAmelCase__ =new_children
else:
UpperCAmelCase__ =new_children
def __UpperCAmelCase ( self, A_ ) -> Union[str, Any]:
if node.parent and node.parent.right:
return node == node.parent.right
return False
def __UpperCAmelCase ( self ) -> List[Any]:
return self.root is None
def __UpperCAmelCase ( self, A_ ) -> Union[str, Any]:
UpperCAmelCase__ =Node(_UpperCamelCase ) # create a new Node
if self.empty(): # if Tree is empty
UpperCAmelCase__ =new_node # set its root
else: # Tree is not empty
UpperCAmelCase__ =self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
UpperCAmelCase__ =new_node # We insert the new node in a leaf
break
else:
UpperCAmelCase__ =parent_node.left
else:
if parent_node.right is None:
UpperCAmelCase__ =new_node
break
else:
UpperCAmelCase__ =parent_node.right
UpperCAmelCase__ =parent_node
def __UpperCAmelCase ( self, *A_ ) -> List[str]:
for value in values:
self.__insert(_UpperCamelCase )
def __UpperCAmelCase ( self, A_ ) -> int:
if self.empty():
raise IndexError("Warning: Tree is empty! please use another." )
else:
UpperCAmelCase__ =self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
UpperCAmelCase__ =node.left if value < node.value else node.right
return node
def __UpperCAmelCase ( self, A_ = None ) -> Optional[Any]:
if node is None:
if self.root is None:
return None
UpperCAmelCase__ =self.root
if not self.empty():
while node.right is not None:
UpperCAmelCase__ =node.right
return node
def __UpperCAmelCase ( self, A_ = None ) -> List[Any]:
if node is None:
UpperCAmelCase__ =self.root
if self.root is None:
return None
if not self.empty():
UpperCAmelCase__ =self.root
while node.left is not None:
UpperCAmelCase__ =node.left
return node
def __UpperCAmelCase ( self, A_ ) -> Optional[Any]:
UpperCAmelCase__ =self.search(_UpperCamelCase ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(_UpperCamelCase, _UpperCamelCase )
elif node.left is None: # Has only right children
self.__reassign_nodes(_UpperCamelCase, node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(_UpperCamelCase, node.left )
else:
UpperCAmelCase__ =self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
UpperCAmelCase__ =(
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def __UpperCAmelCase ( self, A_ ) -> Union[str, Any]:
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def __UpperCAmelCase ( self, A_=None ) -> List[str]:
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def __UpperCAmelCase ( self, A_, A_ ) -> str:
if node:
self.inorder(_UpperCamelCase, node.left )
arr.append(node.value )
self.inorder(_UpperCamelCase, node.right )
def __UpperCAmelCase ( self, A_, A_ ) -> Tuple:
UpperCAmelCase__ =[]
self.inorder(_UpperCamelCase, _UpperCamelCase ) # append all values to list using inorder traversal
return arr[k - 1]
def _UpperCAmelCase ( A ):
'''simple docstring'''
UpperCAmelCase__ =[]
if curr_node is not None:
UpperCAmelCase__ =postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def _UpperCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ =(8, 3, 6, 1, 10, 14, 13, 4, 7)
UpperCAmelCase__ =BinarySearchTree()
for i in testlist:
t.insert(SCREAMING_SNAKE_CASE_ )
# Prints all the elements of the list in order traversal
print(SCREAMING_SNAKE_CASE_ )
if t.search(6 ) is not None:
print("The value 6 exists" )
else:
print("The value 6 doesn\'t exist" )
if t.search(-1 ) is not None:
print("The value -1 exists" )
else:
print("The value -1 doesn\'t exist" )
if not t.empty():
print("Max Value: " , t.get_max().value ) # type: ignore
print("Min Value: " , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(SCREAMING_SNAKE_CASE_ )
print(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 625
|
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> bool:
"""simple docstring"""
_UpperCAmelCase = str(SCREAMING_SNAKE_CASE_ )
return len(SCREAMING_SNAKE_CASE_ ) == 9 and set(SCREAMING_SNAKE_CASE_ ) == set('''123456789''' )
def A__ ( ) -> int | None:
"""simple docstring"""
for base_num in range(99_99 , 49_99 , -1 ):
_UpperCAmelCase = 10_00_02 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
for base_num in range(3_33 , 99 , -1 ):
_UpperCAmelCase = 1_00_20_03 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE_ ):
return candidate
return None
if __name__ == "__main__":
print(f'''{solution() = }''')
| 32
| 0
|
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"""artists_file""": """artists.json""",
"""lyrics_file""": """lyrics.json""",
"""genres_file""": """genres.json""",
}
UpperCamelCase = {
"""artists_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
},
"""genres_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
},
"""lyrics_file""": {
"""jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
},
}
UpperCamelCase = {
"""jukebox""": 512,
}
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
snake_case = VOCAB_FILES_NAMES
snake_case = PRETRAINED_VOCAB_FILES_MAP
snake_case = PRETRAINED_LYRIC_TOKENS_SIZES
snake_case = ["input_ids", "attention_mask"]
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=["v3", "v2", "v2"] , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE="<|endoftext|>" , **_SCREAMING_SNAKE_CASE , )->Any:
'''simple docstring'''
A_ : Union[str, Any] = AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else unk_token
super().__init__(
unk_token=_SCREAMING_SNAKE_CASE , n_genres=_SCREAMING_SNAKE_CASE , version=_SCREAMING_SNAKE_CASE , max_n_lyric_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
A_ : List[str] = version
A_ : str = max_n_lyric_tokens
A_ : Optional[int] = n_genres
with open(_SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as vocab_handle:
A_ : Any = json.load(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as vocab_handle:
A_ : Union[str, Any] = json.load(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , encoding='''utf-8''' ) as vocab_handle:
A_ : Tuple = json.load(_SCREAMING_SNAKE_CASE )
A_ : str = R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
if len(self.lyrics_encoder ) == 79:
A_ : Any = oov.replace(R'''\-\'''' , R'''\-+\'''' )
A_ : List[Any] = regex.compile(_SCREAMING_SNAKE_CASE )
A_ : Any = {v: k for k, v in self.artists_encoder.items()}
A_ : List[Any] = {v: k for k, v in self.genres_encoder.items()}
A_ : Tuple = {v: k for k, v in self.lyrics_encoder.items()}
@property
def _snake_case ( self )->str:
'''simple docstring'''
return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )
def _snake_case ( self )->str:
'''simple docstring'''
return dict(self.artists_encoder , self.genres_encoder , self.lyrics_encoder )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->Tuple:
'''simple docstring'''
A_ : List[str] = [self.artists_encoder.get(_SCREAMING_SNAKE_CASE , 0 ) for artist in list_artists]
for genres in range(len(_SCREAMING_SNAKE_CASE ) ):
A_ : Any = [self.genres_encoder.get(_SCREAMING_SNAKE_CASE , 0 ) for genre in list_genres[genres]]
A_ : Tuple = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))
A_ : int = [[self.lyrics_encoder.get(_SCREAMING_SNAKE_CASE , 0 ) for character in list_lyrics[0]], [], []]
return artists_id, list_genres, lyric_ids
def _snake_case ( self , _SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
return list(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )->Dict:
'''simple docstring'''
A_ , A_ , A_ : int = self.prepare_for_tokenization(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : List[Any] = self._tokenize(_SCREAMING_SNAKE_CASE )
return artist, genre, lyrics
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False )->Tuple[str, str, str, Dict[str, Any]]:
'''simple docstring'''
for idx in range(len(self.version ) ):
if self.version[idx] == "v3":
A_ : Tuple = artists[idx].lower()
A_ : int = [genres[idx].lower()]
else:
A_ : Tuple = self._normalize(artists[idx] ) + '''.v2'''
A_ : int = [
self._normalize(_SCREAMING_SNAKE_CASE ) + '''.v2''' for genre in genres[idx].split('''_''' )
] # split is for the full dictionary with combined genres
if self.version[0] == "v2":
A_ : Optional[int] = regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+''' )
A_ : Tuple = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
A_ : str = {vocab[index]: index + 1 for index in range(len(_SCREAMING_SNAKE_CASE ) )}
A_ : Optional[int] = 0
A_ : Tuple = len(_SCREAMING_SNAKE_CASE ) + 1
A_ : List[Any] = self.vocab
A_ : Dict = {v: k for k, v in self.vocab.items()}
A_ : Dict = ''''''
else:
A_ : Optional[Any] = regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+''' )
A_ : List[Any] = self._run_strip_accents(_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = lyrics.replace('''\\''' , '''\n''' )
A_ : Dict = self.out_of_vocab.sub('''''' , _SCREAMING_SNAKE_CASE ), [], []
return artists, genres, lyrics
def _snake_case ( self , _SCREAMING_SNAKE_CASE )->Optional[int]:
'''simple docstring'''
A_ : Tuple = unicodedata.normalize('''NFD''' , _SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = []
for char in text:
A_ : int = unicodedata.category(_SCREAMING_SNAKE_CASE )
if cat == "Mn":
continue
output.append(_SCREAMING_SNAKE_CASE )
return "".join(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE )->str:
'''simple docstring'''
A_ : int = (
[chr(_SCREAMING_SNAKE_CASE ) for i in range(ord('''a''' ) , ord('''z''' ) + 1 )]
+ [chr(_SCREAMING_SNAKE_CASE ) for i in range(ord('''A''' ) , ord('''Z''' ) + 1 )]
+ [chr(_SCREAMING_SNAKE_CASE ) for i in range(ord('''0''' ) , ord('''9''' ) + 1 )]
+ ['''.''']
)
A_ : Optional[int] = frozenset(_SCREAMING_SNAKE_CASE )
A_ : int = re.compile(R'''_+''' )
A_ : str = ''''''.join([c if c in accepted else '''_''' for c in text.lower()] )
A_ : str = pattern.sub('''_''' , _SCREAMING_SNAKE_CASE ).strip('''_''' )
return text
def _snake_case ( self , _SCREAMING_SNAKE_CASE )->str:
'''simple docstring'''
return " ".join(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False )->int:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : Union[str, Any] = TensorType(_SCREAMING_SNAKE_CASE )
# Get a function reference for the correct framework
if tensor_type == TensorType.TENSORFLOW:
if not is_tf_available():
raise ImportError(
'''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.''' )
import tensorflow as tf
A_ : List[Any] = tf.constant
A_ : Union[str, Any] = tf.is_tensor
elif tensor_type == TensorType.PYTORCH:
if not is_torch_available():
raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.''' )
import torch
A_ : Any = torch.tensor
A_ : List[str] = torch.is_tensor
elif tensor_type == TensorType.JAX:
if not is_flax_available():
raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.''' )
import jax.numpy as jnp # noqa: F811
A_ : List[str] = jnp.array
A_ : Optional[int] = _is_jax
else:
A_ : Optional[Any] = np.asarray
A_ : Union[str, Any] = _is_numpy
# Do the tensor conversion in batch
try:
if prepend_batch_axis:
A_ : Tuple = [inputs]
if not is_tensor(_SCREAMING_SNAKE_CASE ):
A_ : Union[str, Any] = as_tensor(_SCREAMING_SNAKE_CASE )
except: # noqa E722
raise ValueError(
'''Unable to create tensor, you should probably activate truncation and/or padding '''
'''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.''' )
return inputs
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="" , _SCREAMING_SNAKE_CASE="pt" )->BatchEncoding:
'''simple docstring'''
A_ : Dict = [0, 0, 0]
A_ : Dict = [artist] * len(self.version )
A_ : str = [genres] * len(self.version )
A_ , A_ , A_ : Union[str, Any] = self.tokenize(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ , A_ , A_ : str = self._convert_token_to_id(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
A_ : Optional[int] = [-INFINITY] * len(full_tokens[-1] )
A_ : int = [
self.convert_to_tensors(
[input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] , tensor_type=_SCREAMING_SNAKE_CASE )
for i in range(len(self.version ) )
]
return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks} )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None )->Tuple[str]:
'''simple docstring'''
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
A_ : Dict = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file'''] )
with open(_SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.artists_encoder , ensure_ascii=_SCREAMING_SNAKE_CASE ) )
A_ : int = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file'''] )
with open(_SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.genres_encoder , ensure_ascii=_SCREAMING_SNAKE_CASE ) )
A_ : int = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file'''] )
with open(_SCREAMING_SNAKE_CASE , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.lyrics_encoder , ensure_ascii=_SCREAMING_SNAKE_CASE ) )
return (artists_file, genres_file, lyrics_file)
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->List[str]:
'''simple docstring'''
A_ : Union[str, Any] = self.artists_decoder.get(_SCREAMING_SNAKE_CASE )
A_ : Tuple = [self.genres_decoder.get(_SCREAMING_SNAKE_CASE ) for genre in genres_index]
A_ : List[str] = [self.lyrics_decoder.get(_SCREAMING_SNAKE_CASE ) for character in lyric_index]
return artist, genres, lyrics
| 152
|
from functools import reduce
UpperCamelCase = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = N ):
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str(int(SCREAMING_SNAKE_CASE ) * int(SCREAMING_SNAKE_CASE ) ) , n[i : i + 13] ) )
for i in range(len(SCREAMING_SNAKE_CASE ) - 12 ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 152
| 1
|
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'''stable diffusion controlnet''',
'''0.22.0''',
'''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
standard_warn=False,
stacklevel=3,
)
| 14
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 UpperCAmelCase_ ( __lowercase , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = KandinskyInpaintPipeline
UpperCAmelCase__ : Optional[int] = ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
UpperCAmelCase__ : Optional[Any] = [
"prompt",
"negative_prompt",
"image_embeds",
"negative_image_embeds",
"image",
"mask_image",
]
UpperCAmelCase__ : Optional[int] = [
"generator",
"height",
"width",
"latents",
"guidance_scale",
"negative_prompt",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
UpperCAmelCase__ : Any = False
@property
def __lowercase ( self ) -> Optional[int]:
return 3_2
@property
def __lowercase ( self ) -> int:
return 3_2
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim
@property
def __lowercase ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __lowercase ( self ) -> Optional[Any]:
return 1_0_0
@property
def __lowercase ( self ) -> Optional[Any]:
_a : Any = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
return tokenizer
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[Any] = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
_a : Optional[int] = MultilingualCLIP(_a )
_a : Tuple = text_encoder.eval()
return text_encoder
@property
def __lowercase ( self ) -> str:
torch.manual_seed(0 )
_a : List[str] = {
'''in_channels''': 9,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''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''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_a : Dict = UNetaDConditionModel(**_a )
return model
@property
def __lowercase ( self ) -> Optional[int]:
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowercase ( self ) -> Tuple:
torch.manual_seed(0 )
_a : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowercase ( self ) -> Any:
_a : List[Any] = self.dummy_text_encoder
_a : Optional[Any] = self.dummy_tokenizer
_a : Optional[Any] = self.dummy_unet
_a : Union[str, Any] = self.dummy_movq
_a : Tuple = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule='''linear''' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_a , set_alpha_to_one=_a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=_a , )
_a : str = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def __lowercase ( self , _a , _a=0 ) -> int:
_a : Union[str, Any] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(_a ) ).to(_a )
_a : List[str] = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(_a )
# create init_image
_a : Tuple = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(_a ) ).to(_a )
_a : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_a : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert('''RGB''' ).resize((2_5_6, 2_5_6) )
# create mask
_a : Union[str, Any] = np.ones((6_4, 6_4) , dtype=np.floataa )
_a : List[str] = 0
if str(_a ).startswith('''mps''' ):
_a : Tuple = torch.manual_seed(_a )
else:
_a : Any = torch.Generator(device=_a ).manual_seed(_a )
_a : Any = {
'''prompt''': '''horse''',
'''image''': init_image,
'''mask_image''': mask,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''num_inference_steps''': 2,
'''guidance_scale''': 4.0,
'''output_type''': '''np''',
}
return inputs
def __lowercase ( self ) -> Optional[Any]:
_a : Optional[Any] = '''cpu'''
_a : List[Any] = self.get_dummy_components()
_a : Tuple = self.pipeline_class(**_a )
_a : int = pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
_a : Any = pipe(**self.get_dummy_inputs(_a ) )
_a : str = output.images
_a : Tuple = pipe(
**self.get_dummy_inputs(_a ) , return_dict=_a , )[0]
_a : Union[str, Any] = image[0, -3:, -3:, -1]
_a : Tuple = image_from_tuple[0, -3:, -3:, -1]
print(F"""image.shape {image.shape}""" )
assert image.shape == (1, 6_4, 6_4, 3)
_a : str = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
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()}"""
def __lowercase ( self ) -> Dict:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __lowercase ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self ) -> Union[str, Any]:
_a : Tuple = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy''' )
_a : str = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
_a : Tuple = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
_a : Any = 0
_a : Optional[Any] = '''a hat'''
_a : Optional[Any] = KandinskyPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(_a )
_a : Tuple = KandinskyInpaintPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-1-inpaint''' , torch_dtype=torch.floataa )
_a : Union[str, Any] = pipeline.to(_a )
pipeline.set_progress_bar_config(disable=_a )
_a : Union[str, Any] = torch.Generator(device='''cpu''' ).manual_seed(0 )
_a , _a : Dict = pipe_prior(
_a , generator=_a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
_a : Optional[int] = pipeline(
_a , image=_a , mask_image=_a , image_embeds=_a , negative_image_embeds=_a , generator=_a , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type='''np''' , )
_a : Optional[int] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(_a , _a )
| 14
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LayoutLMv3Config',
'LayoutLMv3OnnxConfig',
],
'processing_layoutlmv3': ['LayoutLMv3Processor'],
'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['LayoutLMv3TokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'LayoutLMv3ForQuestionAnswering',
'LayoutLMv3ForSequenceClassification',
'LayoutLMv3ForTokenClassification',
'LayoutLMv3Model',
'LayoutLMv3PreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFLayoutLMv3ForQuestionAnswering',
'TFLayoutLMv3ForSequenceClassification',
'TFLayoutLMv3ForTokenClassification',
'TFLayoutLMv3Model',
'TFLayoutLMv3PreTrainedModel',
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['LayoutLMv3FeatureExtractor']
_lowerCamelCase = ['LayoutLMv3ImageProcessor']
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 613
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCamelCase = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP',
'RobertaPreLayerNormConfig',
'RobertaPreLayerNormOnnxConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
'RobertaPreLayerNormForCausalLM',
'RobertaPreLayerNormForMaskedLM',
'RobertaPreLayerNormForMultipleChoice',
'RobertaPreLayerNormForQuestionAnswering',
'RobertaPreLayerNormForSequenceClassification',
'RobertaPreLayerNormForTokenClassification',
'RobertaPreLayerNormModel',
'RobertaPreLayerNormPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFRobertaPreLayerNormForCausalLM',
'TFRobertaPreLayerNormForMaskedLM',
'TFRobertaPreLayerNormForMultipleChoice',
'TFRobertaPreLayerNormForQuestionAnswering',
'TFRobertaPreLayerNormForSequenceClassification',
'TFRobertaPreLayerNormForTokenClassification',
'TFRobertaPreLayerNormMainLayer',
'TFRobertaPreLayerNormModel',
'TFRobertaPreLayerNormPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'FlaxRobertaPreLayerNormForCausalLM',
'FlaxRobertaPreLayerNormForMaskedLM',
'FlaxRobertaPreLayerNormForMultipleChoice',
'FlaxRobertaPreLayerNormForQuestionAnswering',
'FlaxRobertaPreLayerNormForSequenceClassification',
'FlaxRobertaPreLayerNormForTokenClassification',
'FlaxRobertaPreLayerNormModel',
'FlaxRobertaPreLayerNormPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 613
| 1
|
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ : Any = logging.get_logger(__name__)
def __lowerCamelCase ( A__ : int , A__ : List[str]=False ) -> Optional[Any]:
lowerCamelCase_ : List[Any] = []
# fmt: off
# stem:
rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""") )
rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""") )
rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""") )
rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""") )
# backbone
rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""") )
rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""") )
rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""") )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
("""pre_logits.fc.weight""", """pooler.dense.weight"""),
("""pre_logits.fc.bias""", """pooler.dense.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowerCamelCase_ : List[Any] = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
# fmt: on
return rename_keys
def __lowerCamelCase ( A__ : Tuple , A__ : str , A__ : Any=False ) -> str:
for i in range(config.num_hidden_layers ):
if base_model:
lowerCamelCase_ : List[Any] = """"""
else:
lowerCamelCase_ : List[str] = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowerCamelCase_ : Optional[int] = state_dict.pop(f'''blocks.{i}.attn.qkv.weight''' )
lowerCamelCase_ : List[str] = state_dict.pop(f'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowerCamelCase_ : List[str] = in_proj_weight[
: config.hidden_size, :
]
lowerCamelCase_ : int = in_proj_bias[: config.hidden_size]
lowerCamelCase_ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowerCamelCase_ : str = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowerCamelCase_ : Any = in_proj_weight[
-config.hidden_size :, :
]
lowerCamelCase_ : List[str] = in_proj_bias[-config.hidden_size :]
def __lowerCamelCase ( A__ : Tuple ) -> Optional[int]:
lowerCamelCase_ : List[Any] = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
def __lowerCamelCase ( A__ : Union[str, Any] , A__ : Optional[Any] , A__ : Optional[Any] ) -> str:
lowerCamelCase_ : Optional[int] = dct.pop(_lowerCAmelCase )
lowerCamelCase_ : List[Any] = val
def __lowerCamelCase ( ) -> Optional[int]:
lowerCamelCase_ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCamelCase_ : Optional[Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw )
return im
@torch.no_grad()
def __lowerCamelCase ( A__ : Union[str, Any] , A__ : Tuple , A__ : Tuple=False ) -> int:
lowerCamelCase_ : Tuple = BitConfig(
global_padding="""same""" , layer_type="""bottleneck""" , depths=(3, 4, 9) , out_features=["""stage3"""] , embedding_dynamic_padding=_lowerCAmelCase , )
lowerCamelCase_ : Union[str, Any] = ViTHybridConfig(backbone_config=_lowerCAmelCase , image_size=384 , num_labels=1000 )
lowerCamelCase_ : Tuple = False
# load original model from timm
lowerCamelCase_ : int = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowerCamelCase_ : Tuple = timm_model.state_dict()
if base_model:
remove_classification_head_(_lowerCAmelCase )
lowerCamelCase_ : int = create_rename_keys(_lowerCAmelCase , _lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
lowerCamelCase_ : List[str] = """huggingface/label-files"""
lowerCamelCase_ : Any = """imagenet-1k-id2label.json"""
lowerCamelCase_ : int = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="""dataset""" ) , """r""" ) )
lowerCamelCase_ : List[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
lowerCamelCase_ : Optional[int] = idalabel
lowerCamelCase_ : Optional[Any] = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
lowerCamelCase_ : Any = ViTHybridModel(_lowerCAmelCase ).eval()
else:
lowerCamelCase_ : int = ViTHybridForImageClassification(_lowerCAmelCase ).eval()
model.load_state_dict(_lowerCAmelCase )
# create image processor
lowerCamelCase_ : List[Any] = create_transform(**resolve_data_config({} , model=_lowerCAmelCase ) )
lowerCamelCase_ : Tuple = transform.transforms
lowerCamelCase_ : int = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
lowerCamelCase_ : Dict = ViTHybridImageProcessor(
do_resize=_lowerCAmelCase , size={"""shortest_edge""": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCAmelCase , crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]} , do_normalize=_lowerCAmelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowerCamelCase_ : str = prepare_img()
lowerCamelCase_ : Union[str, Any] = transform(_lowerCAmelCase ).unsqueeze(0 )
lowerCamelCase_ : Union[str, Any] = processor(_lowerCAmelCase , return_tensors="""pt""" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase )
# verify logits
with torch.no_grad():
lowerCamelCase_ : Dict = model(_lowerCAmelCase )
lowerCamelCase_ : Optional[int] = outputs.logits
print("""Predicted class:""" , logits.argmax(-1 ).item() )
if base_model:
lowerCamelCase_ : Optional[Any] = timm_model.forward_features(_lowerCAmelCase )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(_lowerCAmelCase , outputs.pooler_output , atol=1e-3 )
else:
lowerCamelCase_ : int = timm_model(_lowerCAmelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCAmelCase , outputs.logits , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(_lowerCAmelCase )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(_lowerCAmelCase )
if push_to_hub:
print(f'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(f'''ybelkada/{vit_name}''' )
processor.push_to_hub(f'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
snake_case__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--vit_name',
default='vit_base_r50_s16_384',
type=str,
help='Name of the hybrid ViT timm model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
snake_case__ : List[Any] = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 278
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCAmelCase ={
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
"GroupViTTextConfig",
"GroupViTVisionConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase =[
"GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"GroupViTModel",
"GroupViTPreTrainedModel",
"GroupViTTextModel",
"GroupViTVisionModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase =[
"TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFGroupViTModel",
"TFGroupViTPreTrainedModel",
"TFGroupViTTextModel",
"TFGroupViTVisionModel",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
__lowerCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 333
| 0
|
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class a__ ( snake_case__ ):
def __init__( self , _A , _A=1_3 , _A=7 , _A=True , _A=True , _A=True , _A=True , _A=9_9 , _A=3_2 , _A=5 , _A=4 , _A=3_7 , _A="gelu" , _A=0.1 , _A=0.1 , _A=5_1_2 , _A=1_6 , _A=2 , _A=0.02 , _A=False , _A=True , _A="None" , _A=3 , _A=4 , _A=None , ):
"""simple docstring"""
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_input_mask
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_labels
__lowerCAmelCase = num_choices
__lowerCAmelCase = relative_attention
__lowerCAmelCase = position_biased_input
__lowerCAmelCase = pos_att_type
__lowerCAmelCase = scope
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_input_mask:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__lowerCAmelCase = None
if self.use_token_type_ids:
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def __SCREAMING_SNAKE_CASE( self , _A ):
"""simple docstring"""
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaModel(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , attention_mask=_A , token_type_ids=_A )[0]
__lowerCAmelCase = model(_A , token_type_ids=_A )[0]
__lowerCAmelCase = model(_A )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaForMaskedLM(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = DebertaVaForSequenceClassification(_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(_A )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = self.num_labels
__lowerCAmelCase = DebertaVaForTokenClassification(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaForQuestionAnswering(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = model(
_A , attention_mask=_A , token_type_ids=_A , start_positions=_A , end_positions=_A , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __SCREAMING_SNAKE_CASE( self , _A , _A , _A , _A , _A , _A , _A ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaForMultipleChoice(config=_A )
model.to(_A )
model.eval()
__lowerCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowerCAmelCase = model(
_A , attention_mask=_A , token_type_ids=_A , labels=_A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class a__ ( snake_case__ , snake_case__ , unittest.TestCase ):
_a : Dict = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
_a : List[Any] = (
{
"""feature-extraction""": DebertaVaModel,
"""fill-mask""": DebertaVaForMaskedLM,
"""question-answering""": DebertaVaForQuestionAnswering,
"""text-classification""": DebertaVaForSequenceClassification,
"""token-classification""": DebertaVaForTokenClassification,
"""zero-shot""": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
_a : Optional[int] = True
_a : Union[str, Any] = False
_a : Tuple = False
_a : int = False
_a : str = False
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=_A , hidden_size=3_7 )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*_A )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*_A )
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = DebertaVaModel.from_pretrained(_A )
self.assertIsNotNone(_A )
@require_torch
@require_sentencepiece
@require_tokenizers
class a__ ( unittest.TestCase ):
@unittest.skip(reason="Model not available yet" )
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
pass
@slow
def __SCREAMING_SNAKE_CASE( self ):
"""simple docstring"""
