


"""simple docstring"""






__snake_case		=       {
    """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""",
}



"""simple docstring"""






from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		['input_values', 'padding_mask']


     def __init__(      self    ,				__UpperCAmelCase = 1    ,				__UpperCAmelCase = 24000    ,				__UpperCAmelCase = 0.0    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(feature_size=__UpperCAmelCase    ,				sampling_rate=__UpperCAmelCase    ,				padding_value=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          _a    	=				chunk_length_s
          _a    	=				overlap


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None:
               return None
          else:
               return int(self.chunk_length_s * self.sampling_rate							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          if self.chunk_length_s is None or self.overlap is None:
               return None
          else:
               return max(1    ,				int((1.0 - self.overlap) * self.chunk_length							)							)





     def __call__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				)       ->							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 audio 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.'''							)

          if padding and truncation:
               raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
          elif padding is None:
               # by default let's pad the inputs
               _a    	=				True

          _a    	=				bool(
              isinstance(__UpperCAmelCase    ,				(list, tuple)							) and (isinstance(raw_audio[0]    ,				(np.ndarray, tuple, list)							))							)

          if is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							).T for audio in raw_audio]
          elif not is_batched and not isinstance(__UpperCAmelCase    ,				np.ndarray							):
               _a    	=				np.asarray(__UpperCAmelCase    ,				dtype=np.floataa							)
          elif isinstance(__UpperCAmelCase    ,				np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
               _a    	=				raw_audio.astype(np.floataa							)

          # always return batch
          if not is_batched:
               _a    	=				[np.asarray(__UpperCAmelCase							).T]

          # verify inputs are valid
          for idx, example in enumerate(__UpperCAmelCase							):
               if example.ndim > 2:
                    raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'							)
               if self.feature_size == 1 and example.ndim != 1:
                    raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'							)
               if self.feature_size == 2 and example.shape[-1] != 2:
                    raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'							)

          _a    	=				None
          _a    	=				BatchFeature({'''input_values''': raw_audio}							)
          if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
               if truncation:
                    _a    	=				min(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.floor(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _a    	=				max(array.shape[0] for array in raw_audio							)
                    _a    	=				int(np.ceil(max_length / self.chunk_stride							)							)
                    _a    	=				(nb_step - 1) * self.chunk_stride + self.chunk_length
                    _a    	=				'''max_length'''
               else:
                    _a    	=				input_values

        # normal padding on batch
          if padded_inputs is None:
               _a    	=				self.pad(
                   __UpperCAmelCase    ,				max_length=__UpperCAmelCase    ,				truncation=__UpperCAmelCase    ,				padding=__UpperCAmelCase    ,				return_attention_mask=__UpperCAmelCase    ,				)
               if padding:
                    _a    	=				padded_inputs.pop('''attention_mask'''							)

          _a    	=				[]
          for example in padded_inputs.pop('''input_values'''							):
               if self.feature_size == 1:
                    _a    	=				example[..., None]
               input_values.append(example.T							)

          _a    	=				input_values
          if return_tensors is not None:
               _a    	=				padded_inputs.convert_to_tensors(__UpperCAmelCase							)

          return padded_inputs