"""simple docstring"""







import collections
import importlib.util
import os
import re
from pathlib import Path


__SCREAMING_SNAKE_CASE								="src/transformers"


# Matches is_xxx_available()
__SCREAMING_SNAKE_CASE								=re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__SCREAMING_SNAKE_CASE								=re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__SCREAMING_SNAKE_CASE								=re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
__SCREAMING_SNAKE_CASE								=re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
__SCREAMING_SNAKE_CASE								=re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__SCREAMING_SNAKE_CASE								=re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
__SCREAMING_SNAKE_CASE								=re.compile("^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
__SCREAMING_SNAKE_CASE								=re.compile("^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
__SCREAMING_SNAKE_CASE								=re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
__SCREAMING_SNAKE_CASE								=re.compile(r"^\s*try:")
# Catches a line with else:
__SCREAMING_SNAKE_CASE								=re.compile(r"^\s*else:")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple   ):
     if _re_test_backend.search(__SCREAMING_SNAKE_CASE   ) is None:
          return None
     lowercase_			:       Dict									=			[b[0] for b in _re_backend.findall(__SCREAMING_SNAKE_CASE   )]
     backends.sort()
     return "_and_".join(__SCREAMING_SNAKE_CASE   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any   ):
     with open(__SCREAMING_SNAKE_CASE  ,    'r'  ,    encoding='utf-8'  ,    newline='\n'   ) as f:
          lowercase_			:       Optional[Any]									=			f.readlines()

     lowercase_			:       List[str]									=			0
     while line_index < len(__SCREAMING_SNAKE_CASE   ) and not lines[line_index].startswith('_import_structure = {'   ):
          line_index += 1

     # If this is a traditional init, just return.
     if line_index >= len(__SCREAMING_SNAKE_CASE   ):
          return None

     # First grab the objects without a specific backend in _import_structure
     lowercase_			:       Dict									=			[]
     while not lines[line_index].startswith('if TYPE_CHECKING'   ) and find_backend(lines[line_index]   ) is None:
          lowercase_			:       Union[str, Any]									=			lines[line_index]
          # If we have everything on a single line, let's deal with it.
          if _re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE   ):
               lowercase_			:       Tuple									=			_re_one_line_import_struct.search(__SCREAMING_SNAKE_CASE   ).groups()[0]
               lowercase_			:       Any									=			re.findall('\[([^\]]+)\]'  ,    __SCREAMING_SNAKE_CASE   )
               for imp in imports:
                    objects.extend([obj[1:-1] for obj in imp.split(', '   )]   )
               line_index += 1
               continue
          lowercase_			:       Union[str, Any]									=			_re_import_struct_key_value.search(__SCREAMING_SNAKE_CASE   )
          if single_line_import_search is not None:
               lowercase_			:       Dict									=			[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '   ) if len(__SCREAMING_SNAKE_CASE   ) > 0]
               objects.extend(__SCREAMING_SNAKE_CASE   )
          elif line.startswith(' ' * 8 + '"'   ):
               objects.append(line[9:-3]   )
          line_index += 1

     lowercase_			:       int									=			{'none': objects}
     # Let's continue with backend-specific objects in _import_structure
     while not lines[line_index].startswith('if TYPE_CHECKING'   ):
          # If the line is an if not is_backend_available, we grab all objects associated.
          lowercase_			:       List[Any]									=			find_backend(lines[line_index]   )
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]   ) is None:
               lowercase_			:       Tuple									=			None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]   ) is None:
                    line_index += 1

