



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 				__snake_case						(     UpperCamelCase_						):
							# to overwrite at feature extractactor specific tests
							_a			      = None
							_a			      = None




							@property
							def    UpperCAmelCase__			(       self     :       int):
									return self.feat_extract_tester.prepare_feat_extract_dict()




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     List[str]									= self.feature_extraction_class(**self.feat_extract_dict)
									self.assertTrue(hasattr(A_				,				'''feature_size'''))
									self.assertTrue(hasattr(A_				,				'''sampling_rate'''))
									self.assertTrue(hasattr(A_				,				'''padding_value'''))




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     List[Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     int									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     str									= BatchFeature({input_name: speech_inputs})

									self.assertTrue(all(len(A_) == len(A_) for x, y in zip(A_				,				processed_features[input_name])))

									lowerCAmelCase_			:     str									= self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
									lowerCAmelCase_			:     Union[str, Any]									= BatchFeature({input_name: speech_inputs}				,				tensor_type='''np''')

									lowerCAmelCase_			:     Optional[Any]									= processed_features[input_name]

									if len(batch_features_input.shape) < 3:
											lowerCAmelCase_			:     Union[str, 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     :       Any):
									lowerCAmelCase_			:     str									= self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
									lowerCAmelCase_			:     Optional[Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     List[Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     int									= BatchFeature({input_name: speech_inputs}				,				tensor_type='''pt''')

									lowerCAmelCase_			:     List[str]									= processed_features[input_name]

									if len(batch_features_input.shape) < 3:
											lowerCAmelCase_			:     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_tf
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
									lowerCAmelCase_			:     Union[str, Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     Optional[Any]									= BatchFeature({input_name: speech_inputs}				,				tensor_type='''tf''')

									lowerCAmelCase_			:     Optional[Any]									= processed_features[input_name]

									if len(batch_features_input.shape) < 3:
											lowerCAmelCase_			:     List[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))




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]=False):
									def _inputs_have_equal_length(A_     :       Optional[Any]):
											lowerCAmelCase_			:     Optional[Any]									= len(input[0])
											for input_slice in input[1:]:
													if len(A_) != length:
															return False
											return True

									def _inputs_are_equal(A_     :       Union[str, Any]				,				A_     :       str):
											if len(A_) != len(A_):
													return False

											for input_slice_a, input_slice_a in zip(A_				,				A_):
													if not np.allclose(np.asarray(A_)				,				np.asarray(A_)				,				atol=1e-3):
															return False
											return True

									lowerCAmelCase_			:     int									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     Dict									= self.feat_extract_tester.prepare_inputs_for_common(numpify=A_)
									lowerCAmelCase_			:     List[Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[str]									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     Optional[int]									= self.feat_extract_tester.seq_length_diff
									lowerCAmelCase_			:     int									= self.feat_extract_tester.max_seq_length + pad_diff
									lowerCAmelCase_			:     List[str]									= self.feat_extract_tester.min_seq_length
									lowerCAmelCase_			:     int									= self.feat_extract_tester.batch_size
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.feature_size

									# test padding for List[int] + numpy
									lowerCAmelCase_			:     Dict									= feat_extract.pad(A_				,				padding=A_)
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									lowerCAmelCase_			:     Optional[int]									= feat_extract.pad(A_				,				padding='''longest''')
									lowerCAmelCase_			:     List[str]									= input_a[input_name]

									lowerCAmelCase_			:     Optional[Any]									= feat_extract.pad(A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[-1]))
									lowerCAmelCase_			:     Any									= input_a[input_name]

									lowerCAmelCase_			:     Any									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									# max_length parameter has to be provided when setting `padding="max_length"`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''max_length''')[input_name]

									lowerCAmelCase_			:     Dict									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=A_				,				return_tensors='''np''')
									lowerCAmelCase_			:     str									= input_a[input_name]

									self.assertFalse(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_are_equal(A_				,				A_))
									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
									lowerCAmelCase_			:     Optional[int]									= feat_extract.pad(A_				,				pad_to_multiple_of=1_0)
									lowerCAmelCase_			:     List[Any]									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(A_				,				padding='''longest'''				,				pad_to_multiple_of=1_0)
									lowerCAmelCase_			:     Optional[int]									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				pad_to_multiple_of=1_0				,				max_length=A_)
									lowerCAmelCase_			:     Optional[Any]									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				pad_to_multiple_of=1_0				,				max_length=A_				,				return_tensors='''np'''				,				)
									lowerCAmelCase_			:     Union[str, Any]									= input_a[input_name]

									self.assertTrue(all(len(A_) % 1_0 == 0 for x in input_a))
									self.assertTrue(_inputs_are_equal(A_				,				A_))

