
import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCAmelCase	       =	'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase	       =	direct_transformers_import(PATH_TO_TRANSFORMERS)

__UpperCAmelCase	       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

__UpperCAmelCase	       =	{
    # used to compute the property `self.chunk_length`
    '''EncodecConfig''': ['''overlap'''],
    # used as `self.bert_model = BertModel(config, ...)`
    '''DPRConfig''': True,
    # not used in modeling files, but it's an important information
    '''FSMTConfig''': ['''langs'''],
    # used internally in the configuration class file
    '''GPTNeoConfig''': ['''attention_types'''],
    # used internally in the configuration class file
    '''EsmConfig''': ['''is_folding_model'''],
    # used during training (despite we don't have training script for these models yet)
    '''Mask2FormerConfig''': ['''ignore_value'''],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    '''OneFormerConfig''': ['''ignore_value''', '''norm'''],
    # used during preprocessing and collation, see `collating_graphormer.py`
    '''GraphormerConfig''': ['''spatial_pos_max'''],
    # used internally in the configuration class file
    '''T5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    '''MT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    '''UMT5Config''': ['''feed_forward_proj''', '''tokenizer_class'''],
    # used internally in the configuration class file
    '''LongT5Config''': ['''feed_forward_proj'''],
    # used internally in the configuration class file
    '''SwitchTransformersConfig''': ['''feed_forward_proj'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''BioGptConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''GLPNConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''SegformerConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''CvtConfig''': ['''layer_norm_eps'''],
    # having default values other than `1e-5` - we can't fix them without breaking
    '''PerceiverConfig''': ['''layer_norm_eps'''],
    # used internally to calculate the feature size
    '''InformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''TimeSeriesTransformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate the feature size
    '''AutoformerConfig''': ['''num_static_real_features''', '''num_time_features'''],
    # used internally to calculate `mlp_dim`
    '''SamVisionConfig''': ['''mlp_ratio'''],
    # For (head) training, but so far not implemented
    '''ClapAudioConfig''': ['''num_classes'''],
    # Not used, but providing useful information to users
    '''SpeechT5HifiGanConfig''': ['''sampling_rate'''],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        '''CLIPSegConfig''': True,
        '''DeformableDetrConfig''': True,
        '''DetaConfig''': True,
        '''DinatConfig''': True,
        '''DonutSwinConfig''': True,
        '''EfficientFormerConfig''': True,
        '''FSMTConfig''': True,
        '''JukeboxConfig''': True,
        '''LayoutLMv2Config''': True,
        '''MaskFormerSwinConfig''': True,
        '''MT5Config''': True,
        '''NatConfig''': True,
        '''OneFormerConfig''': True,
        '''PerceiverConfig''': True,
        '''RagConfig''': True,
        '''SpeechT5Config''': True,
        '''SwinConfig''': True,
        '''Swin2SRConfig''': True,
        '''Swinv2Config''': True,
        '''SwitchTransformersConfig''': True,
        '''TableTransformerConfig''': True,
        '''TapasConfig''': True,
        '''TransfoXLConfig''': True,
        '''UniSpeechConfig''': True,
        '''UniSpeechSatConfig''': True,
        '''WavLMConfig''': True,
        '''WhisperConfig''': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        '''JukeboxPriorConfig''': True,
        # TODO: @Younes (for `is_decoder`)
        '''Pix2StructTextConfig''': True,
    }
)
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Optional[Any]     )     ->					Tuple:
   UpperCamelCase   :  str						     =     False
   for attribute in attributes:
      for modeling_source in source_strings:
         # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
         if (
             F"""config.{attribute}""" in modeling_source
             or F"""getattr(config, \"{attribute}\"""" in modeling_source
             or F"""getattr(self.config, \"{attribute}\"""" in modeling_source
         ):
            UpperCamelCase   :  Dict						     =     True
         # Deal with multi-line cases
         elif (
             re.search(
                 RF"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""""	,					snake_case__	,					)
             is not None
         ):
            UpperCamelCase   :  Dict						     =     True
         # `SequenceSummary` is called with `SequenceSummary(config)`
         elif attribute in [
             "summary_type",
             "summary_use_proj",
             "summary_activation",
             "summary_last_dropout",
             "summary_proj_to_labels",
             "summary_first_dropout",
         ]:
            if "SequenceSummary" in modeling_source:
               UpperCamelCase   :  Tuple						     =     True
         if attribute_used:
            break
      if attribute_used:
         break

