



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__    =	logging.get_logger(__name__)







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: Union[str, Any]          =		["""input_features""", """is_longer"""]

   def __init__(					self							:			int				,		lowerCAmelCase							:			List[str]=64				,		lowerCAmelCase							:			Any=4_8000				,		lowerCAmelCase							:			List[Any]=480				,		lowerCAmelCase							:			str=10				,		lowerCAmelCase							:			Tuple=1024				,		lowerCAmelCase							:			Dict=0.0				,		lowerCAmelCase							:			Optional[Any]=False				,		lowerCAmelCase							:			float = 0				,		lowerCAmelCase							:			float = 1_4000				,		lowerCAmelCase							:			int = None				,		lowerCAmelCase							:			str = "fusion"				,		lowerCAmelCase							:			str = "repeatpad"				,		**lowerCAmelCase							:			Dict				,		)				->				Optional[Any]:

    """simple docstring"""







    super().__init__(
        feature_size=lowerCAmelCase				,		sampling_rate=lowerCAmelCase				,		padding_value=lowerCAmelCase				,		return_attention_mask=lowerCAmelCase				,		**lowerCAmelCase				,		)
    _snake_case    :  int					=					top_db
    _snake_case    :  Optional[Any]					=					truncation
    _snake_case    :  int					=					padding
    _snake_case    :  Dict					=					fft_window_size
    _snake_case    :  List[str]					=					(fft_window_size >> 1) + 1
    _snake_case    :  Union[str, Any]					=					hop_length
    _snake_case    :  str					=					max_length_s
    _snake_case    :  Optional[int]					=					max_length_s * sampling_rate
    _snake_case    :  List[str]					=					sampling_rate
    _snake_case    :  int					=					frequency_min
    _snake_case    :  int					=					frequency_max
    _snake_case    :  Optional[int]					=					mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins				,		num_mel_filters=lowerCAmelCase				,		min_frequency=lowerCAmelCase				,		max_frequency=lowerCAmelCase				,		sampling_rate=lowerCAmelCase				,		norm=lowerCAmelCase				,		mel_scale="""htk"""				,		)
    _snake_case    :  List[str]					=					mel_filter_bank(
        num_frequency_bins=self.nb_frequency_bins				,		num_mel_filters=lowerCAmelCase				,		min_frequency=lowerCAmelCase				,		max_frequency=lowerCAmelCase				,		sampling_rate=lowerCAmelCase				,		norm="""slaney"""				,		mel_scale="""slaney"""				,		)

   def      UpperCamelCase_  (					self							:			List[Any])				->				Dict[str, Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					copy.deepcopy(self.__dict__)
    _snake_case    :  Union[str, Any]					=					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      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			np.array				,		lowerCAmelCase							:			Optional[np.array] = None)				->				np.ndarray:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					spectrogram(
        lowerCAmelCase				,		window_function(self.fft_window_size				,		"""hann""")				,		frame_length=self.fft_window_size				,		hop_length=self.hop_length				,		power=2.0				,		mel_filters=lowerCAmelCase				,		log_mel="""dB"""				,		)
    return log_mel_spectrogram.T

   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			int)				->				List[str]:

    """simple docstring"""







    _snake_case    :  List[Any]					=					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
     _snake_case    :  Any					=					[0]
    if len(ranges[2]) == 0:
     # if the audio is too short, we just use the first chunk
     _snake_case    :  Any					=					[0]
    # randomly choose index for each part
    _snake_case    :  Tuple					=					np.random.choice(ranges[0])
    _snake_case    :  List[Any]					=					np.random.choice(ranges[1])
    _snake_case    :  Dict					=					np.random.choice(ranges[2])

    _snake_case    :  Optional[int]					=					mel[idx_front : idx_front + chunk_frames, :]
    _snake_case    :  Any					=					mel[idx_middle : idx_middle + chunk_frames, :]
    _snake_case    :  Union[str, Any]					=					mel[idx_back : idx_back + chunk_frames, :]