               line_index += 1

               lowercase_			:       Optional[Any]									=			[]
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(' ' * 4   ):
                    lowercase_			:       str									=			lines[line_index]
                    if _re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE   ) is not None:
                         objects.append(_re_import_struct_add_one.search(__SCREAMING_SNAKE_CASE   ).groups()[0]   )
                    elif _re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE   ) is not None:
                         lowercase_			:       int									=			_re_import_struct_add_many.search(__SCREAMING_SNAKE_CASE   ).groups()[0].split(', '   )
                         lowercase_			:       Optional[Any]									=			[obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE   ) > 0]
                         objects.extend(__SCREAMING_SNAKE_CASE   )
                    elif _re_between_brackets.search(__SCREAMING_SNAKE_CASE   ) is not None:
                         lowercase_			:       Any									=			_re_between_brackets.search(__SCREAMING_SNAKE_CASE   ).groups()[0].split(', '   )
                         lowercase_			:       Tuple									=			[obj[1:-1] for obj in imports if len(__SCREAMING_SNAKE_CASE   ) > 0]
                         objects.extend(__SCREAMING_SNAKE_CASE   )
                    elif _re_quote_object.search(__SCREAMING_SNAKE_CASE   ) is not None:
                         objects.append(_re_quote_object.search(__SCREAMING_SNAKE_CASE   ).groups()[0]   )
                    elif line.startswith(' ' * 8 + '"'   ):
                         objects.append(line[9:-3]   )
                    elif line.startswith(' ' * 12 + '"'   ):
                         objects.append(line[13:-3]   )
                    line_index += 1

               lowercase_			:       Optional[int]									=			objects
          else:
               line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
     lowercase_			:       Any									=			[]
     while (
         line_index < len(__SCREAMING_SNAKE_CASE   )
         and find_backend(lines[line_index]   ) is None
         and not lines[line_index].startswith('else'   )
     ):
          lowercase_			:       str									=			lines[line_index]
          lowercase_			:       Dict									=			_re_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

     lowercase_			:       List[Any]									=			{'none': objects}
     # Let's continue with backend-specific objects
     while line_index < len(__SCREAMING_SNAKE_CASE   ):
          # If the line is an if is_backend_available, we grab all objects associated.
          lowercase_			:       Any									=			find_backend(lines[line_index]   )
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]   ) is None:
               lowercase_			:       List[str]									=			None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]   ) is None:
                    line_index += 1

               line_index += 1

               lowercase_			:       int									=			[]
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(' ' * 8   ):
                    lowercase_			:       Union[str, Any]									=			lines[line_index]
                    lowercase_			:       List[str]									=			_re_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(' ' * 12   ):
                         objects.append(line[12:-2]   )
                    line_index += 1

               lowercase_			:       Optional[Any]									=			objects
          else:
               line_index += 1

     return import_dict_objects, type_hint_objects



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  List[str]   ):

     def find_duplicates(__SCREAMING_SNAKE_CASE							:  Optional[int]   ):
          return [k for k, v in collections.Counter(__SCREAMING_SNAKE_CASE   ).items() if v > 1]

     if list(import_dict_objects.keys()   ) != list(type_hint_objects.keys()   ):
          return ["Both sides of the init do not have the same backends!"]

     lowercase_			:       Any									=			[]
     for key in import_dict_objects.keys():
          lowercase_			:       List[Any]									=			find_duplicates(import_dict_objects[key]   )
          if duplicate_imports:
               errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}'''   )
          lowercase_			:       Dict									=			find_duplicates(type_hint_objects[key]   )
          if duplicate_type_hints:
               errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''   )

          if sorted(set(import_dict_objects[key]   )   ) != sorted(set(type_hint_objects[key]   )   ):
               lowercase_			:       List[str]									=			'base imports' if key == 'none' else F'''{key} backend'''
               errors.append(F'''Differences for {name}:'''   )
               for a in type_hint_objects[key]:
                    if a not in import_dict_objects[key]:
                         errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.'''   )
               for a in import_dict_objects[key]:
                    if a not in type_hint_objects[key]:
                         errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.'''   )
     return errors



def     lowercase__( ):
     lowercase_			:       Union[str, Any]									=			[]
     for root, _, files in os.walk(__SCREAMING_SNAKE_CASE   ):
          if "__init__.py" in files:
               lowercase_			:       Dict									=			os.path.join(__SCREAMING_SNAKE_CASE  ,    '__init__.py'   )
               lowercase_			:       str									=			parse_init(__SCREAMING_SNAKE_CASE   )
               if objects is not None:
                    lowercase_			:       Union[str, Any]									=			analyze_results(*__SCREAMING_SNAKE_CASE   )
                    if len(__SCREAMING_SNAKE_CASE   ) > 0:
                         lowercase_			:       Tuple									=			F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
                         failures.append('\n'.join(__SCREAMING_SNAKE_CASE   )   )
     if len(__SCREAMING_SNAKE_CASE   ) > 0:
          raise ValueError('\n\n'.join(__SCREAMING_SNAKE_CASE   )   )