									lowerCAmelCase_			:     str									= pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
									self.assertTrue(all(len(A_) == 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
									lowerCAmelCase_			:     List[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     :       List[Any]				,				A_     :       List[Any]=False):
									def _inputs_have_equal_length(A_     :       Optional[Any]):
											lowerCAmelCase_			:     Optional[Any]									= len(input[0])
											for input_slice in input[1:]:
													if len(A_) != length:
															return False
											return True

									def _inputs_are_equal(A_     :       List[str]				,				A_     :       Optional[Any]):
											if len(A_) != len(A_):
													return False

											for input_slice_a, input_slice_a in zip(A_				,				A_):
													if not np.allclose(np.asarray(A_)				,				np.asarray(A_)				,				atol=1e-3):
															return False
											return True

									lowerCAmelCase_			:     str									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.prepare_inputs_for_common(numpify=A_)
									lowerCAmelCase_			:     Any									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[Any]									= BatchFeature({input_name: speech_inputs})

									# truncate to smallest
									lowerCAmelCase_			:     int									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				truncation=A_)
									lowerCAmelCase_			:     int									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0]))
									lowerCAmelCase_			:     str									= input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertFalse(_inputs_have_equal_length(A_))

									# truncate to smallest with np
									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np'''				,				truncation=A_				,				)
									lowerCAmelCase_			:     Tuple									= input_a[input_name]

									lowerCAmelCase_			:     Any									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np''')
									lowerCAmelCase_			:     Optional[Any]									= input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(A_))
									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(A_))

									# truncate to middle
									lowerCAmelCase_			:     List[Any]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=A_				,				return_tensors='''np'''				,				)
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									lowerCAmelCase_			:     int									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=A_)
									lowerCAmelCase_			:     int									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				return_tensors='''np''')
									lowerCAmelCase_			:     int									= input_a[input_name]

									self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_are_equal(A_				,				A_))

									# 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(A_))
									self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				truncation=A_)[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''longest'''				,				truncation=A_)[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''longest'''				,				truncation=A_)[input_name]

									# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''max_length'''				,				truncation=A_)[input_name]

									# test truncation for `pad_to_multiple_of` for List[int] + numpy
									lowerCAmelCase_			:     Dict									= 1_2
									lowerCAmelCase_			:     Dict									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=A_				,				truncation=A_				,				)
									lowerCAmelCase_			:     Any									= input_a[input_name]

									lowerCAmelCase_			:     List[str]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=A_				,				)
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									# retrieve expected_length as multiple of pad_to_multiple_of
									lowerCAmelCase_			:     Optional[int]									= len(speech_inputs[0])
									if expected_length % pad_to_multiple_of != 0:
											lowerCAmelCase_			:     Any									= ((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(A_))
									self.assertFalse(_inputs_have_equal_length(A_))




							def    UpperCAmelCase__			(       self     :       List[Any]):
									self._check_padding(numpify=A_)




							def    UpperCAmelCase__			(       self     :       Tuple):
									self._check_padding(numpify=A_)




							def    UpperCAmelCase__			(       self     :       Dict):
									self._check_truncation(numpify=A_)




							def    UpperCAmelCase__			(       self     :       Dict):
									self._check_truncation(numpify=A_)




							@require_torch
							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     List[str]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     int									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     Dict									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[str]									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
									lowerCAmelCase_			:     str									= feat_extract.pad(A_				,				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):
									lowerCAmelCase_			:     Union[str, Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     List[str]									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     Optional[int]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[Any]									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     Optional[Any]									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
									lowerCAmelCase_			:     List[str]									= feat_extract.pad(A_				,				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     :       List[Any]):
									lowerCAmelCase_			:     Union[str, Any]									= self.feat_extract_dict
									lowerCAmelCase_			:     Optional[Any]									= True
									lowerCAmelCase_			:     str									= self.feature_extraction_class(**A_)
									lowerCAmelCase_			:     List[Any]									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     List[str]									= [len(A_) for x in speech_inputs]
									lowerCAmelCase_			:     Tuple									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     int									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     int									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')
									self.assertIn('''attention_mask'''				,				A_)
									self.assertListEqual(list(processed.attention_mask.shape)				,				list(processed[input_name].shape[:2]))
									self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,				A_)







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     int									= self.feat_extract_dict
									lowerCAmelCase_			:     Optional[int]									= True
									lowerCAmelCase_			:     str									= self.feature_extraction_class(**A_)
									lowerCAmelCase_			:     str									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     str									= [len(A_) for x in speech_inputs]
									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     Tuple									= BatchFeature({input_name: speech_inputs})
									lowerCAmelCase_			:     Any									= min(A_)

									lowerCAmelCase_			:     List[str]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=A_				,				truncation=A_				,				return_tensors='''np''')
									self.assertIn('''attention_mask'''				,				A_)
									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])