    # common and important attributes, even if they do not always appear in the modeling files
   UpperCamelCase   :  Tuple						     =     [
       'bos_index',
       'eos_index',
       'pad_index',
       'unk_index',
       'mask_index',
       'image_size',
       'use_cache',
       'out_features',
       'out_indices',
   ]
   UpperCamelCase   :  Union[str, Any]						     =     ['encoder_no_repeat_ngram_size']

   # Special cases to be allowed
   UpperCamelCase   :  int						     =     True
   if not attribute_used:
      UpperCamelCase   :  Dict						     =     False
      for attribute in attributes:
         # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
         if attribute in ["is_encoder_decoder"] and default_value is True:
            UpperCamelCase   :  Dict						     =     True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            UpperCamelCase   :  List[str]						     =     True

         # Allow cases without checking the default value in the configuration class
         elif attribute in attributes_to_allow + attributes_used_in_generation:
            UpperCamelCase   :  Dict						     =     True
         elif attribute.endswith('_token_id'     ):
            UpperCamelCase   :  int						     =     True

         # configuration class specific cases
         if not case_allowed:
            UpperCamelCase   :  Tuple						     =     SPECIAL_CASES_TO_ALLOW.get(config_class.__name__	,					[]     )
            UpperCamelCase   :  Tuple						     =     allowed_cases is True or attribute in allowed_cases

   return attribute_used or case_allowed
def 		UpperCamelCase     (       snake_case__	:   Any     )     ->					Any:
   UpperCamelCase   :  List[Any]						     =     dict(inspect.signature(config_class.__init__     ).parameters     )
   UpperCamelCase   :  str						     =     [x for x in list(signature.keys()     ) if x not in ['self', 'kwargs']]
   UpperCamelCase   :  int						     =     [signature[param].default for param in parameter_names]

   # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
   # as one variant is used, the test should pass
   UpperCamelCase   :  Tuple						     =     {}
   if len(config_class.attribute_map     ) > 0:
      UpperCamelCase   :  List[str]						     =     {v: k for k, v in config_class.attribute_map.items()}

   # Get the path to modeling source files
   UpperCamelCase   :  Optional[int]						     =     inspect.getsourcefile(snake_case__     )
   UpperCamelCase   :  List[str]						     =     os.path.dirname(snake_case__     )
   # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
   UpperCamelCase   :  List[str]						     =     [os.path.join(snake_case__	,					snake_case__     ) for fn in os.listdir(snake_case__     ) if fn.startswith('modeling_'     )]

   # Get the source code strings
   UpperCamelCase   :  Optional[Any]						     =     []
   for path in modeling_paths:
      if os.path.isfile(snake_case__     ):
         with open(snake_case__     ) as fp:
            modeling_sources.append(fp.read()     )

   UpperCamelCase   :  List[Any]						     =     []
   for config_param, default_value in zip(snake_case__	,					snake_case__     ):
      # `attributes` here is all the variant names for `config_param`
      UpperCamelCase   :  Tuple						     =     [config_param]
      # some configuration classes have non-empty `attribute_map`, and both names could be used in the
      # corresponding modeling files. As long as one of them appears, it is fine.
      if config_param in reversed_attribute_map:
         attributes.append(reversed_attribute_map[config_param]     )

      if not check_attribute_being_used(snake_case__	,					snake_case__	,					snake_case__	,					snake_case__     ):
         unused_attributes.append(attributes[0]     )