__lowerCAmelCase = DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge" )
__lowerCAmelCase = torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] )
__lowerCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowerCAmelCase = model(_A , attention_mask=_A )[0]
# compare the actual values for a slice.
__lowerCAmelCase = torch.tensor(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _A , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 700
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
"""tokenization_ctrl""": ["""CTRLTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CTRLForSequenceClassification""",
"""CTRLLMHeadModel""",
"""CTRLModel""",
"""CTRLPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCTRLForSequenceClassification""",
"""TFCTRLLMHeadModel""",
"""TFCTRLModel""",
"""TFCTRLPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .tokenization_ctrl import CTRLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_ctrl import (
CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
CTRLForSequenceClassification,
CTRLLMHeadModel,
CTRLModel,
CTRLPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_ctrl import (
TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCTRLForSequenceClassification,
TFCTRLLMHeadModel,
TFCTRLModel,
TFCTRLPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 552
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowerCamelCase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 95
|
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
class UpperCamelCase_ :
__magic_name__ = 42
__magic_name__ = None
@staticmethod
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
raise NotImplementedError
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : int , lowerCAmelCase_ : str , **lowerCAmelCase_ : Union[str, Any] ) -> Optional[Any]:
raise NotImplementedError
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : Union[str, Any] ) -> str:
raise NotImplementedError
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict:
if not self.is_available():
raise RuntimeError(
f"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Tuple ) -> List[str]:
return f"""`pip install {cls.pip_package or cls.name}`"""
class UpperCamelCase_ (__A ):
__magic_name__ = '''optuna'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( ) -> List[str]:
return is_optuna_available()
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , **lowerCAmelCase_ : Tuple ) -> int:
return run_hp_search_optuna(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : Dict ) -> Optional[Any]:
return default_hp_space_optuna(lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = '''ray'''
__magic_name__ = '''\'ray[tune]\''''
@staticmethod
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
return is_ray_available()
def _SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : str , **lowerCAmelCase_ : Tuple ) -> List[str]:
return run_hp_search_ray(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase_ : Tuple ) -> Optional[int]:
return default_hp_space_ray(lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = '''sigopt'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( ) -> List[Any]:
return is_sigopt_available()
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase_ : str , lowerCAmelCase_ : int , lowerCAmelCase_ : str , **lowerCAmelCase_ : str ) -> str:
return run_hp_search_sigopt(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase_ : Any ) -> Any:
return default_hp_space_sigopt(lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = '''wandb'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( ) -> int:
return is_wandb_available()
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : int , lowerCAmelCase_ : str , **lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
return run_hp_search_wandb(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : List[str] ) -> Any:
return default_hp_space_wandb(lowerCAmelCase_ )
lowerCamelCase_ = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def snake_case ( ):
UpperCAmelCase_ : List[str] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(A__ ) > 0:
UpperCAmelCase_ : Optional[Any] = available_backends[0].name
if len(A__ ) > 1:
logger.info(
F"""{len(A__ )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
F""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 95
| 1
|
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class lowercase__( snake_case__ ):
'''simple docstring'''
snake_case__ = 4_2
snake_case__ = jnp.floataa
snake_case__ = True
def UpperCAmelCase ( self) -> str:
"""simple docstring"""
super().setup()
UpperCamelCase__ : str =nn.Dense(5 , dtype=self.dtype)
def __call__( self , *__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE) -> str:
"""simple docstring"""
UpperCamelCase__ : Optional[int] =super().__call__(*__A , **__A)
UpperCamelCase__ : int =self.cls(outputs[2])
return outputs[:2] + (cls_out,)
class lowercase__( snake_case__ ):
'''simple docstring'''
snake_case__ = FlaxBigBirdForNaturalQuestionsModule
def _lowerCamelCase ( A_ : Optional[Any] , A_ : Dict , A_ : int , A_ : Optional[int] , A_ : Tuple , A_ : Dict ) -> Optional[Any]:
'''simple docstring'''
def cross_entropy(A_ : Dict , A_ : str , A_ : Tuple=None ):
UpperCamelCase__ : Optional[int] =logits.shape[-1]
UpperCamelCase__ : Optional[Any] =(labels[..., None] == jnp.arange(_lowerCAmelCase )[None]).astype("f4" )
UpperCamelCase__ : str =jax.nn.log_softmax(_lowerCAmelCase , axis=-1 )
UpperCamelCase__ : int =-jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
UpperCamelCase__ : List[Any] =reduction(_lowerCAmelCase )
return loss
UpperCamelCase__ : int =partial(_lowerCAmelCase , reduction=jnp.mean )
UpperCamelCase__ : List[Any] =cross_entropy(_lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase__ : Union[str, Any] =cross_entropy(_lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase__ : Optional[int] =cross_entropy(_lowerCAmelCase , _lowerCAmelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class lowercase__:
'''simple docstring'''
snake_case__ = '''google/bigbird-roberta-base'''
snake_case__ = 3_0_0_0
snake_case__ = 1_0_5_0_0
snake_case__ = 1_2_8
snake_case__ = 3
snake_case__ = 1
snake_case__ = 5
# tx_args
snake_case__ = 3E-5
snake_case__ = 0.0
snake_case__ = 2_0_0_0_0
snake_case__ = 0.0095
snake_case__ = '''bigbird-roberta-natural-questions'''
snake_case__ = '''training-expt'''
snake_case__ = '''data/nq-training.jsonl'''
snake_case__ = '''data/nq-validation.jsonl'''
def UpperCAmelCase ( self) -> int:
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=__A)
UpperCamelCase__ : Union[str, Any] =os.path.join(self.base_dir , self.save_dir)
UpperCamelCase__ : Any =self.batch_size_per_device * jax.device_count()
@dataclass
class lowercase__:
'''simple docstring'''
snake_case__ = 4_2
snake_case__ = 4_0_9_6 # no dynamic padding on TPUs
def __call__( self , __SCREAMING_SNAKE_CASE) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Dict =self.collate_fn(__A)
UpperCamelCase__ : int =jax.tree_util.tree_map(__A , __A)
return batch
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> str:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] =self.fetch_inputs(features["input_ids"])
UpperCamelCase__ : Dict ={
"input_ids": jnp.array(__A , dtype=jnp.intaa),
"attention_mask": jnp.array(__A , dtype=jnp.intaa),
"start_labels": jnp.array(features["start_token"] , dtype=jnp.intaa),
"end_labels": jnp.array(features["end_token"] , dtype=jnp.intaa),
"pooled_labels": jnp.array(features["category"] , dtype=jnp.intaa),
}
return batch
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> int:
"""simple docstring"""
UpperCamelCase__ : str =[self._fetch_inputs(__A) for ids in input_ids]
return zip(*__A)
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase__ : str =[1 for _ in range(len(__A))]
while len(__A) < self.max_length:
input_ids.append(self.pad_id)
attention_mask.append(0)
return input_ids, attention_mask
def _lowerCamelCase ( A_ : Any , A_ : List[str] , A_ : Dict=None ) -> Optional[int]:
'''simple docstring'''
if seed is not None:
UpperCamelCase__ : Union[str, Any] =dataset.shuffle(seed=_lowerCAmelCase )
for i in range(len(_lowerCAmelCase ) // batch_size ):
UpperCamelCase__ : Any =dataset[i * batch_size : (i + 1) * batch_size]
yield dict(_lowerCAmelCase )
@partial(jax.pmap , axis_name="batch" )
def _lowerCamelCase ( A_ : Union[str, Any] , A_ : List[str] , **A_ : Optional[int] ) -> Optional[int]:
'''simple docstring'''
def loss_fn(A_ : str ):
UpperCamelCase__ : List[Any] =model_inputs.pop("start_labels" )
UpperCamelCase__ : Union[str, Any] =model_inputs.pop("end_labels" )
UpperCamelCase__ : Optional[int] =model_inputs.pop("pooled_labels" )
UpperCamelCase__ : List[str] =state.apply_fn(**_lowerCAmelCase , params=_lowerCAmelCase , dropout_rng=_lowerCAmelCase , train=_lowerCAmelCase )
UpperCamelCase__ : Optional[Any] =outputs
return state.loss_fn(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )
UpperCamelCase__ : List[Any] =jax.random.split(_lowerCAmelCase )
UpperCamelCase__ : Tuple =jax.value_and_grad(_lowerCAmelCase )
UpperCamelCase__ : int =grad_fn(state.params )
UpperCamelCase__ : Optional[Any] =jax.lax.pmean({"loss": loss} , axis_name="batch" )
UpperCamelCase__ : Union[str, Any] =jax.lax.pmean(_lowerCAmelCase , "batch" )
UpperCamelCase__ : Union[str, Any] =state.apply_gradients(grads=_lowerCAmelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="batch" )
def _lowerCamelCase ( A_ : int , **A_ : Tuple ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] =model_inputs.pop("start_labels" )
UpperCamelCase__ : Any =model_inputs.pop("end_labels" )
UpperCamelCase__ : Any =model_inputs.pop("pooled_labels" )
UpperCamelCase__ : Optional[int] =state.apply_fn(**_lowerCAmelCase , params=state.params , train=_lowerCAmelCase )
UpperCamelCase__ : Dict =outputs
UpperCamelCase__ : List[str] =state.loss_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase__ : Union[str, Any] =jax.lax.pmean({"loss": loss} , axis_name="batch" )
return metrics
class lowercase__( train_state.TrainState ):
'''simple docstring'''
snake_case__ = struct.field(pytree_node=snake_case__ )
@dataclass
class lowercase__:
'''simple docstring'''
snake_case__ = 4_2
snake_case__ = 4_2
snake_case__ = 4_2
snake_case__ = 4_2
snake_case__ = 4_2
snake_case__ = 4_2
snake_case__ = None
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None) -> Any:
"""simple docstring"""
UpperCamelCase__ : Optional[Any] =model.params
UpperCamelCase__ : List[Any] =TrainState.create(
apply_fn=model.__call__ , params=__A , tx=__A , loss_fn=__A , )
if ckpt_dir is not None:
UpperCamelCase__ : List[Any] =restore_checkpoint(__A , __A)
UpperCamelCase__ : List[str] ={
"lr": args.lr,
"init_lr": args.init_lr,
"warmup_steps": args.warmup_steps,
"num_train_steps": num_train_steps,
"weight_decay": args.weight_decay,
}
UpperCamelCase__ : Any =build_tx(**__A)
UpperCamelCase__ : int =train_state.TrainState(
step=__A , apply_fn=model.__call__ , params=__A , tx=__A , opt_state=__A , )
UpperCamelCase__ : int =args
UpperCamelCase__ : str =data_collator
UpperCamelCase__ : Tuple =lr
UpperCamelCase__ : Tuple =params
UpperCamelCase__ : Tuple =jax_utils.replicate(__A)
return state
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase__ : str =self.args
UpperCamelCase__ : Union[str, Any] =len(__A) // args.batch_size
UpperCamelCase__ : Dict =jax.random.PRNGKey(0)
UpperCamelCase__ : Optional[int] =jax.random.split(__A , jax.device_count())
for epoch in range(args.max_epochs):
UpperCamelCase__ : List[str] =jnp.array(0 , dtype=jnp.floataa)
UpperCamelCase__ : Any =get_batched_dataset(__A , args.batch_size , seed=__A)
UpperCamelCase__ : Union[str, Any] =0
for batch in tqdm(__A , total=__A , desc=F'''Running EPOCH-{epoch}'''):
UpperCamelCase__ : Optional[Any] =self.data_collator(__A)
UpperCamelCase__ : Optional[Any] =self.train_step_fn(__A , __A , **__A)
running_loss += jax_utils.unreplicate(metrics["loss"])
i += 1
if i % args.logging_steps == 0:
UpperCamelCase__ : Dict =jax_utils.unreplicate(state.step)
UpperCamelCase__ : Optional[Any] =running_loss.item() / i
UpperCamelCase__ : Tuple =self.scheduler_fn(state_step - 1)
UpperCamelCase__ : Any =self.evaluate(__A , __A)
UpperCamelCase__ : str ={
"step": state_step.item(),
"eval_loss": eval_loss.item(),
"tr_loss": tr_loss,
"lr": lr.item(),
}
tqdm.write(str(__A))
self.logger.log(__A , commit=__A)
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F'''-e{epoch}-s{i}''' , state=__A)
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) -> List[str]:
"""simple docstring"""
UpperCamelCase__ : List[Any] =get_batched_dataset(__A , self.args.batch_size)
UpperCamelCase__ : str =len(__A) // self.args.batch_size
UpperCamelCase__ : Optional[Any] =jnp.array(0 , dtype=jnp.floataa)
UpperCamelCase__ : Dict =0
for batch in tqdm(__A , total=__A , desc="Evaluating ... "):
UpperCamelCase__ : str =self.data_collator(__A)
UpperCamelCase__ : Dict =self.val_step_fn(__A , **__A)
running_loss += jax_utils.unreplicate(metrics["loss"])
i += 1
return running_loss / i
def UpperCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : Any =jax_utils.unreplicate(__A)
print(F'''SAVING CHECKPOINT IN {save_dir}''' , end=" ... ")
self.model_save_fn(__A , params=state.params)
with open(os.path.join(__A , "opt_state.msgpack") , "wb") as f:
f.write(to_bytes(state.opt_state))
joblib.dump(self.args , os.path.join(__A , "args.joblib"))
joblib.dump(self.data_collator , os.path.join(__A , "data_collator.joblib"))
with open(os.path.join(__A , "training_state.json") , "w") as f:
json.dump({"step": state.step.item()} , __A)
print("DONE")
def _lowerCamelCase ( A_ : Optional[Any] , A_ : List[str] ) -> Optional[int]:
'''simple docstring'''
print(f'''RESTORING CHECKPOINT FROM {save_dir}''' , end=" ... " )
with open(os.path.join(_lowerCAmelCase , "flax_model.msgpack" ) , "rb" ) as f:
UpperCamelCase__ : str =from_bytes(state.params , f.read() )
with open(os.path.join(_lowerCAmelCase , "opt_state.msgpack" ) , "rb" ) as f:
UpperCamelCase__ : List[Any] =from_bytes(state.opt_state , f.read() )
UpperCamelCase__ : Optional[int] =joblib.load(os.path.join(_lowerCAmelCase , "args.joblib" ) )
UpperCamelCase__ : List[Any] =joblib.load(os.path.join(_lowerCAmelCase , "data_collator.joblib" ) )
with open(os.path.join(_lowerCAmelCase , "training_state.json" ) , "r" ) as f:
UpperCamelCase__ : str =json.load(_lowerCAmelCase )
UpperCamelCase__ : Union[str, Any] =training_state["step"]
print("DONE" )
return params, opt_state, step, args, data_collator
def _lowerCamelCase ( A_ : Optional[int] , A_ : int , A_ : List[str] , A_ : str ) -> Any:
'''simple docstring'''
UpperCamelCase__ : List[str] =num_train_steps - warmup_steps
UpperCamelCase__ : Dict =optax.linear_schedule(init_value=_lowerCAmelCase , end_value=_lowerCAmelCase , transition_steps=_lowerCAmelCase )
UpperCamelCase__ : Dict =optax.linear_schedule(init_value=_lowerCAmelCase , end_value=1E-7 , transition_steps=_lowerCAmelCase )
UpperCamelCase__ : List[Any] =optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _lowerCamelCase ( A_ : Dict , A_ : int , A_ : Optional[int] , A_ : Dict , A_ : Optional[int] ) -> str:
'''simple docstring'''
def weight_decay_mask(A_ : List[Any] ):
UpperCamelCase__ : Tuple =traverse_util.flatten_dict(_lowerCAmelCase )
UpperCamelCase__ : Tuple ={k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
return traverse_util.unflatten_dict(_lowerCAmelCase )
UpperCamelCase__ : int =scheduler_fn(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
UpperCamelCase__ : Optional[Any] =optax.adamw(learning_rate=_lowerCAmelCase , weight_decay=_lowerCAmelCase , mask=_lowerCAmelCase )
return tx, lr
| 711
|
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(
description=(
"""Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
""" Distillation"""
)
)
parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
parser.add_argument("""--vocab_transform""", action="""store_true""")
__UpperCAmelCase = parser.parse_args()
if args.model_type == "roberta":
__UpperCAmelCase = RobertaForMaskedLM.from_pretrained(args.model_name)
__UpperCAmelCase = """roberta"""
elif args.model_type == "gpt2":
