





'''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
UpperCAmelCase					:		Optional[Any]		=							'src/transformers'


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

UpperCAmelCase					:		List[str]		=							transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase					:		Optional[int]		=							{
    # 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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     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
                  ):
                        _snake_case		:	int					     =     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}"'''			,      lowerCAmelCase_			,      )
                      is not None
                  ):
                        _snake_case		:	str					     =     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:
                              _snake_case		:	List[str]					     =     True
                  if attribute_used:
                        break
            if attribute_used:
                  break

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

      # Special cases to be allowed
      _snake_case		:	List[Any]					     =     True
      if not attribute_used:
            _snake_case		:	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:
                        _snake_case		:	Dict					     =     True
                  elif attribute in ["tie_word_embeddings"] and default_value is False:
                        _snake_case		:	Union[str, Any]					     =     True

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

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

      return attribute_used or case_allowed




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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

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

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

      _snake_case		:	Dict					     =     []
      for config_param, default_value in zip(lowerCAmelCase_			,      lowerCAmelCase_	):
            # `attributes` here is all the variant names for `config_param`
            _snake_case		:	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(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):
                  unused_attributes.append(attributes[0]	)

      return sorted(lowerCAmelCase_	)




def    _a    (							):





      """simple docstring"""



      _snake_case		:	Optional[int]					     =     {}
      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.)
            _snake_case		:	Tuple					     =     [
                cls
                for name, cls in inspect.getmembers(
                    inspect.getmodule(_config_class	)			,      lambda lowerCAmelCase_	: inspect.isclass(lowerCAmelCase_	)
                    and issubclass(lowerCAmelCase_			,      lowerCAmelCase_	)
                    and inspect.getmodule(lowerCAmelCase_	) == inspect.getmodule(_config_class	)			,      )
            ]
            for config_class in config_classes_in_module:
                  _snake_case		:	Dict					     =     check_config_attributes_being_used(lowerCAmelCase_	)
                  if len(lowerCAmelCase_	) > 0:
                        _snake_case		:	str					     =     unused_attributes

      if len(lowerCAmelCase_	) > 0:
            _snake_case		:	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(lowerCAmelCase_	)


if __name__ == "__main__":
   check_config_attributes()









'''simple docstring'''

UpperCAmelCase					:		Union[str, Any]		=							tuple[float, float, float]
UpperCAmelCase					:		int		=							tuple[float, float, float]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     end_pointa[0] - end_pointa[0]
      _snake_case		:	Tuple					     =     end_pointa[1] - end_pointa[1]
      _snake_case		:	Any					     =     end_pointa[2] - end_pointa[2]
      return (x, y, z)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     ab[1] * ac[2] - ab[2] * ac[1]  # *i
      _snake_case		:	List[str]					     =     (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
      _snake_case		:	Optional[int]					     =     ab[0] * ac[1] - ab[1] * ac[0]  # *k
      return (x, y, z)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      return tuple(round(lowerCAmelCase_			,      lowerCAmelCase_	) for x in vector	) == (0, 0, 0)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_ = 10	):





      """simple docstring"""



      _snake_case		:	str					     =     create_vector(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     create_vector(lowerCAmelCase_			,      lowerCAmelCase_	)
      return is_zero_vector(get_ad_vectors_cross(lowerCAmelCase_			,      lowerCAmelCase_	)			,      lowerCAmelCase_	)