    _snake_case    :  List[str]					=					torch.tensor(mel[None, None, :])
    _snake_case    :  Optional[Any]					=					torch.nn.functional.interpolate(
        lowerCAmelCase				,		size=[chunk_frames, 64]				,		mode="""bilinear"""				,		align_corners=lowerCAmelCase)
    _snake_case    :  str					=					mel_shrink[0][0].numpy()
    _snake_case    :  int					=					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,		axis=0)
    return mel_fusion

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			np.array				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[int])				->				np.array:

    """simple docstring"""







    if waveform.shape[0] > max_length:
     if truncation == "rand_trunc":
      _snake_case    :  List[Any]					=					True
      # random crop to max_length (for compatibility) -> this should be handled by self.pad
      _snake_case    :  Optional[Any]					=					len(lowerCAmelCase) - max_length
      _snake_case    :  Any					=					np.random.randint(0				,		overflow + 1)
      _snake_case    :  str					=					waveform[idx : idx + max_length]
      _snake_case    :  Any					=					self._np_extract_fbank_features(lowerCAmelCase				,		self.mel_filters_slaney)[None, :]
     elif truncation == "fusion":
      _snake_case    :  List[str]					=					self._np_extract_fbank_features(lowerCAmelCase				,		self.mel_filters)
      _snake_case    :  List[Any]					=					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
      _snake_case    :  Tuple					=					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.
       _snake_case    :  int					=					np.stack([mel, mel, mel, mel]				,		axis=0)
       _snake_case    :  List[str]					=					False
      else:
       _snake_case    :  Dict					=					self._random_mel_fusion(lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase)
       _snake_case    :  List[Any]					=					True
     else:
      raise NotImplementedError(F'''data_truncating {truncation} not implemented''')

    else:
     _snake_case    :  Tuple					=					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":
       _snake_case    :  Dict					=					int(max_length / len(lowerCAmelCase))
       _snake_case    :  List[str]					=					np.stack(np.tile(lowerCAmelCase				,		n_repeat + 1))[:max_length]
      if padding == "repeatpad":
       _snake_case    :  Tuple					=					int(max_length / len(lowerCAmelCase))
       _snake_case    :  List[Any]					=					np.stack(np.tile(lowerCAmelCase				,		lowerCAmelCase))
      _snake_case    :  Any					=					np.pad(lowerCAmelCase				,		(0, max_length - waveform.shape[0])				,		mode="""constant"""				,		constant_values=0)

     if truncation == "fusion":
      _snake_case    :  List[Any]					=					self._np_extract_fbank_features(lowerCAmelCase				,		self.mel_filters)
      _snake_case    :  int					=					np.stack([input_mel, input_mel, input_mel, input_mel]				,		axis=0)
     else:
      _snake_case    :  int					=					self._np_extract_fbank_features(lowerCAmelCase				,		self.mel_filters_slaney)[None, :]

    return input_mel, longer


   def __call__(					self							:			Any				,		lowerCAmelCase							:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,		lowerCAmelCase							:			str = None				,		lowerCAmelCase							:			Optional[str] = None				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			Optional[int] = None				,		lowerCAmelCase							:			Optional[Union[str, TensorType]] = None				,		**lowerCAmelCase							:			List[Any]				,		)				->				BatchFeature:

    """simple docstring"""







    _snake_case    :  Dict					=					truncation if truncation is not None else self.truncation
    _snake_case    :  int					=					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.""")

