


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


SCREAMING_SNAKE_CASE			: List[Any]				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[Any]       					=     ["""input_values""", """padding_mask"""]
       def __init__(   self, lowerCamelCase = 1, lowerCamelCase = 2_40_00, lowerCamelCase = 0.0, lowerCamelCase = None, lowerCamelCase = None, **lowerCamelCase, )			->  Any:




           """simple docstring"""

           super().__init__(feature_size=lowerCamelCase, sampling_rate=lowerCamelCase, padding_value=lowerCamelCase, **lowerCamelCase)
           _lowercase			:				int 						=						chunk_length_s
           _lowercase			:				int 						=						overlap
       @property
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           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]:




           """simple docstring"""

           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, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = False, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, )			->  BatchFeature:




           """simple docstring"""

           if sampling_rate is not None:
               if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F'''The model corresponding to this feature extractor: {self} 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
               _lowercase			:				Tuple 						=						True

           _lowercase			:				Tuple 						=						bool(
               isinstance(lowerCamelCase, (list, tuple)) and (isinstance(raw_audio[0], (np.ndarray, tuple, list))))

           if is_batched:
               _lowercase			:				Tuple 						=						[np.asarray(lowerCamelCase, dtype=np.floataa).T for audio in raw_audio]
           elif not is_batched and not isinstance(lowerCamelCase, np.ndarray):
               _lowercase			:				List[str] 						=						np.asarray(lowerCamelCase, dtype=np.floataa)
           elif isinstance(lowerCamelCase, np.ndarray) and raw_audio.dtype is np.dtype(np.floataa):
               _lowercase			:				Union[str, Any] 						=						raw_audio.astype(np.floataa)

           # always return batch
           if not is_batched:
               _lowercase			:				str 						=						[np.asarray(lowerCamelCase).T]

           # verify inputs are valid
           for idx, example in enumerate(lowerCamelCase):
               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''')

           _lowercase			:				str 						=						None
           _lowercase			:				Optional[int] 						=						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:
                   _lowercase			:				str 						=						min(array.shape[0] for array in raw_audio)
                   _lowercase			:				Tuple 						=						int(np.floor(max_length / self.chunk_stride))
                   _lowercase			:				Union[str, Any] 						=						(nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                   _lowercase			:				Dict 						=						max(array.shape[0] for array in raw_audio)
                   _lowercase			:				Optional[int] 						=						int(np.ceil(max_length / self.chunk_stride))
                   _lowercase			:				List[Any] 						=						(nb_step - 1) * self.chunk_stride + self.chunk_length
                   _lowercase			:				Tuple 						=						'max_length'
               else:
                   _lowercase			:				int 						=						input_values

        # normal padding on batch
           if padded_inputs is None:
               _lowercase			:				int 						=						self.pad(
                   lowerCamelCase, max_length=lowerCamelCase, truncation=lowerCamelCase, padding=lowerCamelCase, return_attention_mask=lowerCamelCase, )
               if padding:
                   _lowercase			:				Optional[Any] 						=						padded_inputs.pop('attention_mask')

           _lowercase			:				Union[str, Any] 						=						[]
           for example in padded_inputs.pop('input_values'):
               if self.feature_size == 1:
                   _lowercase			:				Tuple 						=						example[..., None]
               input_values.append(example.T)

           _lowercase			:				Union[str, Any] 						=						input_values
           if return_tensors is not None:
               _lowercase			:				str 						=						padded_inputs.convert_to_tensors(lowerCamelCase)

           return padded_inputs





'''simple docstring'''

import os
from collections.abc import Iterator

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     for dir_path, dir_names, filenames in os.walk(lowercase__			):
          __lowercase      		=[d for d in dir_names if d != 'scripts' and d[0] not in '._']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(lowercase__			)[1] in (".py", ".ipynb"):
                    yield os.path.join(lowercase__,       lowercase__			).lstrip('./'			)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     return F'''{i * "  "}*''' if i else "\n##"

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=old_path.split(os.sep			)
     for i, new_part in enumerate(new_path.split(os.sep			)			):
          if (i + 1 > len(lowercase__			) or old_parts[i] != new_part) and new_part:
               print(F'''{md_prefix(lowercase__			)} {new_part.replace("_",       " "			).title()}'''			)
     return new_path

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     __lowercase      		=''
     for filepath in sorted(good_file_paths(lowercase__			)			):
          __lowercase		,  __lowercase      		=os.path.split(lowercase__			)
          if filepath != old_path:
               __lowercase      		=print_path(lowercase__,       lowercase__			)
          __lowercase      		=(filepath.count(os.sep			) + 1) if filepath else 0
          __lowercase      		=F'''{filepath}/{filename}'''.replace(' ',       '%20'			)
          __lowercase      		=os.path.splitext(filename.replace('_',       ' '			).title()			)[0]
          print(F'''{md_prefix(lowercase__			)} [{filename}]({url})'''			)


if __name__ == "__main__":
       print_directory_md('''.''')


