





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__A							:			Union[str, Any]         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			['input_features', 'is_longer']




     def __init__(    self				,     snake_case_=64				,     snake_case_=4_8000				,     snake_case_=480				,     snake_case_=10				,     snake_case_=1024				,     snake_case_=0.0				,     snake_case_=False				,     snake_case_ = 0				,     snake_case_ = 1_4000				,     snake_case_ = None				,     snake_case_ = "fusion"				,     snake_case_ = "repeatpad"				,     **snake_case_				,     ):
         super().__init__(
             feature_size=snake_case_				,     sampling_rate=snake_case_				,     padding_value=snake_case_				,     return_attention_mask=snake_case_				,     **snake_case_				,     )
         _A     =							top_db
         _A     =							truncation
         _A     =							padding
         _A     =							fft_window_size
         _A     =							(fft_window_size >> 1) + 1
         _A     =							hop_length
         _A     =							max_length_s
         _A     =							max_length_s * sampling_rate
         _A     =							sampling_rate
         _A     =							frequency_min
         _A     =							frequency_max
         _A     =							mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins				,     num_mel_filters=snake_case_				,     min_frequency=snake_case_				,     max_frequency=snake_case_				,     sampling_rate=snake_case_				,     norm=snake_case_				,     mel_scale='htk'				,     )
         _A     =							mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins				,     num_mel_filters=snake_case_				,     min_frequency=snake_case_				,     max_frequency=snake_case_				,     sampling_rate=snake_case_				,     norm='slaney'				,     mel_scale='slaney'				,     )




     def        lowerCAmelCase__		(    self							):
         _A     =							copy.deepcopy(self.__dict__							)
         _A     =							self.__class__.__name__
         if "mel_filters" in output:
             del output["mel_filters"]
         if "mel_filters_slaney" in output:
             del output["mel_filters_slaney"]
         return output




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None							):
         _A     =							spectrogram(
             snake_case_				,     window_function(self.fft_window_size				,     'hann'							)				,     frame_length=self.fft_window_size				,     hop_length=self.hop_length				,     power=2.0				,     mel_filters=snake_case_				,     log_mel='dB'				,     )
         return log_mel_spectrogram.T




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_							):
         _A     =							np.array_split(list(range(0				,     total_frames - chunk_frames + 1							)							)				,     3							)
         if len(ranges[1]							) == 0:
             # if the audio is too short, we just use the first chunk
             _A     =							[0]
         if len(ranges[2]							) == 0:
             # if the audio is too short, we just use the first chunk
             _A     =							[0]
         # randomly choose index for each part
         _A     =							np.random.choice(ranges[0]							)
         _A     =							np.random.choice(ranges[1]							)
         _A     =							np.random.choice(ranges[2]							)

         _A     =							mel[idx_front : idx_front + chunk_frames, :]
         _A     =							mel[idx_middle : idx_middle + chunk_frames, :]
         _A     =							mel[idx_back : idx_back + chunk_frames, :]

         _A     =							torch.tensor(mel[None, None, :]							)
         _A     =							torch.nn.functional.interpolate(
             snake_case_				,     size=[chunk_frames, 64]				,     mode='bilinear'				,     align_corners=snake_case_							)
         _A     =							mel_shrink[0][0].numpy()
         _A     =							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,     axis=0							)
         return mel_fusion




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         if waveform.shape[0] > max_length:
             if truncation == "rand_trunc":
                 _A     =							True
                 # random crop to max_length (for compatibility) -> this should be handled by self.pad
                 _A     =							len(snake_case_							) - max_length
                 _A     =							np.random.randint(0				,     overflow + 1							)
                 _A     =							waveform[idx : idx + max_length]
                 _A     =							self._np_extract_fbank_features(snake_case_				,     self.mel_filters_slaney							)[None, :]
             elif truncation == "fusion":
                 _A     =							self._np_extract_fbank_features(snake_case_				,     self.mel_filters							)
                 _A     =							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                 _A     =							mel.shape[0]
                 if chunk_frames == total_frames:
                     # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                     # In this case, we just use the whole audio.
                     _A     =							np.stack([mel, mel, mel, mel]				,     axis=0							)
                     _A     =							False
                 else:
                     _A     =							self._random_mel_fusion(snake_case_				,     snake_case_				,     snake_case_							)
                     _A     =							True
             else:
                 raise NotImplementedError(F"data_truncating {truncation} not implemented"							)