def     lowercase__( ):
     lowercase_			:       Dict									=			[]
     for path, directories, files in os.walk(__SCREAMING_SNAKE_CASE   ):
          for folder in directories:
               # Ignore private modules
               if folder.startswith('_'   ):
                    directories.remove(__SCREAMING_SNAKE_CASE   )
                    continue
               # Ignore leftovers from branches (empty folders apart from pycache)
               if len(list((Path(__SCREAMING_SNAKE_CASE   ) / folder).glob('*.py'   )   )   ) == 0:
                    continue
               lowercase_			:       List[Any]									=			str((Path(__SCREAMING_SNAKE_CASE   ) / folder).relative_to(__SCREAMING_SNAKE_CASE   )   )
               lowercase_			:       Dict									=			short_path.replace(os.path.sep  ,    '.'   )
               submodules.append(__SCREAMING_SNAKE_CASE   )
          for fname in files:
               if fname == "__init__.py":
                    continue
               lowercase_			:       Union[str, Any]									=			str((Path(__SCREAMING_SNAKE_CASE   ) / fname).relative_to(__SCREAMING_SNAKE_CASE   )   )
               lowercase_			:       Union[str, Any]									=			short_path.replace('.py'  ,    ''   ).replace(os.path.sep  ,    '.'   )
               if len(submodule.split('.'   )   ) == 1:
                    submodules.append(__SCREAMING_SNAKE_CASE   )
     return submodules


__SCREAMING_SNAKE_CASE								=[
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
]



def     lowercase__( ):
     # This is to make sure the transformers module imported is the one in the repo.
     lowercase_			:       List[str]									=			importlib.util.spec_from_file_location(
         'transformers'  ,    os.path.join(__SCREAMING_SNAKE_CASE  ,    '__init__.py'   )  ,    submodule_search_locations=[PATH_TO_TRANSFORMERS]  ,    )
     lowercase_			:       Any									=			spec.loader.load_module()

     lowercase_			:       str									=			[
         module
         for module in get_transformers_submodules()
         if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
     ]
     if len(__SCREAMING_SNAKE_CASE   ) > 0:
          lowercase_			:       str									=			'\n'.join(F'''- {module}''' for module in module_not_registered   )
          raise ValueError(
              'The following submodules are not properly registered in the main init of Transformers:\n'
              F'''{list_of_modules}\n'''
              'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'   )


if __name__ == "__main__":
       check_all_inits()
       check_submodules()







"""simple docstring"""







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

import numpy as np

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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						['input_features']
   def __init__(    self  ,__UpperCamelCase=80  ,__UpperCamelCase=1_6000  ,__UpperCamelCase=160  ,__UpperCamelCase=30  ,__UpperCamelCase=400  ,__UpperCamelCase=0.0  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   Any:


        '''simple docstring'''


        super().__init__(
            feature_size=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,padding_value=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,**__UpperCamelCase  ,)
        lowercase_			:       Dict									=			n_fft
        lowercase_			:       Optional[Any]									=			hop_length
        lowercase_			:       Optional[int]									=			chunk_length
        lowercase_			:       Optional[Any]									=			chunk_length * sampling_rate
        lowercase_			:       Tuple									=			self.n_samples // hop_length
        lowercase_			:       int									=			sampling_rate
        lowercase_			:       int									=			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2  ,num_mel_filters=__UpperCamelCase  ,min_frequency=0.0  ,max_frequency=8000.0  ,sampling_rate=__UpperCamelCase  ,norm='slaney'  ,mel_scale='slaney'  ,)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   np.ndarray:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			spectrogram(
            __UpperCamelCase  ,window_function(self.n_fft  ,'hann'    )  ,frame_length=self.n_fft  ,hop_length=self.hop_length  ,power=2.0  ,mel_filters=self.mel_filters  ,log_mel='log10'  ,)
        lowercase_			:       Optional[Any]									=			log_spec[:, :-1]
        lowercase_			:       int									=			np.maximum(__UpperCamelCase  ,log_spec.max() - 8.0    )
        lowercase_			:       List[Any]									=			(log_spec + 4.0) / 4.0
        return log_spec
   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def     _UpperCAmelCase     (    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 0.0    )			->   List[np.ndarray]:


        '''simple docstring'''


        if attention_mask is not None:
             lowercase_			:       Optional[int]									=			np.array(__UpperCamelCase  ,np.intaa    )
             lowercase_			:       Optional[Any]									=			[]

             for vector, length in zip(__UpperCamelCase  ,attention_mask.sum(-1    )    ):
                  lowercase_			:       Union[str, Any]									=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
                  if length < normed_slice.shape[0]:
                       lowercase_			:       Any									=			padding_value

                  normed_input_values.append(__UpperCamelCase    )
        else:
             lowercase_			:       Optional[int]									=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

        return normed_input_values
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = "max_length"  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   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.__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.'    )

        lowercase_			:       Optional[int]									=			isinstance(__UpperCamelCase  ,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}'''    )
        lowercase_			:       Union[str, Any]									=			is_batched_numpy or (
            isinstance(__UpperCamelCase  ,(list, tuple)    ) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)    ))
        )

        if is_batched:
             lowercase_			:       Union[str, Any]									=			[np.asarray([speech]  ,dtype=np.floataa    ).T for speech in raw_speech]
        elif not is_batched and not isinstance(__UpperCamelCase  ,np.ndarray    ):
             lowercase_			:       Dict									=			np.asarray(__UpperCamelCase  ,dtype=np.floataa    )
        elif isinstance(__UpperCamelCase  ,np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
             lowercase_			:       Dict									=			raw_speech.astype(np.floataa    )

        # always return batch
        if not is_batched:
             lowercase_			:       List[Any]									=			[np.asarray([raw_speech]    ).T]

        lowercase_			:       str									=			BatchFeature({'input_features': raw_speech}    )

        # convert into correct format for padding

        lowercase_			:       str									=			self.pad(
            __UpperCamelCase  ,padding=__UpperCamelCase  ,max_length=max_length if max_length else self.n_samples  ,truncation=__UpperCamelCase  ,pad_to_multiple_of=__UpperCamelCase  ,return_attention_mask=return_attention_mask or do_normalize  ,)

        # zero-mean and unit-variance normalization
        if do_normalize:
             lowercase_			:       int									=			self.zero_mean_unit_var_norm(
                 padded_inputs['input_features']  ,attention_mask=padded_inputs['attention_mask']  ,padding_value=self.padding_value  ,)
             lowercase_			:       Tuple									=			np.stack(padded_inputs['input_features']  ,axis=0    )

        # make sure list is in array format
        lowercase_			:       Dict									=			padded_inputs.get('input_features'    ).transpose(2  ,0  ,1    )

        lowercase_			:       List[str]									=			[self._np_extract_fbank_features(__UpperCamelCase    ) for waveform in input_features[0]]

        if isinstance(input_features[0]  ,__UpperCamelCase    ):
             lowercase_			:       Optional[Any]									=			[np.asarray(__UpperCamelCase  ,dtype=np.floataa    ) for feature in input_features]
        else:
             lowercase_			:       str									=			input_features

        if return_attention_mask:
             # rescale from sample (48000) to feature (3000)
             lowercase_			:       str									=			padded_inputs['attention_mask'][:, :: self.hop_length]

        if return_tensors is not None:
             lowercase_			:       Union[str, Any]									=			padded_inputs.convert_to_tensors(__UpperCamelCase    )

        return padded_inputs







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


        '''simple docstring'''


        lowercase_			:       Dict									=			copy.deepcopy(self.__dict__    )
        lowercase_			:       Union[str, Any]									=			self.__class__.__name__
        if "mel_filters" in output:
             del output["mel_filters"]
        return output