__UpperCAmelCase = GPTaLMHeadModel.from_pretrained(args.model_name)
__UpperCAmelCase = """transformer"""
__UpperCAmelCase = model.state_dict()
__UpperCAmelCase = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
__UpperCAmelCase = state_dict[F"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
__UpperCAmelCase = F"""{prefix}.embeddings.{w}.weight"""
__UpperCAmelCase = state_dict[param_name]
for w in ["weight", "bias"]:
__UpperCAmelCase = F"""{prefix}.embeddings.LayerNorm.{w}"""
__UpperCAmelCase = state_dict[param_name]
# Transformer Blocks #
__UpperCAmelCase = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[
F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
__UpperCAmelCase = state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[
F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
__UpperCAmelCase = state_dict[F"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[F"""lm_head.dense.{w}"""]
__UpperCAmelCase = state_dict[F"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
__UpperCAmelCase = state_dict[F"""{prefix}.ln_f.{w}"""]
__UpperCAmelCase = state_dict["""lm_head.weight"""]
print(F"""N layers selected for distillation: {std_idx}""")
print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 582
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : Dict = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vqa-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
'''uclanlp/visualbert-vcr-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
),
'''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
'''uclanlp/visualbert-nlvr2-coco-pre''': (
'''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
)
# See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class __A ( _SCREAMING_SNAKE_CASE ):
lowerCAmelCase_ : Union[str, Any] = 'visual_bert'
def __init__( self : Union[str, Any] , UpperCAmelCase_ : Tuple=30522 , UpperCAmelCase_ : List[str]=768 , UpperCAmelCase_ : str=512 , UpperCAmelCase_ : Tuple=12 , UpperCAmelCase_ : Tuple=12 , UpperCAmelCase_ : Tuple=3072 , UpperCAmelCase_ : List[str]="gelu" , UpperCAmelCase_ : Optional[Any]=0.1 , UpperCAmelCase_ : Optional[int]=0.1 , UpperCAmelCase_ : List[Any]=512 , UpperCAmelCase_ : int=2 , UpperCAmelCase_ : Optional[int]=0.02 , UpperCAmelCase_ : Union[str, Any]=1E-12 , UpperCAmelCase_ : str=False , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Union[str, Any]=1 , UpperCAmelCase_ : str=0 , UpperCAmelCase_ : Optional[int]=2 , **UpperCAmelCase_ : Dict , ):
super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__ )
lowerCAmelCase : List[Any] = vocab_size
lowerCAmelCase : Optional[int] = max_position_embeddings
lowerCAmelCase : Union[str, Any] = hidden_size
lowerCAmelCase : Optional[int] = visual_embedding_dim
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : Union[str, Any] = num_attention_heads
lowerCAmelCase : int = intermediate_size
lowerCAmelCase : Any = hidden_act
lowerCAmelCase : Optional[Any] = hidden_dropout_prob
lowerCAmelCase : int = attention_probs_dropout_prob
lowerCAmelCase : Union[str, Any] = initializer_range
lowerCAmelCase : str = type_vocab_size
lowerCAmelCase : Optional[int] = layer_norm_eps
lowerCAmelCase : str = bypass_transformer
lowerCAmelCase : List[Any] = special_visual_initialize
| 343
|
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def __a ( a ):
"""simple docstring"""
_a = min(a ) # min() finds the minimum value
_a = max(a ) # max() finds the maximum value
_a = max_val - min_val + 1 # size is difference of max and min values plus one
# list of pigeonholes of size equal to the variable size
_a = [0] * size
# Populate the pigeonholes.
for x in a:
assert isinstance(a, a ), "integers only please"
holes[x - min_val] += 1
# Putting the elements back into the array in an order.
_a = 0
for count in range(a ):
while holes[count] > 0:
holes[count] -= 1
_a = count + min_val
i += 1
def __a ( ):
"""simple docstring"""
_a = [8, 3, 2, 7, 4, 6, 8]
pigeonhole_sort(a )
print("Sorted order is:", " ".join(a ) )
if __name__ == "__main__":
main()
| 388
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
'''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
'''kssteven/ibert-roberta-large-mnli''': (
'''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
),
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'ibert'
def __init__( self , UpperCamelCase_=30522 , UpperCamelCase_=768 , UpperCamelCase_=12 , UpperCamelCase_=12 , UpperCamelCase_=3072 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=512 , UpperCamelCase_=2 , UpperCamelCase_=0.02 , UpperCamelCase_=1e-12 , UpperCamelCase_=1 , UpperCamelCase_=0 , UpperCamelCase_=2 , UpperCamelCase_="absolute" , UpperCamelCase_=False , UpperCamelCase_="none" , **UpperCamelCase_ , ):
'''simple docstring'''
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
UpperCamelCase__ :Tuple = vocab_size
UpperCamelCase__ :Union[str, Any] = hidden_size
UpperCamelCase__ :List[str] = num_hidden_layers
UpperCamelCase__ :List[Any] = num_attention_heads
UpperCamelCase__ :Optional[int] = hidden_act
UpperCamelCase__ :str = intermediate_size
UpperCamelCase__ :Dict = hidden_dropout_prob
UpperCamelCase__ :Optional[int] = attention_probs_dropout_prob
UpperCamelCase__ :Tuple = max_position_embeddings
UpperCamelCase__ :List[Any] = type_vocab_size
UpperCamelCase__ :str = initializer_range
UpperCamelCase__ :Any = layer_norm_eps
UpperCamelCase__ :str = position_embedding_type
UpperCamelCase__ :Union[str, Any] = quant_mode
UpperCamelCase__ :Optional[int] = force_dequant
class lowercase ( A__ ):
"""simple docstring"""
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
UpperCamelCase__ :Optional[int] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase__ :Dict = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 716
|
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
__snake_case = logging.getLogger()
def a ( __a ) -> Dict:
'''simple docstring'''
UpperCamelCase__ :str = {}
UpperCamelCase__ :Dict = os.path.join(__a , '''all_results.json''' )
if os.path.exists(__a ):
with open(__a , '''r''' ) as f:
UpperCamelCase__ :int = json.load(__a )
else:
raise ValueError(f'''can\'t find {path}''' )
return results
__snake_case = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class lowercase ( A__ ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
import xla_spawn
UpperCamelCase__ :Any = self.get_auto_remove_tmp_dir()
UpperCamelCase__ :List[str] = F'''
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
'''.split()
with patch.object(UpperCamelCase_ , '''argv''' , UpperCamelCase_ ):
UpperCamelCase__ :List[Any] = time()
xla_spawn.main()
UpperCamelCase__ :List[Any] = time()
UpperCamelCase__ :Tuple = get_results(UpperCamelCase_ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.75 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 500 )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
import xla_spawn
UpperCamelCase__ :Tuple = '''
./tests/test_trainer_tpu.py
--num_cores=8
./tests/test_trainer_tpu.py
'''.split()
with patch.object(UpperCamelCase_ , '''argv''' , UpperCamelCase_ ):
xla_spawn.main()
| 280
| 0
|
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class _a ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase):
__magic_name__ = StableDiffusionDiffEditPipeline
__magic_name__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
__magic_name__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
__magic_name__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__magic_name__ = frozenset([])
def __lowercase ( self : List[str] ) -> Any:
torch.manual_seed(0 )
snake_case : Dict = 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 , attention_head_dim=(2, 4) , use_linear_projection=_lowercase , )
snake_case : Dict = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
snake_case : Any = DDIMInverseScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=_lowercase , set_alpha_to_zero=_lowercase , )
torch.manual_seed(0 )
snake_case : int = 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 , sample_size=128 , )
torch.manual_seed(0 )
snake_case : Optional[int] = 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 , hidden_act="gelu" , projection_dim=512 , )
snake_case : Union[str, Any] = CLIPTextModel(_lowercase )
snake_case : Tuple = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
snake_case : Tuple = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def __lowercase ( self : List[str] , _lowercase : Union[str, Any] , _lowercase : Optional[Any]=0 ) -> str:
snake_case : List[Any] = floats_tensor((1, 16, 16) , rng=random.Random(_lowercase ) ).to(_lowercase )
snake_case : Any = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_lowercase ) ).to(_lowercase )
if str(_lowercase ).startswith("mps" ):
snake_case : int = torch.manual_seed(_lowercase )
else:
snake_case : Optional[int] = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
snake_case : Any = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __lowercase ( self : Optional[int] , _lowercase : List[str] , _lowercase : str=0 ) -> Any:
snake_case : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowercase ) ).to(_lowercase )
snake_case : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case : Union[str, Any] = Image.fromarray(np.uinta(_lowercase ) ).convert("RGB" )
if str(_lowercase ).startswith("mps" ):
snake_case : Tuple = torch.manual_seed(_lowercase )
else:
snake_case : List[Any] = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
snake_case : Union[str, Any] = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __lowercase ( self : Any , _lowercase : int , _lowercase : Union[str, Any]=0 ) -> List[str]:
snake_case : Dict = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowercase ) ).to(_lowercase )
snake_case : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case : Tuple = Image.fromarray(np.uinta(_lowercase ) ).convert("RGB" )
if str(_lowercase ).startswith("mps" ):
snake_case : List[Any] = torch.manual_seed(_lowercase )
else:
snake_case : List[Any] = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
snake_case : str = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def __lowercase ( self : Optional[Any] ) -> Tuple:
if not hasattr(self.pipeline_class , "_optional_components" ):
return
snake_case : Tuple = self.get_dummy_components()
snake_case : str = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_lowercase , _lowercase , _lowercase )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
snake_case : Optional[Any] = self.get_dummy_inputs(_lowercase )
snake_case : Dict = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
snake_case : Tuple = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowercase , _lowercase ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
snake_case : Tuple = self.get_dummy_inputs(_lowercase )
snake_case : str = pipe_loaded(**_lowercase )[0]
snake_case : List[Any] = np.abs(output - output_loaded ).max()
self.assertLess(_lowercase , 1E-4 )
def __lowercase ( self : Union[str, Any] ) -> List[str]:
snake_case : Tuple = "cpu"
snake_case : Optional[int] = self.get_dummy_components()
snake_case : List[str] = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case : Tuple = self.get_dummy_mask_inputs(_lowercase )
snake_case : str = pipe.generate_mask(**_lowercase )
snake_case : Optional[Any] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
snake_case : str = np.array([0] * 9 )
snake_case : Tuple = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowercase , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def __lowercase ( self : str ) -> int:
snake_case : str = "cpu"
snake_case : Optional[Any] = self.get_dummy_components()
snake_case : Dict = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case : int = self.get_dummy_inversion_inputs(_lowercase )
snake_case : Union[str, Any] = pipe.invert(**_lowercase ).images
snake_case : Dict = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
snake_case : Optional[int] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
snake_case : List[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowercase , 1E-3 )
def __lowercase ( self : Optional[Any] ) -> Tuple:
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def __lowercase ( self : List[Any] ) -> Optional[int]:
snake_case : str = "cpu"
snake_case : Optional[Any] = self.get_dummy_components()
snake_case : Union[str, Any] = {"beta_start": 0.00085, "beta_end": 0.012, "beta_schedule": "scaled_linear"}
snake_case : Tuple = DPMSolverMultistepScheduler(**_lowercase )
snake_case : int = DPMSolverMultistepInverseScheduler(**_lowercase )
snake_case : Union[str, Any] = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
snake_case : str = self.get_dummy_inversion_inputs(_lowercase )
snake_case : Optional[Any] = pipe.invert(**_lowercase ).images
snake_case : Optional[Any] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
snake_case : List[str] = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799] , )
snake_case : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowercase , 1E-3 )
@require_torch_gpu
@slow
class _a ( unittest.TestCase):
def __lowercase ( self : str ) -> Optional[int]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def __lowercase ( cls : Optional[int] ) -> Optional[Any]:
snake_case : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
snake_case : str = raw_image.convert("RGB" ).resize((768, 768) )
snake_case : Tuple = raw_image
def __lowercase ( self : Tuple ) -> int:
snake_case : int = torch.manual_seed(0 )
snake_case : List[Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=_lowercase , torch_dtype=torch.floataa )
snake_case : List[str] = DDIMScheduler.from_config(pipe.scheduler.config )
snake_case : int = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_lowercase )
snake_case : int = "a bowl of fruit"
snake_case : Any = "a bowl of pears"
snake_case : Tuple = pipe.generate_mask(
image=self.raw_image , source_prompt=_lowercase , target_prompt=_lowercase , generator=_lowercase , )
snake_case : Any = pipe.invert(
prompt=_lowercase , image=self.raw_image , inpaint_strength=0.7 , generator=_lowercase ).latents
snake_case : Any = pipe(
prompt=_lowercase , mask_image=_lowercase , image_latents=_lowercase , generator=_lowercase , negative_prompt=_lowercase , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
snake_case : List[str] = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def __lowercase ( self : str ) -> int:
snake_case : Any = torch.manual_seed(0 )
snake_case : List[str] = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=_lowercase , torch_dtype=torch.floataa )
snake_case : Tuple = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
snake_case : str = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_lowercase )
snake_case : List[str] = "a bowl of fruit"
snake_case : List[Any] = "a bowl of pears"
snake_case : int = pipe.generate_mask(
image=self.raw_image , source_prompt=_lowercase , target_prompt=_lowercase , generator=_lowercase , )
snake_case : List[str] = pipe.invert(
prompt=_lowercase , image=self.raw_image , inpaint_strength=0.7 , generator=_lowercase , num_inference_steps=25 , ).latents
snake_case : str = pipe(
prompt=_lowercase , mask_image=_lowercase , image_latents=_lowercase , generator=_lowercase , negative_prompt=_lowercase , inpaint_strength=0.7 , num_inference_steps=25 , output_type="numpy" , ).images[0]
snake_case : Tuple = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 449
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _a ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase):
__magic_name__ = StableDiffusionPanoramaPipeline
__magic_name__ = TEXT_TO_IMAGE_PARAMS
__magic_name__ = TEXT_TO_IMAGE_BATCH_PARAMS
__magic_name__ = TEXT_TO_IMAGE_IMAGE_PARAMS
__magic_name__ = TEXT_TO_IMAGE_IMAGE_PARAMS
def __lowercase ( self : Optional[int] ) -> int:
torch.manual_seed(0 )
snake_case : Optional[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , 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 : Tuple = DDIMScheduler()
torch.manual_seed(0 )
snake_case : List[Any] = 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 : str = 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 : int = CLIPTextModel(_lowercase )
snake_case : Union[str, Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
snake_case : Any = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def __lowercase ( self : List[str] , _lowercase : int , _lowercase : Dict=0 ) -> Optional[int]:
snake_case : Union[str, Any] = torch.manual_seed(_lowercase )