         else:
             _A     =							False
             # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
             if waveform.shape[0] < max_length:
                 if padding == "repeat":
                     _A     =							int(max_length / len(snake_case_							)							)
                     _A     =							np.stack(np.tile(snake_case_				,     n_repeat + 1							)							)[:max_length]
                 if padding == "repeatpad":
                     _A     =							int(max_length / len(snake_case_							)							)
                     _A     =							np.stack(np.tile(snake_case_				,     snake_case_							)							)
                 _A     =							np.pad(snake_case_				,     (0, max_length - waveform.shape[0])				,     mode='constant'				,     constant_values=0							)

             if truncation == "fusion":
                 _A     =							self._np_extract_fbank_features(snake_case_				,     self.mel_filters							)
                 _A     =							np.stack([input_mel, input_mel, input_mel, input_mel]				,     axis=0							)
             else:
                 _A     =							self._np_extract_fbank_features(snake_case_				,     self.mel_filters_slaney							)[None, :]

         return input_mel, longer




     def __call__(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							truncation if truncation is not None else self.truncation
         _A     =							padding if padding else self.padding

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                     F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                     F" was sampled with {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.'							)

         _A     =							isinstance(snake_case_				,     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}"							)
         _A     =							is_batched_numpy or (
             isinstance(snake_case_				,     (list, tuple)							) and (isinstance(raw_speech[0]				,     (np.ndarray, tuple, list)							))
         )

         if is_batched:
             _A     =							[np.asarray(snake_case_				,     dtype=np.floataa							) for speech in raw_speech]
         elif not is_batched and not isinstance(snake_case_				,     np.ndarray							):
             _A     =							np.asarray(snake_case_				,     dtype=np.floataa							)
         elif isinstance(snake_case_				,     np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
             _A     =							raw_speech.astype(np.floataa							)

         # always return batch
         if not is_batched:
             _A     =							[np.asarray(snake_case_							)]

         # convert to mel spectrogram, truncate and pad if needed.
         _A     =							[
             self._get_input_mel(snake_case_				,     max_length if max_length else self.nb_max_samples				,     snake_case_				,     snake_case_							)
             for waveform in raw_speech
         ]

         _A     =							[]
         _A     =							[]
         for mel, longer in padded_inputs:
             input_mel.append(snake_case_							)
             is_longer.append(snake_case_							)

         if truncation == "fusion" and sum(snake_case_							) == 0:
             # if no audio is longer than 10s, then randomly select one audio to be longer
             _A     =							np.random.randint(0				,     len(snake_case_							)							)
             _A     =							True

         if isinstance(input_mel[0]				,     snake_case_							):
             _A     =							[np.asarray(snake_case_				,     dtype=np.floataa							) for feature in input_mel]

         # is_longer is a list of bool
         _A     =							[[longer] for longer in is_longer]

         _A     =							{'input_features': input_mel, 'is_longer': is_longer}
         _A     =							BatchFeature(snake_case_							)

         if return_tensors is not None:
             _A     =							input_features.convert_to_tensors(snake_case_							)

         return input_features












'''simple docstring'''




import argparse
import os
import re


lowercase__       : Optional[int]         =       '''src/diffusers'''

# Pattern that looks at the indentation in a line.
lowercase__       : Dict         =       re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__       : List[str]         =       re.compile(r'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase__       : Tuple         =       re.compile(r'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase__       : str         =       re.compile(r'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase__       : str         =       re.compile(r'''\[([^\]]+)\]''')