"""simple docstring"""







__lowercase    					=      {
    """Pillow""": """Pillow<10.0.0""",
    """accelerate""": """accelerate>=0.20.3""",
    """av""": """av==9.2.0""",
    """beautifulsoup4""": """beautifulsoup4""",
    """black""": """black~=23.1""",
    """codecarbon""": """codecarbon==1.2.0""",
    """cookiecutter""": """cookiecutter==1.7.3""",
    """dataclasses""": """dataclasses""",
    """datasets""": """datasets!=2.5.0""",
    """decord""": """decord==0.6.0""",
    """deepspeed""": """deepspeed>=0.9.3""",
    """diffusers""": """diffusers""",
    """dill""": """dill<0.3.5""",
    """evaluate""": """evaluate>=0.2.0""",
    """fairscale""": """fairscale>0.3""",
    """faiss-cpu""": """faiss-cpu""",
    """fastapi""": """fastapi""",
    """filelock""": """filelock""",
    """flax""": """flax>=0.4.1,<=0.7.0""",
    """ftfy""": """ftfy""",
    """fugashi""": """fugashi>=1.0""",
    """GitPython""": """GitPython<3.1.19""",
    """hf-doc-builder""": """hf-doc-builder>=0.3.0""",
    """huggingface-hub""": """huggingface-hub>=0.14.1,<1.0""",
    """importlib_metadata""": """importlib_metadata""",
    """ipadic""": """ipadic>=1.0.0,<2.0""",
    """isort""": """isort>=5.5.4""",
    """jax""": """jax>=0.2.8,!=0.3.2,<=0.4.13""",
    """jaxlib""": """jaxlib>=0.1.65,<=0.4.13""",
    """jieba""": """jieba""",
    """kenlm""": """kenlm""",
    """keras-nlp""": """keras-nlp>=0.3.1""",
    """librosa""": """librosa""",
    """nltk""": """nltk""",
    """natten""": """natten>=0.14.6""",
    """numpy""": """numpy>=1.17""",
    """onnxconverter-common""": """onnxconverter-common""",
    """onnxruntime-tools""": """onnxruntime-tools>=1.4.2""",
    """onnxruntime""": """onnxruntime>=1.4.0""",
    """opencv-python""": """opencv-python""",
    """optuna""": """optuna""",
    """optax""": """optax>=0.0.8,<=0.1.4""",
    """packaging""": """packaging>=20.0""",
    """parameterized""": """parameterized""",
    """phonemizer""": """phonemizer""",
    """protobuf""": """protobuf""",
    """psutil""": """psutil""",
    """pyyaml""": """pyyaml>=5.1""",
    """pydantic""": """pydantic<2""",
    """pytest""": """pytest>=7.2.0""",
    """pytest-timeout""": """pytest-timeout""",
    """pytest-xdist""": """pytest-xdist""",
    """python""": """python>=3.8.0""",
    """ray[tune]""": """ray[tune]""",
    """regex""": """regex!=2019.12.17""",
    """requests""": """requests""",
    """rhoknp""": """rhoknp>=1.1.0,<1.3.1""",
    """rjieba""": """rjieba""",
    """rouge-score""": """rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1""",
    """ruff""": """ruff>=0.0.241,<=0.0.259""",
    """sacrebleu""": """sacrebleu>=1.4.12,<2.0.0""",
    """sacremoses""": """sacremoses""",
    """safetensors""": """safetensors>=0.3.1""",
    """sagemaker""": """sagemaker>=2.31.0""",
    """scikit-learn""": """scikit-learn""",
    """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
    """sigopt""": """sigopt""",
    """starlette""": """starlette""",
    """sudachipy""": """sudachipy>=0.6.6""",
    """sudachidict_core""": """sudachidict_core>=20220729""",
    """tensorflow-cpu""": """tensorflow-cpu>=2.6,<2.14""",
    """tensorflow""": """tensorflow>=2.6,<2.14""",
    """tensorflow-text""": """tensorflow-text<2.14""",
    """tf2onnx""": """tf2onnx""",
    """timeout-decorator""": """timeout-decorator""",
    """timm""": """timm""",
    """tokenizers""": """tokenizers>=0.11.1,!=0.11.3,<0.14""",
    """torch""": """torch>=1.9,!=1.12.0""",
    """torchaudio""": """torchaudio""",
    """torchvision""": """torchvision""",
    """pyctcdecode""": """pyctcdecode>=0.4.0""",
    """tqdm""": """tqdm>=4.27""",
    """unidic""": """unidic>=1.0.2""",
    """unidic_lite""": """unidic_lite>=1.0.7""",
    """urllib3""": """urllib3<2.0.0""",
    """uvicorn""": """uvicorn""",
}