snake_case : str = {
"prompt": "a photo of the dolomites",
"generator": generator,
# Setting height and width to None to prevent OOMs on CPU.
"height": None,
"width": None,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def __lowercase ( self : str ) -> List[str]:
snake_case : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case : Dict = self.get_dummy_components()
snake_case : Any = StableDiffusionPanoramaPipeline(**_lowercase )
snake_case : List[Any] = sd_pipe.to(_lowercase )
sd_pipe.set_progress_bar_config(disable=_lowercase )
snake_case : Optional[Any] = self.get_dummy_inputs(_lowercase )
snake_case : Union[str, Any] = sd_pipe(**_lowercase ).images
snake_case : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case : List[Any] = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self : int ) -> Union[str, Any]:
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def __lowercase ( self : Tuple ) -> Tuple:
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25E-3 )
def __lowercase ( self : Any ) -> List[Any]:
snake_case : Any = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case : Union[str, Any] = self.get_dummy_components()
snake_case : Tuple = StableDiffusionPanoramaPipeline(**_lowercase )
snake_case : Tuple = sd_pipe.to(_lowercase )
sd_pipe.set_progress_bar_config(disable=_lowercase )
snake_case : List[str] = self.get_dummy_inputs(_lowercase )
snake_case : int = "french fries"
snake_case : Union[str, Any] = sd_pipe(**_lowercase , negative_prompt=_lowercase )
snake_case : str = output.images
snake_case : Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case : Union[str, Any] = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self : str ) -> Any:
snake_case : Optional[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case : List[Any] = self.get_dummy_components()
snake_case : List[Any] = StableDiffusionPanoramaPipeline(**_lowercase )
snake_case : int = sd_pipe.to(_lowercase )
sd_pipe.set_progress_bar_config(disable=_lowercase )
snake_case : Tuple = self.get_dummy_inputs(_lowercase )
snake_case : str = sd_pipe(**_lowercase , view_batch_size=2 )
snake_case : Optional[Any] = output.images
snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case : str = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self : int ) -> Optional[Any]:
snake_case : Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case : List[str] = self.get_dummy_components()
snake_case : Any = EulerAncestralDiscreteScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" )
snake_case : List[str] = StableDiffusionPanoramaPipeline(**_lowercase )
snake_case : List[str] = sd_pipe.to(_lowercase )
sd_pipe.set_progress_bar_config(disable=_lowercase )
snake_case : List[Any] = self.get_dummy_inputs(_lowercase )
snake_case : Optional[Any] = sd_pipe(**_lowercase ).images
snake_case : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case : Tuple = np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def __lowercase ( self : Tuple ) -> Union[str, Any]:
snake_case : str = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case : str = self.get_dummy_components()
snake_case : Optional[int] = PNDMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule="scaled_linear" , skip_prk_steps=_lowercase )
snake_case : Dict = StableDiffusionPanoramaPipeline(**_lowercase )
snake_case : Dict = sd_pipe.to(_lowercase )
sd_pipe.set_progress_bar_config(disable=_lowercase )
snake_case : Dict = self.get_dummy_inputs(_lowercase )
snake_case : Optional[int] = sd_pipe(**_lowercase ).images
snake_case : str = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
snake_case : Optional[int] = np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _a ( unittest.TestCase):
def __lowercase ( self : Union[str, Any] ) -> str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase ( self : Dict , _lowercase : Any=0 ) -> Optional[Any]:
snake_case : Any = torch.manual_seed(_lowercase )
snake_case : Any = {
"prompt": "a photo of the dolomites",
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "numpy",
}
return inputs
def __lowercase ( self : Optional[Any] ) -> Union[str, Any]:
snake_case : List[Any] = "stabilityai/stable-diffusion-2-base"
snake_case : Dict = DDIMScheduler.from_pretrained(_lowercase , subfolder="scheduler" )
snake_case : Any = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase , scheduler=_lowercase , safety_checker=_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
snake_case : str = self.get_inputs()
snake_case : List[Any] = pipe(**_lowercase ).images
snake_case : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
snake_case : List[str] = np.array(
[
0.36968392,
0.27025372,
0.32446766,
0.28379387,
0.36363274,
0.30733347,
0.27100027,
0.27054125,
0.25536096,
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-2
def __lowercase ( self : Tuple ) -> List[str]:
snake_case : List[str] = StableDiffusionPanoramaPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base" , safety_checker=_lowercase )
snake_case : int = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
snake_case : Dict = self.get_inputs()
snake_case : int = pipe(**_lowercase ).images
snake_case : Optional[Any] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2048, 3)
snake_case : List[str] = np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1E-3
def __lowercase ( self : str ) -> Any:
snake_case : Any = 0
def callback_fn(_lowercase : int , _lowercase : int , _lowercase : torch.FloatTensor ) -> None:
snake_case : List[Any] = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
snake_case : List[Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
snake_case : int = latents[0, -3:, -3:, -1]
snake_case : Tuple = np.array(
[
0.18681869,
0.33907816,
0.5361276,
0.14432865,
-0.02856611,
-0.73941123,
0.23397987,
0.47322682,
-0.37823164,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
elif step == 2:
snake_case : Union[str, Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
snake_case : Tuple = latents[0, -3:, -3:, -1]
snake_case : Tuple = np.array(
[
0.18539645,
0.33987248,
0.5378559,
0.14437142,
-0.02455261,
-0.7338317,
0.23990755,
0.47356272,
-0.3786505,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
snake_case : Dict = False
snake_case : List[Any] = "stabilityai/stable-diffusion-2-base"
snake_case : List[str] = DDIMScheduler.from_pretrained(_lowercase , subfolder="scheduler" )
snake_case : Optional[Any] = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase , scheduler=_lowercase , safety_checker=_lowercase )
snake_case : Any = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
snake_case : Tuple = self.get_inputs()
pipe(**_lowercase , callback=_lowercase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def __lowercase ( self : Any ) -> Tuple:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
snake_case : Optional[int] = "stabilityai/stable-diffusion-2-base"
snake_case : Tuple = DDIMScheduler.from_pretrained(_lowercase , subfolder="scheduler" )
snake_case : Any = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase , scheduler=_lowercase , safety_checker=_lowercase )
snake_case : Tuple = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
snake_case : Any = self.get_inputs()
snake_case : Optional[int] = pipe(**_lowercase )
snake_case : int = torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 449
| 1
|
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :int = set()
__UpperCamelCase :int = []
def parse_line(SCREAMING_SNAKE_CASE ):
for line in fp:
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :List[Any] = line.decode('''UTF-8''' )
if "warnings summary (final)" in line:
continue
# This means we are outside the body of a warning
elif not line.startswith(''' ''' ):
# process a single warning and move it to `selected_warnings`.
if len(SCREAMING_SNAKE_CASE ) > 0:
__UpperCamelCase :List[Any] = '''\n'''.join(SCREAMING_SNAKE_CASE )
# Only keep the warnings specified in `targets`
if any(f""": {x}: """ in warning for x in targets ):
selected_warnings.add(SCREAMING_SNAKE_CASE )
buffer.clear()
continue
else:
__UpperCamelCase :str = line.strip()
buffer.append(SCREAMING_SNAKE_CASE )
if from_gh:
for filename in os.listdir(SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
# read the file
if filename != "warnings.txt":
continue
with open(SCREAMING_SNAKE_CASE ) as fp:
parse_line(SCREAMING_SNAKE_CASE )
else:
try:
with zipfile.ZipFile(SCREAMING_SNAKE_CASE ) as z:
for filename in z.namelist():
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
# read the file
if filename != "warnings.txt":
continue
with z.open(SCREAMING_SNAKE_CASE ) as fp:
parse_line(SCREAMING_SNAKE_CASE )
except Exception:
logger.warning(
f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.""" )
return selected_warnings
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Optional[Any] = set()
__UpperCamelCase :Union[str, Any] = [os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) for p in os.listdir(SCREAMING_SNAKE_CASE ) if (p.endswith('''.zip''' ) or from_gh)]
for p in paths:
selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
return selected_warnings
if __name__ == "__main__":
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return values.split(''',''' )
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
parser.add_argument(
'''--output_dir''',
type=str,
required=True,
help='''Where to store the downloaded artifacts and other result files.''',
)
parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
# optional parameters
parser.add_argument(
'''--targets''',
default='''DeprecationWarning,UserWarning,FutureWarning''',
type=list_str,
help='''Comma-separated list of target warning(s) which we want to extract.''',
)
parser.add_argument(
'''--from_gh''',
action='''store_true''',
help='''If running from a GitHub action workflow and collecting warnings from its artifacts.''',
)
__lowercase = parser.parse_args()
__lowercase = args.from_gh
if from_gh:
# The artifacts have to be downloaded using `actions/download-artifact@v3`
pass
else:
os.makedirs(args.output_dir, exist_ok=True)
# get download links
__lowercase = get_artifacts_links(args.workflow_run_id, token=args.token)
with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(artifacts, fp, ensure_ascii=False, indent=4)
# download artifacts
for idx, (name, url) in enumerate(artifacts.items()):
print(name)
print(url)
print('''=''' * 80)
download_artifact(name, url, args.output_dir, args.token)
# Be gentle to GitHub
time.sleep(1)
# extract warnings from artifacts
__lowercase = extract_warnings(args.output_dir, args.targets)
__lowercase = sorted(selected_warnings)
with open(os.path.join(args.output_dir, '''selected_warnings.json'''), '''w''', encoding='''UTF-8''') as fp:
json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)
| 452
|
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Union[str, Any] = None
a__ : Tuple = None
@property
def UpperCamelCase__ ( self) -> Any:
return self.feat_extract_tester.prepare_feat_extract_dict()
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :str = self.feature_extraction_class(**self.feat_extract_dict)
self.assertTrue(hasattr(__lowercase , '''feature_size'''))
self.assertTrue(hasattr(__lowercase , '''sampling_rate'''))
self.assertTrue(hasattr(__lowercase , '''padding_value'''))
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :str = self.feat_extract_tester.prepare_inputs_for_common()
__UpperCamelCase :Optional[int] = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :Optional[Any] = feat_extract.model_input_names[0]
__UpperCamelCase :Union[str, Any] = BatchFeature({input_name: speech_inputs})
self.assertTrue(all(len(__lowercase) == len(__lowercase) for x, y in zip(__lowercase , processed_features[input_name])))
__UpperCamelCase :Any = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
__UpperCamelCase :Optional[int] = BatchFeature({input_name: speech_inputs} , tensor_type='''np''')
__UpperCamelCase :Any = processed_features[input_name]
if len(batch_features_input.shape) < 3:
__UpperCamelCase :Optional[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))
@require_torch
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Dict = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
__UpperCamelCase :Tuple = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :str = feat_extract.model_input_names[0]
__UpperCamelCase :Optional[Any] = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''')
__UpperCamelCase :Union[str, Any] = processed_features[input_name]
if len(batch_features_input.shape) < 3:
__UpperCamelCase :str = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))
@require_tf
def UpperCamelCase__ ( self) -> Optional[Any]:
__UpperCamelCase :str = self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowercase)
__UpperCamelCase :str = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :Union[str, Any] = feat_extract.model_input_names[0]
__UpperCamelCase :Optional[Any] = BatchFeature({input_name: speech_inputs} , tensor_type='''tf''')
__UpperCamelCase :int = processed_features[input_name]
if len(batch_features_input.shape) < 3:
__UpperCamelCase :List[Any] = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))
def UpperCamelCase__ ( self , __lowercase=False) -> Dict:
def _inputs_have_equal_length(__lowercase):
__UpperCamelCase :List[str] = len(input[0])
for input_slice in input[1:]:
if len(__lowercase) != length:
return False
return True
def _inputs_are_equal(__lowercase , __lowercase):
if len(__lowercase) != len(__lowercase):
return False
for input_slice_a, input_slice_a in zip(__lowercase , __lowercase):
if not np.allclose(np.asarray(__lowercase) , np.asarray(__lowercase) , atol=1E-3):
return False
return True
