




'''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
a_      :		Tuple							      =       """src/transformers"""


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

a_      :		Any							      =       transformers.models.auto.configuration_auto.CONFIG_MAPPING

a_      :		Tuple							      =       {
    # 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_							(      __snake_case							:			Any   ,       __snake_case							:			Tuple   ,       __snake_case							:			Dict   ,       __snake_case							:			str   )							->						Optional[Any]:



       """simple docstring"""
       lowerCamelCase_							=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
                     ):
                            lowerCamelCase_							=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
                     ):
                            lowerCamelCase_							=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:
                                   lowerCamelCase_							=True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

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

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

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

       return attribute_used or case_allowed





def      a_							(      __snake_case							:			Dict   )							->						Optional[int]:



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

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

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

       lowerCamelCase_							=[]
       for config_param, default_value in zip(__snake_case   ,       __snake_case   ):
              # `attributes` here is all the variant names for `config_param`
              lowerCamelCase_							=[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      a_							(      )							->						List[Any]:



       """simple docstring"""
       lowerCamelCase_							={}
       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.)
              lowerCamelCase_							=[
                  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:
                     lowerCamelCase_							=check_config_attributes_being_used(__snake_case   )
                     if len(__snake_case   ) > 0:
                            lowerCamelCase_							=unused_attributes

       if len(__snake_case   ) > 0:
              lowerCamelCase_							='''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'''







from __future__ import annotations

from typing import TypedDict







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str
      __UpperCamelCase       :  int


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			list[str]:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )

       return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase  )  )]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			BWTTransformDict:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )
       if not s:
              raise ValueError("""The parameter s must not be empty."""  )

       _a    : List[Any]     					=all_rotations(_UpperCAmelCase  )
       rotations.sort()  # sort the list of rotations in alphabetically order
       # make a string composed of the last char of each rotation
       _a    : BWTTransformDict     					={
           "bwt_string": "".join([word[-1] for word in rotations]  ),
           "idx_original_string": rotations.index(_UpperCAmelCase  ),
       }
       return response


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	int  )  ->			str:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter bwt_string type must be str."""  )
       if not bwt_string:
              raise ValueError("""The parameter bwt_string must not be empty."""  )
       try:
              _a    : List[str]     					=int(_UpperCAmelCase  )
       except ValueError:
              raise TypeError(
                  """The parameter idx_original_string type must be int or passive"""
                  """ of cast to int."""  )
       if idx_original_string < 0:
              raise ValueError("""The parameter idx_original_string must not be lower than 0."""  )
       if idx_original_string >= len(_UpperCAmelCase  ):
              raise ValueError(
                  """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""  )

       _a    : Optional[int]     					=[""""""] * len(_UpperCAmelCase  )
       for _ in range(len(_UpperCAmelCase  )  ):
              for i in range(len(_UpperCAmelCase  )  ):
                     _a    : int     					=bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   A__:    Any					    =       '''Provide a string that I will generate its BWT transform: '''
   A__:    Union[str, Any]					    =       input(entry_msg).strip()
   A__:    Optional[int]					    =       bwt_transform(s)
   print(
       F"Burrows Wheeler transform for string '{s}' results "
       F"in '{result['bwt_string']}'"
   )
   A__:    Union[str, Any]					    =       reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
   print(
       F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
       F"we get original string '{original_string}'"
   )