   return sorted(snake_case__     )
def 		UpperCamelCase     (       )     ->					str:
   UpperCamelCase   :  List[str]						     =     {}
   for _config_class in list(CONFIG_MAPPING.values()     ):
      # Skip deprecated models
      if "models.deprecated" in _config_class.__module__:
         continue
      # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
      UpperCamelCase   :  List[str]						     =     [
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class     )	,					lambda snake_case__     : inspect.isclass(snake_case__     )
              and issubclass(snake_case__	,					snake_case__     )
              and inspect.getmodule(snake_case__     ) == inspect.getmodule(_config_class     )	,					)
      ]
      for config_class in config_classes_in_module:
         UpperCamelCase   :  List[Any]						     =     check_config_attributes_being_used(snake_case__     )
         if len(snake_case__     ) > 0:
            UpperCamelCase   :  List[Any]						     =     unused_attributes

   if len(snake_case__     ) > 0:
      UpperCamelCase   :  Optional[Any]						     =     'The following configuration classes contain unused attributes in the corresponding modeling files:\n'
      for name, attributes in configs_with_unused_attributes.items():
         error += F"""{name}: {attributes}\n"""

      raise ValueError(snake_case__     )


if __name__ == "__main__":
    check_config_attributes()








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

import numpy as np

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


__UpperCAmelCase	       =	logging.get_logger(__name__)



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  ["input_values", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_ = 1,      SCREAMING_SNAKE_CASE_ = 1_6000,      SCREAMING_SNAKE_CASE_ = 0.0,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 16,      SCREAMING_SNAKE_CASE_ = 64,      SCREAMING_SNAKE_CASE_ = "hann_window",      SCREAMING_SNAKE_CASE_ = 1.0,      SCREAMING_SNAKE_CASE_ = 80,      SCREAMING_SNAKE_CASE_ = 7600,      SCREAMING_SNAKE_CASE_ = 1e-10,      SCREAMING_SNAKE_CASE_ = 2,      SCREAMING_SNAKE_CASE_ = True,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_,      sampling_rate=SCREAMING_SNAKE_CASE_,      padding_value=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     return_attention_mask

          UpperCamelCase   :  Union[str, Any]						     =     num_mel_bins
          UpperCamelCase   :  int						     =     hop_length
          UpperCamelCase   :  Any						     =     win_length
          UpperCamelCase   :  Dict						     =     win_function
          UpperCamelCase   :  Any						     =     frame_signal_scale
          UpperCamelCase   :  str						     =     fmin
          UpperCamelCase   :  int						     =     fmax
          UpperCamelCase   :  Dict						     =     mel_floor
          UpperCamelCase   :  Any						     =     reduction_factor

          UpperCamelCase   :  List[str]						     =     win_length * sampling_rate // 1000
          UpperCamelCase   :  Union[str, Any]						     =     hop_length * sampling_rate // 1000
          UpperCamelCase   :  Tuple						     =     optimal_fft_length(self.sample_size	)
          UpperCamelCase   :  int						     =     (self.n_fft // 2) + 1

          UpperCamelCase   :  Any						     =     window_function(window_length=self.sample_size,      name=self.win_function,      periodic=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     mel_filter_bank(
              num_frequency_bins=self.n_freqs,      num_mel_filters=self.num_mel_bins,      min_frequency=self.fmin,      max_frequency=self.fmax,      sampling_rate=self.sampling_rate,      norm='slaney',      mel_scale='slaney',      )

          if frame_signal_scale != 1.0:
             warnings.warn(
                 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
          if reduction_factor != 2.0:
             warnings.warn(
                 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers',      SCREAMING_SNAKE_CASE_,      )
       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  snake_case_	(      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = 0.0	)					->		List[np.ndarray]:
          if attention_mask is not None:
             UpperCamelCase   :  Dict						     =     np.array(SCREAMING_SNAKE_CASE_,      np.intaa	)
             UpperCamelCase   :  int						     =     []

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

                normed_input_values.append(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  str						     =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7	) for x in input_values]

          return normed_input_values
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      )					->		np.ndarray:
          UpperCamelCase   :  int						     =     spectrogram(
              SCREAMING_SNAKE_CASE_,      window=self.window,      frame_length=self.sample_size,      hop_length=self.sample_stride,      fft_length=self.n_fft,      mel_filters=self.mel_filters,      mel_floor=self.mel_floor,      log_mel='log10',      )
          return log_mel_spec.T
       def __call__(      self,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          if audio is None and audio_target is None:
             raise ValueError('You must provide either `audio` or `audio_target` values.'	)

