




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
_lowerCamelCase     :		str								=       """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase     :		List[Any]								=       direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCamelCase     :		int								=       transformers.models.auto.configuration_auto.CONFIG_MAPPING

_lowerCamelCase     :		List[str]								=       {
    # 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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   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
                     ):
                            A__							=   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
                     ):
                            A__							=   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:
                                   A__							=   True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

       # Special cases to be allowed
       A__							=   True
       if not attribute_used:
              A__							=   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:
                            A__							=   True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            A__							=   True

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

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

       return attribute_used or case_allowed







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Any:



       """simple docstring"""

       A__							=   dict(inspect.signature(config_class.__init__						).parameters						)
       A__							=   [x for x in list(signature.keys()						) if x not in ['self', 'kwargs']]
       A__							=   [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
       A__							=   {}
       if len(config_class.attribute_map						) > 0:
              A__							=   {v: k for k, v in config_class.attribute_map.items()}

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

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

       A__							=   []
       for config_param, default_value in zip(snake_case__			,    snake_case__						):
              # `attributes` here is all the variant names for `config_param`
              A__							=   [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 SCREAMING_SNAKE_CASE    (	)       ->	str:



       """simple docstring"""

       A__							=   {}
       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.)
              A__							=   [
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class						)			,    lambda lowercase_						: 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:
                     A__							=   check_config_attributes_being_used(snake_case__						)
                     if len(snake_case__						) > 0:
                            A__							=   unused_attributes

       if len(snake_case__						) > 0:
              A__							=   '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()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_lowerCamelCase     :		Tuple								=       {
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		int								=       ["""Speech2TextTokenizer"""]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[int]								=       ["""Speech2TextFeatureExtractor"""]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Optional[Any]								=       [
      """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFSpeech2TextForConditionalGeneration""",
      """TFSpeech2TextModel""",
      """TFSpeech2TextPreTrainedModel""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		List[Any]								=       [
      """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2TextForConditionalGeneration""",
      """Speech2TextModel""",
      """Speech2TextPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
  from .processing_speech_to_text import SpeechaTextProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_speech_to_text import SpeechaTextTokenizer

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_speech_to_text import (
        TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFSpeechaTextForConditionalGeneration,
        TFSpeechaTextModel,
        TFSpeechaTextPreTrainedModel,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text import (
        SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextForConditionalGeneration,
        SpeechaTextModel,
        SpeechaTextPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Union[str, Any]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