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple:
     __A		:					List[Any]						=			_re_indent.search(__snake_case     )
     return "" if search is None else search.groups()[0]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					str=""					,				__snake_case    :					Any=None					,				__snake_case    :					List[Any]=None     )	->	Optional[int]:
     __A		:					Tuple						=			0
     __A		:					Optional[int]						=			code.split('\n'     )
     if start_prompt is not None:
          while not lines[index].startswith(__snake_case     ):
               index += 1
          __A		:					Optional[int]						=			['\n'.join(lines[:index]     )]
     else:
          __A		:					Any						=			[]

     # We split into blocks until we get to the `end_prompt` (or the end of the block).
     __A		:					Tuple						=			[lines[index]]
     index += 1
     while index < len(__snake_case     ) and (end_prompt is None or not lines[index].startswith(__snake_case     )):
          if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
               if len(__snake_case     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
                    current_block.append(lines[index]     )
                    blocks.append('\n'.join(__snake_case     )     )
                    if index < len(__snake_case     ) - 1:
                         __A		:					Union[str, Any]						=			[lines[index + 1]]
                         index += 1
                    else:
                         __A		:					Union[str, Any]						=			[]
               else:
                    blocks.append('\n'.join(__snake_case     )     )
                    __A		:					Optional[Any]						=			[lines[index]]
          else:
               current_block.append(lines[index]     )
          index += 1

     # Adds current block if it's nonempty.
     if len(__snake_case     ) > 0:
          blocks.append('\n'.join(__snake_case     )     )

     # Add final block after end_prompt if provided.
     if end_prompt is not None and index < len(__snake_case     ):
          blocks.append('\n'.join(lines[index:]     )     )

     return blocks






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:

     def _inner(__snake_case    :					List[Any]     ):
          return key(__snake_case     ).lower().replace('_'					,				''     )

     return _inner






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any=None     )	->	List[Any]:

     # If no key is provided, we use a noop.
     def noop(__snake_case    :					List[Any]     ):
          return x

     if key is None:
          __A		:					Optional[Any]						=			noop
     # Constants are all uppercase, they go first.
     __A		:					str						=			[obj for obj in objects if key(__snake_case     ).isupper()]
     # Classes are not all uppercase but start with a capital, they go second.
     __A		:					List[str]						=			[obj for obj in objects if key(__snake_case     )[0].isupper() and not key(__snake_case     ).isupper()]
     # Functions begin with a lowercase, they go last.
     __A		:					str						=			[obj for obj in objects if not key(__snake_case     )[0].isupper()]

     __A		:					Tuple						=			ignore_underscore(__snake_case     )
     return sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	Tuple:

     # This inner function sort imports between [ ].
     def _replace(__snake_case    :					Tuple     ):
          __A		:					List[str]						=			match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          __A		:					int						=			[part.strip().replace('"'					,				''     ) for part in imports.split(','     )]
          # We will have a final empty element if the line finished with a comma.
          if len(keys[-1]     ) == 0:
               __A		:					Dict						=			keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case     )]     ) + "]"

     __A		:					List[Any]						=			import_statement.split('\n'     )
     if len(__snake_case     ) > 3:
          # Here we have to sort internal imports that are on several lines (one per name):
          # key: [
          #     "object1",
          #     "object2",
          #     ...
          # ]

          # We may have to ignore one or two lines on each side.
          __A		:					Optional[int]						=			2 if lines[1].strip() == '[' else 1
          __A		:					Any						=			[(i, _re_strip_line.search(__snake_case     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
          __A		:					Optional[int]						=			sort_objects(__snake_case					,				key=lambda __snake_case     : x[1]     )
          __A		:					Any						=			[lines[x[0] + idx] for x in sorted_indices]
          return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
     elif len(__snake_case     ) == 3:
          # Here we have to sort internal imports that are on one separate line:
          # key: [
          #     "object1", "object2", ...
          # ]
          if _re_bracket_content.search(lines[1]     ) is not None:
               __A		:					Union[str, Any]						=			_re_bracket_content.sub(_replace					,				lines[1]     )
          else:
               __A		:					Dict						=			[part.strip().replace('"'					,				''     ) for part in lines[1].split(','     )]
               # We will have a final empty element if the line finished with a comma.
               if len(keys[-1]     ) == 0:
                    __A		:					Tuple						=			keys[:-1]
               __A		:					List[Any]						=			get_indent(lines[1]     ) + ', '.join([f'"{k}"' for k in sort_objects(__snake_case     )]     )
          return "\n".join(__snake_case     )
     else:
          # Finally we have to deal with imports fitting on one line
          __A		:					Optional[Any]						=			_re_bracket_content.sub(_replace					,				__snake_case     )
          return import_statement






