





"""simple docstring"""


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__				  =   "src/transformers"


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

lowerCamelCase__				  =   transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCamelCase__				  =   {
    # 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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       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      :	Any						=       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      :	Optional[int]						=       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      :	List[Any]						=       [
          "bos_index",
          "eos_index",
          "pad_index",
          "unk_index",
          "mask_index",
          "image_size",
          "use_cache",
          "out_features",
          "out_indices",
      ]
      _UpperCamelCase      :	int						=       ["encoder_no_repeat_ngram_size"]

      # Special cases to be allowed
      _UpperCamelCase      :	Tuple						=       True
      if not attribute_used:
            _UpperCamelCase      :	List[Any]						=       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      :	Optional[Any]						=       True
                  elif attribute in ["tie_word_embeddings"] and default_value is False:
                        _UpperCamelCase      :	Optional[Any]						=       True

                  # Allow cases without checking the default value in the configuration class
                  elif attribute in attributes_to_allow + attributes_used_in_generation:
                        _UpperCamelCase      :	List[Any]						=       True
                  elif attribute.endswith("_token_id"			):
                        _UpperCamelCase      :	Dict						=       True

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

      return attribute_used or case_allowed




def 					lowercase__    (					lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       dict(inspect.signature(config_class.__init__			).parameters			)
      _UpperCamelCase      :	Optional[int]						=       [x for x in list(signature.keys()			) if x not in ["self", "kwargs"]]
      _UpperCamelCase      :	Tuple						=       [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      :	Union[str, Any]						=       {}
      if len(config_class.attribute_map			) > 0:
            _UpperCamelCase      :	Dict						=       {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      :	Union[str, Any]						=       os.path.dirname(snake_case__			)
      # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
      _UpperCamelCase      :	Optional[int]						=       [os.path.join(snake_case__		,snake_case__			) for fn in os.listdir(snake_case__			) if fn.startswith("modeling_"			)]

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

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




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       {}
      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      :	Dict						=       [
                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:
                  _UpperCamelCase      :	int						=       check_config_attributes_being_used(snake_case__			)
                  if len(snake_case__			) > 0:
                        _UpperCamelCase      :	str						=       unused_attributes

      if len(snake_case__			) > 0:
            _UpperCamelCase      :	str						=       "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()







"""simple docstring"""


class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Optional[Any]					,   __a   :       list[int]	)		->				None:
        _UpperCamelCase      :	Tuple						=       len(__a	)
        _UpperCamelCase      :	Dict						=       [0] * len_array

        if len_array > 0:
              _UpperCamelCase      :	Optional[Any]						=       array[0]

        for i in range(1					,   __a	):
              _UpperCamelCase      :	Tuple						=       self.prefix_sum[i - 1] + array[i]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       int					,   __a   :       int	)		->				int:
        if start == 0:
              return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int	)		->				bool:
        _UpperCamelCase      :	int						=       {0}
        for sum_item in self.prefix_sum:
              if sum_item - target_sum in sums:
                    return True

              sums.add(__a	)

        return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