    _snake_case    :  int					=					isinstance(lowerCAmelCase				,		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}''')
    _snake_case    :  int					=					is_batched_numpy or (
        isinstance(lowerCAmelCase				,		(list, tuple)) and (isinstance(raw_speech[0]				,		(np.ndarray, tuple, list)))
    )

    if is_batched:
     _snake_case    :  Union[str, Any]					=					[np.asarray(lowerCAmelCase				,		dtype=np.floataa) for speech in raw_speech]
    elif not is_batched and not isinstance(lowerCAmelCase				,		np.ndarray):
     _snake_case    :  Optional[int]					=					np.asarray(lowerCAmelCase				,		dtype=np.floataa)
    elif isinstance(lowerCAmelCase				,		np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
     _snake_case    :  Any					=					raw_speech.astype(np.floataa)

    # always return batch
    if not is_batched:
     _snake_case    :  Optional[Any]					=					[np.asarray(lowerCAmelCase)]

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

    _snake_case    :  List[Any]					=					[]
    _snake_case    :  int					=					[]
    for mel, longer in padded_inputs:
     input_mel.append(lowerCAmelCase)
     is_longer.append(lowerCAmelCase)

    if truncation == "fusion" and sum(lowerCAmelCase) == 0:
     # if no audio is longer than 10s, then randomly select one audio to be longer
     _snake_case    :  Any					=					np.random.randint(0				,		len(lowerCAmelCase))
     _snake_case    :  Optional[int]					=					True

    if isinstance(input_mel[0]				,		lowerCAmelCase):
     _snake_case    :  List[str]					=					[np.asarray(lowerCAmelCase				,		dtype=np.floataa) for feature in input_mel]

    # is_longer is a list of bool
    _snake_case    :  Dict					=					[[longer] for longer in is_longer]

    _snake_case    :  Optional[Any]					=					{"""input_features""": input_mel, """is_longer""": is_longer}
    _snake_case    :  Optional[int]					=					BatchFeature(lowerCAmelCase)

    if return_tensors is not None:
     _snake_case    :  int					=					input_features.convert_to_tensors(lowerCAmelCase)

    return input_features





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a__    =	"""src/diffusers"""

# Matches is_xxx_available()
a__    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a__    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a__    =	"""
{0} = None
"""

a__    =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a__    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[Any]:
 _snake_case    :  str					=					_re_backend.findall(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Optional[Any]:
 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """__init__.py"""				)				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
  _snake_case    :  Dict					=					f.readlines()

 # Get to the point we do the actual imports for type checking
 _snake_case    :  Optional[int]					=					0
 _snake_case    :  Union[str, Any]					=					{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__				):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  _snake_case    :  int					=					find_backend(lines[line_index]				)
  if backend is not None:
   while not lines[line_index].startswith("""else:"""				):
    line_index += 1
   line_index += 1
   _snake_case    :  str					=					[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
    _snake_case    :  int					=					lines[line_index]
    _snake_case    :  str					=					_re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
    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

   if len(SCREAMING_SNAKE_CASE__				) > 0:
    _snake_case    :  Optional[int]					=					objects
  else:
   line_index += 1

 return backend_specific_objects



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Dict:
 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__				)
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict=None				)  ->     List[Any]:
 if backend_specific_objects is None:
  _snake_case    :  Dict					=					read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 _snake_case    :  Union[str, Any]					=					{}

 for backend, objects in backend_specific_objects.items():
  _snake_case    :  List[Any]					=					"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_"""				)				) + """]"""
  _snake_case    :  Any					=					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for o in objects]				)
  _snake_case    :  Any					=					dummy_file

 return dummy_files



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Optional[int]:
 _snake_case    :  str					=					create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _snake_case    :  List[Any]					=					{"""torch""": """pt"""}

 # Locate actual dummy modules and read their content.
 _snake_case    :  Dict					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """utils"""				)
 _snake_case    :  int					=					{
     backend: os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py'''				)
     for backend in dummy_files.keys()
 }

 _snake_case    :  int					=					{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE__				):
   with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
    _snake_case    :  Union[str, Any]					=					f.read()
  else:
   _snake_case    :  str					=					""""""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py as the main '''
        """__init__ has new objects."""				)
    with open(dummy_file_paths[backend]				,       """w"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
     f.write(dummy_files[backend]				)
   else:
    raise ValueError(
        """The main __init__ has objects that are not present in """
        F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '''
        """to fix this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  a__    =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)