__UpperCamelCase :List[str] = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :Optional[int] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
__UpperCamelCase :Any = feat_extract.model_input_names[0]
__UpperCamelCase :Optional[Any] = BatchFeature({input_name: speech_inputs})
__UpperCamelCase :Optional[Any] = self.feat_extract_tester.seq_length_diff
__UpperCamelCase :Union[str, Any] = self.feat_extract_tester.max_seq_length + pad_diff
__UpperCamelCase :Tuple = self.feat_extract_tester.min_seq_length
__UpperCamelCase :Optional[int] = self.feat_extract_tester.batch_size
__UpperCamelCase :Any = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__UpperCamelCase :List[Any] = feat_extract.pad(__lowercase , padding=__lowercase)
__UpperCamelCase :Tuple = input_a[input_name]
__UpperCamelCase :int = feat_extract.pad(__lowercase , padding='''longest''')
__UpperCamelCase :int = input_a[input_name]
__UpperCamelCase :Optional[Any] = feat_extract.pad(__lowercase , padding='''max_length''' , max_length=len(speech_inputs[-1]))
__UpperCamelCase :Dict = input_a[input_name]
__UpperCamelCase :List[Any] = feat_extract.pad(__lowercase , padding='''longest''' , return_tensors='''np''')
__UpperCamelCase :Optional[int] = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(__lowercase):
feat_extract.pad(__lowercase , padding='''max_length''')[input_name]
__UpperCamelCase :Any = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=__lowercase , return_tensors='''np''')
__UpperCamelCase :Tuple = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(__lowercase))
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertTrue(_inputs_are_equal(__lowercase , __lowercase))
self.assertTrue(len(input_a[0]) == pad_min_length)
self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)
# test padding for `pad_to_multiple_of` for List[int] + numpy
__UpperCamelCase :int = feat_extract.pad(__lowercase , pad_to_multiple_of=10)
__UpperCamelCase :Tuple = input_a[input_name]
__UpperCamelCase :Optional[int] = feat_extract.pad(__lowercase , padding='''longest''' , pad_to_multiple_of=10)
__UpperCamelCase :Tuple = input_a[input_name]
__UpperCamelCase :str = feat_extract.pad(
__lowercase , padding='''max_length''' , pad_to_multiple_of=10 , max_length=__lowercase)
__UpperCamelCase :Any = input_a[input_name]
__UpperCamelCase :List[str] = feat_extract.pad(
__lowercase , padding='''max_length''' , pad_to_multiple_of=10 , max_length=__lowercase , return_tensors='''np''' , )
__UpperCamelCase :List[str] = input_a[input_name]
self.assertTrue(all(len(__lowercase) % 10 == 0 for x in input_a))
self.assertTrue(_inputs_are_equal(__lowercase , __lowercase))
__UpperCamelCase :str = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(__lowercase) == expected_mult_pad_length for x in input_a))
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length))
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size)
# Check padding value is correct
__UpperCamelCase :Optional[Any] = (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
< 1E-3)
self.assertTrue(
abs(
np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
< 1E-3)
self.assertTrue(
abs(
np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
< 1E-3)
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
< 1E-3)
def UpperCamelCase__ ( self , __lowercase=False) -> Dict:
def _inputs_have_equal_length(__lowercase):
__UpperCamelCase :Dict = len(input[0])
for input_slice in input[1:]:
if len(__lowercase) != length:
return False
return True
def _inputs_are_equal(__lowercase , __lowercase):
if len(__lowercase) != len(__lowercase):
return False
for input_slice_a, input_slice_a in zip(__lowercase , __lowercase):
if not np.allclose(np.asarray(__lowercase) , np.asarray(__lowercase) , atol=1E-3):
return False
return True
__UpperCamelCase :str = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowercase)
__UpperCamelCase :Tuple = feat_extract.model_input_names[0]
__UpperCamelCase :Dict = BatchFeature({input_name: speech_inputs})
# truncate to smallest
__UpperCamelCase :List[Any] = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[0]) , truncation=__lowercase)
__UpperCamelCase :str = input_a[input_name]
__UpperCamelCase :Optional[int] = feat_extract.pad(__lowercase , padding='''max_length''' , max_length=len(speech_inputs[0]))
__UpperCamelCase :List[str] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertFalse(_inputs_have_equal_length(__lowercase))
# truncate to smallest with np
__UpperCamelCase :Union[str, Any] = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[0]) , return_tensors='''np''' , truncation=__lowercase , )
__UpperCamelCase :List[Any] = input_a[input_name]
__UpperCamelCase :int = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[0]) , return_tensors='''np''')
__UpperCamelCase :Optional[Any] = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(__lowercase))
# truncate to middle
__UpperCamelCase :Optional[Any] = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[1]) , truncation=__lowercase , return_tensors='''np''' , )
__UpperCamelCase :Dict = input_a[input_name]
__UpperCamelCase :str = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[1]) , truncation=__lowercase)
__UpperCamelCase :Union[str, Any] = input_a[input_name]
__UpperCamelCase :Optional[Any] = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[1]) , return_tensors='''np''')
__UpperCamelCase :Union[str, Any] = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertTrue(_inputs_are_equal(__lowercase , __lowercase))
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(__lowercase))
self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__lowercase):
feat_extract.pad(__lowercase , truncation=__lowercase)[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__lowercase):
feat_extract.pad(__lowercase , padding='''longest''' , truncation=__lowercase)[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(__lowercase):
feat_extract.pad(__lowercase , padding='''longest''' , truncation=__lowercase)[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(__lowercase):
feat_extract.pad(__lowercase , padding='''max_length''' , truncation=__lowercase)[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__UpperCamelCase :Dict = 12
__UpperCamelCase :str = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[0]) , pad_to_multiple_of=__lowercase , truncation=__lowercase , )
__UpperCamelCase :List[Any] = input_a[input_name]
__UpperCamelCase :Tuple = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=len(speech_inputs[0]) , pad_to_multiple_of=__lowercase , )
__UpperCamelCase :Any = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__UpperCamelCase :Optional[Any] = len(speech_inputs[0])
if expected_length % pad_to_multiple_of != 0:
__UpperCamelCase :Dict = ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0]) == expected_length)
self.assertTrue(_inputs_have_equal_length(__lowercase))
self.assertFalse(_inputs_have_equal_length(__lowercase))
def UpperCamelCase__ ( self) -> Any:
self._check_padding(numpify=__lowercase)
def UpperCamelCase__ ( self) -> Dict:
self._check_padding(numpify=__lowercase)
def UpperCamelCase__ ( self) -> Any:
self._check_truncation(numpify=__lowercase)
def UpperCamelCase__ ( self) -> Union[str, Any]:
self._check_truncation(numpify=__lowercase)
@require_torch
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :int = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :Union[str, Any] = self.feat_extract_tester.prepare_inputs_for_common()
__UpperCamelCase :str = feat_extract.model_input_names[0]
__UpperCamelCase :int = BatchFeature({input_name: speech_inputs})
__UpperCamelCase :List[str] = feat_extract.pad(__lowercase , padding='''longest''' , return_tensors='''np''')[input_name]
__UpperCamelCase :List[Any] = feat_extract.pad(__lowercase , padding='''longest''' , return_tensors='''pt''')[input_name]
self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1E-2)
@require_tf
def UpperCamelCase__ ( self) -> Tuple:
__UpperCamelCase :int = self.feature_extraction_class(**self.feat_extract_dict)
__UpperCamelCase :Any = self.feat_extract_tester.prepare_inputs_for_common()
__UpperCamelCase :Any = feat_extract.model_input_names[0]
__UpperCamelCase :Union[str, Any] = BatchFeature({input_name: speech_inputs})
__UpperCamelCase :str = feat_extract.pad(__lowercase , padding='''longest''' , return_tensors='''np''')[input_name]
__UpperCamelCase :Optional[Any] = feat_extract.pad(__lowercase , padding='''longest''' , return_tensors='''tf''')[input_name]
self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1E-2)
def UpperCamelCase__ ( self) -> str:
__UpperCamelCase :List[Any] = self.feat_extract_dict
__UpperCamelCase :Dict = True
__UpperCamelCase :Dict = self.feature_extraction_class(**__lowercase)
__UpperCamelCase :List[Any] = self.feat_extract_tester.prepare_inputs_for_common()
__UpperCamelCase :Any = [len(__lowercase) for x in speech_inputs]
__UpperCamelCase :int = feat_extract.model_input_names[0]
__UpperCamelCase :Optional[int] = BatchFeature({input_name: speech_inputs})
__UpperCamelCase :Union[str, Any] = feat_extract.pad(__lowercase , padding='''longest''' , return_tensors='''np''')
self.assertIn('''attention_mask''' , __lowercase)
self.assertListEqual(list(processed.attention_mask.shape) , list(processed[input_name].shape[:2]))
self.assertListEqual(processed.attention_mask.sum(-1).tolist() , __lowercase)
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Optional[int] = self.feat_extract_dict
__UpperCamelCase :Optional[int] = True
__UpperCamelCase :Dict = self.feature_extraction_class(**__lowercase)
__UpperCamelCase :List[str] = self.feat_extract_tester.prepare_inputs_for_common()
__UpperCamelCase :List[Any] = [len(__lowercase) for x in speech_inputs]
__UpperCamelCase :List[Any] = feat_extract.model_input_names[0]
__UpperCamelCase :int = BatchFeature({input_name: speech_inputs})
__UpperCamelCase :Dict = min(__lowercase)
__UpperCamelCase :Union[str, Any] = feat_extract.pad(
__lowercase , padding='''max_length''' , max_length=__lowercase , truncation=__lowercase , return_tensors='''np''')
self.assertIn('''attention_mask''' , __lowercase)
self.assertListEqual(
list(processed_pad.attention_mask.shape) , [processed_pad[input_name].shape[0], max_length])
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1).tolist() , [max_length for x in speech_inputs])
| 452
| 1
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
lowerCamelCase_ : List[Any] = None
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : Optional[int] = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
lowerCamelCase_ : Optional[Any] = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
},
"""tokenizer_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
},
}
lowerCamelCase_ : Any = {
"""camembert-base""": 512,
}
lowerCamelCase_ : Dict = """▁"""
class a__ ( __snake_case ):
A__ : Tuple = VOCAB_FILES_NAMES
A__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
A__ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ : List[Any] = ['input_ids', 'attention_mask']
A__ : Any = CamembertTokenizer
def __init__( self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase="<s>" , UpperCAmelCase="</s>" , UpperCAmelCase="</s>" , UpperCAmelCase="<s>" , UpperCAmelCase="<unk>" , UpperCAmelCase="<pad>" , UpperCAmelCase="<mask>" , UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"] , **UpperCAmelCase , ) -> List[Any]:
# Mask token behave like a normal word, i.e. include the space before it
__a = AddedToken(UpperCAmelCase , lstrip=UpperCAmelCase , rstrip=UpperCAmelCase ) if isinstance(UpperCAmelCase , UpperCAmelCase ) else mask_token
super().__init__(
UpperCAmelCase , tokenizer_file=UpperCAmelCase , bos_token=UpperCAmelCase , eos_token=UpperCAmelCase , sep_token=UpperCAmelCase , cls_token=UpperCAmelCase , unk_token=UpperCAmelCase , pad_token=UpperCAmelCase , mask_token=UpperCAmelCase , additional_special_tokens=UpperCAmelCase , **UpperCAmelCase , )
__a = vocab_file
__a = False if not self.vocab_file else True
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
__a = [self.cls_token_id]
__a = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase = None ) -> List[int]:
__a = [self.sep_token_id]
__a = [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]
def __SCREAMING_SNAKE_CASE ( self , UpperCAmelCase , UpperCAmelCase = None ) -> Tuple[str]:
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(UpperCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
__a = os.path.join(
UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase ):
copyfile(self.vocab_file , UpperCAmelCase )
return (out_vocab_file,)
| 559
|
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase = 0 ):
__a = length or len(__lowerCamelCase )
__a = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__a , __a = list_data[i + 1], list_data[i]
__a = True
return list_data if not swapped else bubble_sort(__lowerCamelCase , length - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 559
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
'SEWForCTC',
'SEWForSequenceClassification',
'SEWModel',
'SEWPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
_A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 403
|
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : Tuple ) -> Tuple:
"""simple docstring"""
if isinstance(UpperCamelCase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class lowerCamelCase_ :
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
pass
def __magic_name__ ( self ):
pass
def __magic_name__ ( self ):
pass
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = np.abs((a - b) ).max()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , f"""Difference between torch and flax is {diff} (>= {tol}).""" )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ = VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ , a_ = self.get_vision_text_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = {"""vision_model""": vision_model, """text_model""": text_model}
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ , a_ = self.get_vision_text_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = {"""vision_model""": vision_model, """text_model""": text_model}
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