          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 audio is not None:
             UpperCamelCase   :  Dict						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          else:
             UpperCamelCase   :  str						     =     None

          if audio_target is not None:
             UpperCamelCase   :  str						     =     self._process_audio(
                 SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

             if inputs is None:
                return inputs_target
             else:
                UpperCamelCase   :  Dict						     =     inputs_target['input_values']
                UpperCamelCase   :  str						     =     inputs_target.get('attention_mask'	)
                if decoder_attention_mask is not None:
                   UpperCamelCase   :  str						     =     decoder_attention_mask

          return inputs
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      **SCREAMING_SNAKE_CASE_,      )					->		BatchFeature:
          UpperCamelCase   :  Any						     =     isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and len(speech.shape	) > 1
          if is_batched_numpy and len(speech.shape	) > 2:
             raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""	)
          UpperCamelCase   :  Any						     =     is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_,      (list, tuple)	) and (isinstance(speech[0],      (np.ndarray, tuple, list)	))
          )

          if is_batched:
             UpperCamelCase   :  str						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for speech in speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	):
             UpperCamelCase   :  int						     =     np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	)
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and speech.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Any						     =     speech.astype(np.floataa	)

          # always return batch
          if not is_batched:
             UpperCamelCase   :  str						     =     [speech]

          # needed to make pad() work on spectrogram inputs
          UpperCamelCase   :  Optional[Any]						     =     self.feature_size

          # convert into correct format for padding
          if is_target:
             UpperCamelCase   :  Optional[int]						     =     [self._extract_mel_features(SCREAMING_SNAKE_CASE_	) for waveform in speech]
             UpperCamelCase   :  Union[str, Any]						     =     BatchFeature({'input_values': features}	)
             UpperCamelCase   :  List[str]						     =     self.num_mel_bins
          else:
             UpperCamelCase   :  Dict						     =     BatchFeature({'input_values': speech}	)

          UpperCamelCase   :  Tuple						     =     self.pad(
              SCREAMING_SNAKE_CASE_,      padding=SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_,      truncation=SCREAMING_SNAKE_CASE_,      pad_to_multiple_of=SCREAMING_SNAKE_CASE_,      return_attention_mask=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  int						     =     feature_size_hack

          # convert input values to correct format
          UpperCamelCase   :  Optional[int]						     =     padded_inputs['input_values']
          if not isinstance(input_values[0],      np.ndarray	):
             UpperCamelCase   :  Union[str, Any]						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.floataa	) for array in input_values]
          elif (
              not isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)
              and isinstance(input_values[0],      np.ndarray	)
              and input_values[0].dtype is np.dtype(np.floataa	)
          ):
             UpperCamelCase   :  Optional[int]						     =     [array.astype(np.floataa	) for array in input_values]
          elif isinstance(SCREAMING_SNAKE_CASE_,      np.ndarray	) and input_values.dtype is np.dtype(np.floataa	):
             UpperCamelCase   :  Dict						     =     input_values.astype(np.floataa	)

          # convert attention_mask to correct format
          UpperCamelCase   :  Dict						     =     padded_inputs.get('attention_mask'	)
          if attention_mask is not None:
             UpperCamelCase   :  int						     =     [np.asarray(SCREAMING_SNAKE_CASE_,      dtype=np.intaa	) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if not is_target and self.do_normalize:
             UpperCamelCase   :  Dict						     =     (
                 attention_mask
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE_,      max_length=SCREAMING_SNAKE_CASE_	) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase   :  Tuple						     =     self.zero_mean_unit_var_norm(
                 padded_inputs['input_values'],      attention_mask=SCREAMING_SNAKE_CASE_,      padding_value=self.padding_value	)

          if return_tensors is not None:
             UpperCamelCase   :  int						     =     padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_	)

          return padded_inputs






       def  snake_case_	(      self	)					->		Dict[str, Any]:
          UpperCamelCase   :  Any						     =     super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          UpperCamelCase   :  Any						     =     ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
          for name in names:
             if name in output:
                del output[name]

          return output