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					List[Any]=True     )	->	Optional[Any]:
     with open(__snake_case					,				'r'     ) as f:
          __A		:					Dict						=			f.read()

     if "_import_structure" not in code:
          return

     # Blocks of indent level 0
     __A		:					str						=			split_code_in_indented_blocks(
         __snake_case					,				start_prompt='_import_structure = {'					,				end_prompt='if TYPE_CHECKING:'     )

     # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1					,				len(__snake_case     ) - 1     ):
          # Check if the block contains some `_import_structure`s thingy to sort.
          __A		:					Tuple						=			main_blocks[block_idx]
          __A		:					int						=			block.split('\n'     )

          # Get to the start of the imports.
          __A		:					Tuple						=			0
          while line_idx < len(__snake_case     ) and "_import_structure" not in block_lines[line_idx]:
               # Skip dummy import blocks
               if "import dummy" in block_lines[line_idx]:
                    __A		:					Optional[int]						=			len(__snake_case     )
               else:
                    line_idx += 1
          if line_idx >= len(__snake_case     ):
               continue

          # Ignore beginning and last line: they don't contain anything.
          __A		:					Dict						=			'\n'.join(block_lines[line_idx:-1]     )
          __A		:					int						=			get_indent(block_lines[1]     )
          # Slit the internal block into blocks of indent level 1.
          __A		:					Optional[int]						=			split_code_in_indented_blocks(__snake_case					,				indent_level=__snake_case     )
          # We have two categories of import key: list or _import_structure[key].append/extend
          __A		:					Any						=			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
          # Grab the keys, but there is a trap: some lines are empty or just comments.
          __A		:					Dict						=			[(pattern.search(__snake_case     ).groups()[0] if pattern.search(__snake_case     ) is not None else None) for b in internal_blocks]
          # We only sort the lines with a key.
          __A		:					Optional[Any]						=			[(i, key) for i, key in enumerate(__snake_case     ) if key is not None]
          __A		:					Tuple						=			[x[0] for x in sorted(__snake_case					,				key=lambda __snake_case     : x[1]     )]

          # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
          __A		:					str						=			0
          __A		:					Any						=			[]
          for i in range(len(__snake_case     )     ):
               if keys[i] is None:
                    reordered_blocks.append(internal_blocks[i]     )
               else:
                    __A		:					str						=			sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
                    reordered_blocks.append(__snake_case     )
                    count += 1

        # And we put our main block back together with its first and last line.
          __A		:					int						=			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]     )

     if code != "\n".join(__snake_case     ):
          if check_only:
               return True
          else:
               print(f'Overwriting {file}.'     )
               with open(__snake_case					,				'w'     ) as f:
                    f.write('\n'.join(__snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					int=True     )	->	Optional[Any]:
     __A		:					Tuple						=			[]
     for root, _, files in os.walk(__snake_case     ):
          if "__init__.py" in files:
               __A		:					List[Any]						=			sort_imports(os.path.join(__snake_case					,				'__init__.py'     )					,				check_only=__snake_case     )
               if result:
                    __A		:					Dict						=			[os.path.join(__snake_case					,				'__init__.py'     )]
     if len(__snake_case     ) > 0:
          raise ValueError(f'Would overwrite {len(__snake_case     )} files, run `make style`.'     )


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       argparse.ArgumentParser()
  parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
  lowercase__       : Union[str, Any]         =       parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)