a_ = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE )
a_ = model(input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
a_ = after_output[0]
a_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1E-3 )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ):
a_ , a_ = self.get_vision_text_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = {"""vision_model""": vision_model, """text_model""": text_model}
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE )
a_ = model(
input_ids=_SCREAMING_SNAKE_CASE , pixel_values=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , output_attentions=_SCREAMING_SNAKE_CASE )
a_ = output.vision_model_output.attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
a_ = to_atuple(vision_model.config.image_size )
a_ = to_atuple(vision_model.config.patch_size )
a_ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
a_ = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) )
a_ = output.text_model_output.attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
pt_model.to(_SCREAMING_SNAKE_CASE )
pt_model.eval()
# prepare inputs
a_ = inputs_dict
a_ = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
a_ = pt_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
a_ = fx_model(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_SCREAMING_SNAKE_CASE , pt_output.numpy() , 4E-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE , from_pt=_SCREAMING_SNAKE_CASE )
a_ = fx_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ):
self.assert_almost_equals(_SCREAMING_SNAKE_CASE , pt_output.numpy() , 4E-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = VisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE , from_flax=_SCREAMING_SNAKE_CASE )
pt_model_loaded.to(_SCREAMING_SNAKE_CASE )
pt_model_loaded.eval()
with torch.no_grad():
a_ = pt_model_loaded(**_SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , len(_SCREAMING_SNAKE_CASE ) , """Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ):
self.assert_almost_equals(_SCREAMING_SNAKE_CASE , pt_output_loaded.numpy() , 4E-2 )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , _SCREAMING_SNAKE_CASE )
a_ = fx_state
self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
a_ = VisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE )
a_ = load_flax_weights_in_pytorch_model(_SCREAMING_SNAKE_CASE , fx_model.params )
self.check_pt_flax_equivalence(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_save_load(**_SCREAMING_SNAKE_CASE )
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE )
@is_pt_flax_cross_test
def __magic_name__ ( self ):
a_ = self.prepare_config_and_inputs()
a_ = config_inputs_dict.pop("""vision_config""" )
a_ = config_inputs_dict.pop("""text_config""" )
a_ = config_inputs_dict
self.check_equivalence_pt_to_flax(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.check_equivalence_flax_to_pt(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@slow
def __magic_name__ ( self ):
a_ , a_ = self.get_pretrained_model_and_inputs()
a_ = model_a(**_SCREAMING_SNAKE_CASE )
a_ = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(_SCREAMING_SNAKE_CASE )
a_ = FlaxVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE )
a_ = model_a(**_SCREAMING_SNAKE_CASE )
a_ = after_outputs[0]
a_ = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1E-5 )
@require_flax
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
def __magic_name__ ( self ):
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=_SCREAMING_SNAKE_CASE , text_from_pt=_SCREAMING_SNAKE_CASE , )
a_ = 13
a_ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
a_ = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
a_ = random_attention_mask([batch_size, 4] )
a_ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = FlaxViTModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxBertModel(_SCREAMING_SNAKE_CASE )
return vision_model, text_model
def __magic_name__ ( self ):
a_ = FlaxViTModelTester(self )
a_ = FlaxBertModelTester(self )
a_ = vit_model_tester.prepare_config_and_inputs()
a_ = bert_model_tester.prepare_config_and_inputs()
a_ , a_ = vision_config_and_inputs
a_ , a_ , a_ , a_ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
def __magic_name__ ( self ):
a_ = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=_SCREAMING_SNAKE_CASE , text_from_pt=_SCREAMING_SNAKE_CASE , )
a_ = 13
a_ = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
a_ = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size )
a_ = random_attention_mask([batch_size, 4] )
a_ = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def __magic_name__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
a_ = FlaxCLIPVisionModel(_SCREAMING_SNAKE_CASE )
a_ = FlaxBertModel(_SCREAMING_SNAKE_CASE )
return vision_model, text_model
def __magic_name__ ( self ):
a_ = FlaxCLIPVisionModelTester(self )
a_ = FlaxBertModelTester(self )
a_ = clip_model_tester.prepare_config_and_inputs()
a_ = bert_model_tester.prepare_config_and_inputs()
a_ , a_ = vision_config_and_inputs
a_ , a_ , a_ , a_ = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@slow
def __magic_name__ ( self ):
a_ = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 )
a_ = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
a_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
a_ = processor(
text=["""una foto di un gatto""", """una foto di un cane"""] , images=_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors="""np""" )
a_ = model(**_SCREAMING_SNAKE_CASE )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , )
a_ = np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]] )
self.assertTrue(np.allclose(outputs.logits_per_image , _SCREAMING_SNAKE_CASE , atol=1E-3 ) )
| 403
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : str = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( snake_case_ ):
lowercase__ = '''nllb-moe'''
lowercase__ = ['''past_key_values''']
lowercase__ = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Dict , lowerCAmelCase_ : List[Any]=1_2_8_1_1_2 , lowerCAmelCase_ : Optional[Any]=1_0_2_4 , lowerCAmelCase_ : Any=1_2 , lowerCAmelCase_ : int=4_0_9_6 , lowerCAmelCase_ : Any=1_6 , lowerCAmelCase_ : List[Any]=1_2 , lowerCAmelCase_ : int=4_0_9_6 , lowerCAmelCase_ : Dict=1_6 , lowerCAmelCase_ : Any=0.05 , lowerCAmelCase_ : Optional[Any]=0.05 , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Tuple=True , lowerCAmelCase_ : Any="relu" , lowerCAmelCase_ : Optional[int]=1_0_2_4 , lowerCAmelCase_ : List[str]=0.1 , lowerCAmelCase_ : int=0.1 , lowerCAmelCase_ : Union[str, Any]=0.0 , lowerCAmelCase_ : Dict=0.02 , lowerCAmelCase_ : Optional[int]=2 , lowerCAmelCase_ : Optional[int]=True , lowerCAmelCase_ : str=False , lowerCAmelCase_ : Any="float32" , lowerCAmelCase_ : str=False , lowerCAmelCase_ : str=1_2_8 , lowerCAmelCase_ : Union[str, Any]=6_4 , lowerCAmelCase_ : int=4 , lowerCAmelCase_ : List[Any]=4 , lowerCAmelCase_ : Optional[Any]=0.001 , lowerCAmelCase_ : Union[str, Any]=0.001 , lowerCAmelCase_ : Tuple="all" , lowerCAmelCase_ : Tuple=False , lowerCAmelCase_ : int=False , lowerCAmelCase_ : Any=1.0 , lowerCAmelCase_ : int=0.2 , lowerCAmelCase_ : Optional[Any]=1 , lowerCAmelCase_ : Dict=0 , lowerCAmelCase_ : Any=2 , lowerCAmelCase_ : Any=False , **lowerCAmelCase_ : int , ):
"""simple docstring"""
lowercase_ = vocab_size
lowercase_ = max_position_embeddings
lowercase_ = d_model
lowercase_ = encoder_ffn_dim
lowercase_ = encoder_layers
lowercase_ = encoder_attention_heads
lowercase_ = decoder_ffn_dim
lowercase_ = decoder_layers
lowercase_ = decoder_attention_heads
lowercase_ = dropout
lowercase_ = attention_dropout
lowercase_ = activation_dropout
lowercase_ = activation_function
lowercase_ = init_std
lowercase_ = encoder_layerdrop
lowercase_ = decoder_layerdrop
lowercase_ = use_cache
lowercase_ = encoder_layers
lowercase_ = scale_embedding # scale factor will be sqrt(d_model) if True
lowercase_ = router_z_loss_coef
lowercase_ = router_aux_loss_coef
lowercase_ = decoder_sparse_step
lowercase_ = encoder_sparse_step
lowercase_ = num_experts
lowercase_ = expert_capacity
lowercase_ = router_bias
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''')
lowercase_ = router_dtype
lowercase_ = router_ignore_padding_tokens
lowercase_ = batch_prioritized_routing
lowercase_ = second_expert_policy
lowercase_ = normalize_router_prob_before_dropping
lowercase_ = moe_eval_capacity_token_fraction
lowercase_ = moe_token_dropout
lowercase_ = output_router_logits
super().__init__(
pad_token_id=UpperCAmelCase__ , bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , is_encoder_decoder=UpperCAmelCase__ , decoder_start_token_id=UpperCAmelCase__ , **UpperCAmelCase__ , )
| 567
|
"""simple docstring"""
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_a : Tuple = str(bin(UpperCamelCase__ ) )[2:] # remove the leading "0b"
_a : Optional[Any] = str(bin(UpperCamelCase__ ) )[2:] # remove the leading "0b"
_a : List[str] = max(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
return "0b" + "".join(
str(int(char_a == """1""" and char_b == """1""" ) )
for char_a, char_b in zip(a_binary.zfill(UpperCamelCase__ ) , b_binary.zfill(UpperCamelCase__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 389
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
"""LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LiltForQuestionAnswering""",
"""LiltForSequenceClassification""",
"""LiltForTokenClassification""",
"""LiltModel""",
"""LiltPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 702
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a__ = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ["""CLIPFeatureExtractor"""]
a__ = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
a__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 99
| 0
|
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
__lowerCamelCase : str = True
from torch.cuda.amp import autocast
__lowerCamelCase : List[str] = logging.getLogger(__name__)
def SCREAMING_SNAKE_CASE__ ( snake_case_=None, snake_case_=None ) -> List[Any]:
return field(default_factory=lambda: default, metadata=A__ )
@dataclass
class a :
__lowercase = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
__lowercase = field(
default=__UpperCAmelCase ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,)
__lowercase = field(
default=__UpperCAmelCase ,metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""} )
__lowercase = field(
default=0.1 ,metadata={"""help""": """The dropout ratio for the attention probabilities."""} )
__lowercase = field(
default=0.1 ,metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""} )
__lowercase = field(
default=0.1 ,metadata={
"""help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
} ,)
__lowercase = field(
default=0.1 ,metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} ,)
__lowercase = field(
default=0.05 ,metadata={
"""help""": (
"""Propability of each feature vector along the time axis to be chosen as the start of the vector"""
"""span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
"""vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
)
} ,)
__lowercase = field(default=0.0 ,metadata={"""help""": """The LayerDrop probability."""} )
@dataclass
class a :
__lowercase = field(
default=__UpperCAmelCase ,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
__lowercase = field(
default="""train+validation""" ,metadata={
"""help""": """The name of the training data set split to use (via the datasets library). Defaults to \'train\'"""
} ,)
__lowercase = field(
default=__UpperCAmelCase ,metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
__lowercase = field(
default=__UpperCAmelCase ,metadata={"""help""": """The number of processes to use for the preprocessing."""} ,)
__lowercase = field(
default=__UpperCAmelCase ,metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} ,)
__lowercase = field(
default=__UpperCAmelCase ,metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of validation examples to this """
"""value if set."""
)
} ,)
__lowercase = list_field(
default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """\'""", """\"""", """�"""] ,metadata={"""help""": """A list of characters to remove from the transcripts."""} ,)
@dataclass
class a :
__lowercase = 42
__lowercase = True
__lowercase = None
__lowercase = None
__lowercase = None
__lowercase = None
def __call__( self , __UpperCamelCase )-> Dict[str, torch.Tensor]:
'''simple docstring'''
A__ : Optional[int] =[{'input_values': feature['input_values']} for feature in features]
A__ : Dict =[{'input_ids': feature['labels']} for feature in features]
A__ : Tuple =self.processor.pad(
__UpperCamelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
A__ : str =self.processor.pad(
labels=__UpperCamelCase , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors='''pt''' , )
# replace padding with -100 to ignore loss correctly
A__ : int =labels_batch['input_ids'].masked_fill(labels_batch.attention_mask.ne(1 ) , -1_00 )
A__ : Any =labels
return batch
class a ( __UpperCAmelCase ):
def lowerCAmelCase_ ( self , __UpperCamelCase , __UpperCamelCase )-> torch.Tensor:
'''simple docstring'''
model.train()
A__ : Any =self._prepare_inputs(__UpperCamelCase )
if self.use_amp:
with autocast():
A__ : Dict =self.compute_loss(__UpperCamelCase , __UpperCamelCase )
else:
A__ : List[str] =self.compute_loss(__UpperCamelCase , __UpperCamelCase )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
A__ : Any =loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
A__ : Optional[int] =loss.sum() / (inputs['labels'] >= 0).sum()
else:
raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']' )
if self.args.gradient_accumulation_steps > 1:
A__ : Optional[int] =loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(__UpperCamelCase ).backward()
elif self.use_apex:
with amp.scale_loss(__UpperCamelCase , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(__UpperCamelCase )
else:
loss.backward()
return loss.detach()
def SCREAMING_SNAKE_CASE__ ( ) -> List[str]:
# 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.
A__ : int =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
A__ : Tuple =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
A__ : List[Any] =parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
A__ : Union[str, Any] =None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A__ : Union[str, Any] =get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', handlers=[logging.StreamHandler(sys.stdout )], )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
# 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''', A__ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
A__ : str =datasets.load_dataset(
'''common_voice''', data_args.dataset_config_name, split=data_args.train_split_name )
A__ : Dict =datasets.load_dataset('''common_voice''', data_args.dataset_config_name, split='''test''' )
# Create and save tokenizer
A__ : Optional[int] =f'[{"".join(data_args.chars_to_ignore )}]'
def remove_special_characters(snake_case_ ):
A__ : Union[str, Any] =re.sub(A__, '''''', batch['''sentence'''] ).lower() + ' '
return batch
A__ : Union[str, Any] =train_dataset.map(A__, remove_columns=['''sentence'''] )
A__ : str =eval_dataset.map(A__, remove_columns=['''sentence'''] )
def extract_all_chars(snake_case_ ):
A__ : Tuple =' '.join(batch['''text'''] )
A__ : Any =list(set(A__ ) )
return {"vocab": [vocab], "all_text": [all_text]}
A__ : Tuple =train_dataset.map(
A__, batched=A__, batch_size=-1, keep_in_memory=A__, remove_columns=train_dataset.column_names, )
A__ : Any =train_dataset.map(
A__, batched=A__, batch_size=-1, keep_in_memory=A__, remove_columns=eval_dataset.column_names, )
A__ : Optional[Any] =list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
A__ : List[str] ={v: k for k, v in enumerate(A__ )}
A__ : List[str] =vocab_dict[' ']
del vocab_dict[" "]
A__ : Tuple =len(A__ )
A__ : Dict =len(A__ )
with open('''vocab.json''', '''w''' ) as vocab_file:
json.dump(A__, A__ )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A__ : str =WavaVecaCTCTokenizer(
'''vocab.json''', unk_token='''[UNK]''', pad_token='''[PAD]''', word_delimiter_token='''|''', )
A__ : Any =WavaVecaFeatureExtractor(
feature_size=1, sampling_rate=1_6_0_0_0, padding_value=0.0, do_normalize=A__, return_attention_mask=A__ )
A__ : int =WavaVecaProcessor(feature_extractor=A__, tokenizer=A__ )
A__ : List[Any] =WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path, cache_dir=model_args.cache_dir, activation_dropout=model_args.activation_dropout, attention_dropout=model_args.attention_dropout, hidden_dropout=model_args.hidden_dropout, feat_proj_dropout=model_args.feat_proj_dropout, mask_time_prob=model_args.mask_time_prob, gradient_checkpointing=training_args.gradient_checkpointing, layerdrop=model_args.layerdrop, ctc_loss_reduction='''mean''', pad_token_id=processor.tokenizer.pad_token_id, vocab_size=len(processor.tokenizer ), )
if data_args.max_train_samples is not None:
A__ : Any =min(len(A__ ), data_args.max_train_samples )
A__ : Any =train_dataset.select(range(A__ ) )
if data_args.max_val_samples is not None:
A__ : List[str] =eval_dataset.select(range(data_args.max_val_samples ) )
A__ : List[str] =torchaudio.transforms.Resample(4_8_0_0_0, 1_6_0_0_0 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(snake_case_ ):
A__ : Optional[int] =torchaudio.load(batch['''path'''] )
A__ : Tuple =resampler(A__ ).squeeze().numpy()
A__ : Any =1_6_0_0_0
A__ : int =batch['text']
return batch
A__ : Dict =train_dataset.map(
A__, remove_columns=train_dataset.column_names, num_proc=data_args.preprocessing_num_workers, )
A__ : List[Any] =eval_dataset.map(
A__, remove_columns=eval_dataset.column_names, num_proc=data_args.preprocessing_num_workers, )
def prepare_dataset(snake_case_ ):
# check that all files have the correct sampling rate
assert (
len(set(batch['''sampling_rate'''] ) ) == 1
), f'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'
A__ : Tuple =processor(
audio=batch['''speech'''], text=batch['''target_text'''], sampling_rate=batch['''sampling_rate'''][0] )
batch.update(A__ )
return batch
A__ : int =train_dataset.map(
A__, remove_columns=train_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=A__, num_proc=data_args.preprocessing_num_workers, )
A__ : Any =eval_dataset.map(
A__, remove_columns=eval_dataset.column_names, batch_size=training_args.per_device_train_batch_size, batched=A__, num_proc=data_args.preprocessing_num_workers, )
# Metric
A__ : int =datasets.load_metric('''wer''' )
def compute_metrics(snake_case_ ):
A__ : Optional[Any] =pred.predictions
A__ : int =np.argmax(A__, axis=-1 )
A__ : Any =processor.tokenizer.pad_token_id
A__ : int =processor.batch_decode(A__ )
# we do not want to group tokens when computing the metrics
A__ : Tuple =processor.batch_decode(pred.label_ids, group_tokens=A__ )
A__ : Union[str, Any] =wer_metric.compute(predictions=A__, references=A__ )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
A__ : Any =DataCollatorCTCWithPadding(processor=A__, padding=A__ )
# Initialize our Trainer
A__ : List[Any] =CTCTrainer(
model=A__, data_collator=A__, args=A__, compute_metrics=A__, train_dataset=train_dataset if training_args.do_train else None, eval_dataset=eval_dataset if training_args.do_eval else None, tokenizer=processor.feature_extractor, )
# Training
if training_args.do_train:
if last_checkpoint is not None:
A__ : str =last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
A__ : Union[str, Any] =model_args.model_name_or_path
else:
A__ : Optional[Any] =None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
A__ : List[str] =trainer.train(resume_from_checkpoint=A__ )
trainer.save_model()
A__ : Dict =train_result.metrics
A__ : List[Any] =(
data_args.max_train_samples if data_args.max_train_samples is not None else len(A__ )
)
A__ : List[Any] =min(A__, len(A__ ) )
trainer.log_metrics('''train''', A__ )
trainer.save_metrics('''train''', A__ )
trainer.save_state()
# Evaluation
A__ : Tuple ={}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
A__ : Dict =trainer.evaluate()
A__ : Tuple =data_args.max_val_samples if data_args.max_val_samples is not None else len(A__ )
A__ : Any =min(A__, len(A__ ) )
trainer.log_metrics('''eval''', A__ )
trainer.save_metrics('''eval''', A__ )
return results
if __name__ == "__main__":
main()
| 416
|
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , lowercase = "▁" , lowercase = True , lowercase = "<unk>" , lowercase = "</s>" , lowercase = "<pad>" , ) -> str:
'''simple docstring'''
a__ : Optional[Any] = {
'pad': {'id': 0, 'token': pad_token},
'eos': {'id': 1, 'token': eos_token},
'unk': {'id': 2, 'token': unk_token},
}
a__ : List[str] = [None] * len(self.special_tokens)
for token_dict in self.special_tokens.values():
a__ : Union[str, Any] = token_dict['token']
a__ : Any = Tokenizer(Unigram())
a__ : Any = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(' {2,}') , ' '),
normalizers.Lowercase(),
])
a__ : Optional[int] = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=lowercase , add_prefix_space=lowercase),
pre_tokenizers.Digits(individual_digits=lowercase),
pre_tokenizers.Punctuation(),
])
a__ : Dict = decoders.Metaspace(replacement=lowercase , add_prefix_space=lowercase)
a__ : int = TemplateProcessing(
single=F'$A {self.special_tokens["eos"]["token"]}' , special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])] , )
a__ : str = {
'model': 'SentencePieceUnigram',
'replacement': replacement,
'add_prefix_space': add_prefix_space,
}
super().__init__(lowercase , lowercase)
def __lowercase ( self , lowercase , lowercase = 8000 , lowercase = True , ) -> Any:
'''simple docstring'''
a__ : int = trainers.UnigramTrainer(
vocab_size=lowercase , special_tokens=self.special_tokens_list , show_progress=lowercase , )
if isinstance(lowercase , lowercase):
a__ : List[Any] = [files]
self._tokenizer.train(lowercase , trainer=lowercase)
self.add_unk_id()
def __lowercase ( self , lowercase , lowercase = 8000 , lowercase = True , ) -> Dict:
'''simple docstring'''
a__ : str = trainers.UnigramTrainer(
vocab_size=lowercase , special_tokens=self.special_tokens_list , show_progress=lowercase , )
self._tokenizer.train_from_iterator(lowercase , trainer=lowercase)
self.add_unk_id()
def __lowercase ( self) -> Tuple:
'''simple docstring'''
a__ : List[str] = json.loads(self._tokenizer.to_str())
a__ : List[Any] = self.special_tokens['unk']['id']
a__ : str = Tokenizer.from_str(json.dumps(lowercase))
| 302
| 0
|
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase = 42
__UpperCAmelCase = None
def lowercase_ ( _UpperCamelCase , _UpperCamelCase=0.999 , _UpperCamelCase="cosine" , ):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(_UpperCamelCase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(_UpperCamelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
__lowercase = []
for i in range(_UpperCamelCase ):
__lowercase = i / num_diffusion_timesteps
__lowercase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(_UpperCamelCase ) / alpha_bar_fn(_UpperCamelCase ) , _UpperCamelCase ) )
return torch.tensor(_UpperCamelCase , dtype=torch.floataa )
class lowerCamelCase_ ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase = 1
@register_to_config
def __init__( self , snake_case_ = 1_0_0_0 , snake_case_ = 0.0_0_0_1 , snake_case_ = 0.0_2 , snake_case_ = "linear" , snake_case_ = None , snake_case_ = True , snake_case_ = True , snake_case_ = 0 , snake_case_ = "epsilon" , snake_case_ = 1.0 , **snake_case_ , ) -> Any:
'''simple docstring'''
if kwargs.get('''set_alpha_to_one''' , snake_case_ ) is not None:
__lowercase = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , snake_case_ , standard_warn=snake_case_ )
__lowercase = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
__lowercase = torch.tensor(snake_case_ , dtype=torch.floataa )
elif beta_schedule == "linear":
__lowercase = torch.linspace(snake_case_ , snake_case_ , snake_case_ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__lowercase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , snake_case_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__lowercase = betas_for_alpha_bar(snake_case_ )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
__lowercase = 1.0 - self.betas
__lowercase = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
__lowercase = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
__lowercase = 1.0
# setable values
__lowercase = None
__lowercase = torch.from_numpy(np.arange(0 , snake_case_ ).copy().astype(np.intaa ) )
def A ( self , snake_case_ , snake_case_ = None ) -> torch.FloatTensor:
'''simple docstring'''
return sample
def A ( self , snake_case_ , snake_case_ = None ) -> int:
'''simple docstring'''
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
F' maximal {self.config.num_train_timesteps} timesteps.' )
__lowercase = num_inference_steps
__lowercase = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
__lowercase = (np.arange(0 , snake_case_ ) * step_ratio).round().copy().astype(np.intaa )
__lowercase = torch.from_numpy(snake_case_ ).to(snake_case_ )
self.timesteps += self.config.steps_offset
def A ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ = 0.0 , snake_case_ = False , snake_case_ = None , snake_case_ = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
'''simple docstring'''
__lowercase = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
__lowercase = self.alphas_cumprod[timestep]
__lowercase = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
__lowercase = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
__lowercase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
__lowercase = model_output
elif self.config.prediction_type == "sample":
__lowercase = model_output
__lowercase = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
__lowercase = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
__lowercase = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
__lowercase = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__lowercase = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
__lowercase = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=snake_case_ , pred_original_sample=snake_case_ )
def __len__( self ) -> Any:
'''simple docstring'''
return self.config.num_train_timesteps
| 527
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowerCamelCase_ ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = FlaxAutoencoderKL
@property
def A ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = 4
__lowercase = 3
__lowercase = (3_2, 3_2)
__lowercase = jax.random.PRNGKey(0 )
__lowercase = jax.random.uniform(snake_case_ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = {
'''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,
}
__lowercase = self.dummy_input
return init_dict, inputs_dict
| 527
| 1
|
import os
# Precomputes a list of the 100 first triangular numbers
__A = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
lowerCamelCase__: Optional[Any] =os.path.dirname(os.path.realpath(__a ) )
lowerCamelCase__: Optional[int] =os.path.join(__a , "words.txt" )
lowerCamelCase__: Dict =""
with open(__a ) as f:
lowerCamelCase__: int =f.readline()
lowerCamelCase__: Any =[word.strip("\"" ) for word in words.strip("\r\n" ).split("," )]
lowerCamelCase__: Optional[Any] =[
word
for word in [sum(ord(__a ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(__a )
if __name__ == "__main__":
print(solution())
| 59
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase__ = '''maskformer-swin'''
UpperCAmelCase__ = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Optional[int] , lowercase__ : List[Any]=224 , lowercase__ : Optional[Any]=4 , lowercase__ : Optional[Any]=3 , lowercase__ : List[str]=96 , lowercase__ : Dict=[2, 2, 6, 2] , lowercase__ : Tuple=[3, 6, 12, 24] , lowercase__ : Optional[Any]=7 , lowercase__ : Any=4.0 , lowercase__ : List[str]=True , lowercase__ : Optional[int]=0.0 , lowercase__ : Dict=0.0 , lowercase__ : Tuple=0.1 , lowercase__ : Any="gelu" , lowercase__ : Union[str, Any]=False , lowercase__ : Optional[int]=0.0_2 , lowercase__ : Tuple=1e-5 , lowercase__ : Dict=None , lowercase__ : List[Any]=None , **lowercase__ : Optional[Any] , ) ->List[str]:
'''simple docstring'''
super().__init__(**lowercase__ )
_UpperCamelCase : List[Any] = image_size
_UpperCamelCase : Any = patch_size
_UpperCamelCase : Union[str, Any] = num_channels
_UpperCamelCase : Dict = embed_dim
_UpperCamelCase : List[Any] = depths
_UpperCamelCase : str = len(lowercase__ )
_UpperCamelCase : List[Any] = num_heads
_UpperCamelCase : str = window_size
_UpperCamelCase : Optional[Any] = mlp_ratio
_UpperCamelCase : Optional[Any] = qkv_bias
_UpperCamelCase : str = hidden_dropout_prob
_UpperCamelCase : List[Any] = attention_probs_dropout_prob
_UpperCamelCase : Union[str, Any] = drop_path_rate
_UpperCamelCase : str = hidden_act
_UpperCamelCase : Any = use_absolute_embeddings
_UpperCamelCase : Tuple = layer_norm_eps
_UpperCamelCase : Union[str, 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
_UpperCamelCase : Dict = int(embed_dim * 2 ** (len(lowercase__ ) - 1) )
_UpperCamelCase : List[Any] = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(lowercase__ ) + 1 )]
_UpperCamelCase , _UpperCamelCase : Optional[Any] = get_aligned_output_features_output_indices(
out_features=lowercase__ , out_indices=lowercase__ , stage_names=self.stage_names )
| 435
| 0
|
'''simple docstring'''
from math import ceil, sqrt
def _a( UpperCamelCase__ : int = 1_0_0_0_0_0_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] =0
for outer_width in range(3, (limit // 4) + 2 ):
if outer_width**2 > limit:
SCREAMING_SNAKE_CASE__ : List[str] =max(ceil(sqrt(outer_width**2 - limit ) ), 1 )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 665
|
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __SCREAMING_SNAKE_CASE :
@staticmethod
def __magic_name__ ( *__lowercase : int , **__lowercase : Optional[Any] ) -> Optional[Any]:
pass
@is_pipeline_test
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __magic_name__ ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , )
SCREAMING_SNAKE_CASE__ : List[str] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(__lowercase ) , [
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
] , )
SCREAMING_SNAKE_CASE__ : int =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] =pipeline(
model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification''' , framework='''tf''' )
SCREAMING_SNAKE_CASE__ : Dict =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Tuple =image_classifier(__lowercase , candidate_labels=['''a''', '''b''', '''c'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''A''', '''B''', '''C'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
[
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
{'''score''': 0.333, '''label''': ANY(__lowercase )},
],
] , )
@slow
@require_torch
def __magic_name__ ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Any =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : List[str] =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
@slow
@require_tf
def __magic_name__ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ : str =pipeline(
task='''zero-shot-image-classification''' , model='''openai/clip-vit-base-patch32''' , framework='''tf''' )
# This is an image of 2 cats with remotes and no planes
SCREAMING_SNAKE_CASE__ : Optional[Any] =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
SCREAMING_SNAKE_CASE__ : Any =image_classifier(__lowercase , candidate_labels=['''cat''', '''plane''', '''remote'''] )
self.assertEqual(
nested_simplify(__lowercase ) , [
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =image_classifier([image] * 5 , candidate_labels=['''cat''', '''plane''', '''remote'''] , batch_size=2 )
self.assertEqual(
nested_simplify(__lowercase ) , [
[
{'''score''': 0.511, '''label''': '''remote'''},
{'''score''': 0.485, '''label''': '''cat'''},
{'''score''': 0.004, '''label''': '''plane'''},
],
]
* 5 , )
| 665
| 1
|
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