




"""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         =      '''src/transformers'''


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

a         =      transformers.models.auto.configuration_auto.CONFIG_MAPPING

a         =      {
    # 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 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: str    ,  _snake_case							: Dict    ,  _snake_case							: Union[str, Any]							)   -> Optional[Any]:






      '''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 						_snake_case					( _snake_case							: str							)   -> Dict:






      '''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 						_snake_case					( )   -> Optional[Any]:






      '''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 _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:
                  _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()







"""simple docstring"""

import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation






def 						_snake_case					( _snake_case							: int = 8							)   -> str:






      '''simple docstring'''


      _A        =       ascii_letters + digits + punctuation
      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      i -= len(_snake_case							)
      _A        =       i // 3
      _A        =       i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      _A        =       (
          chars_incl
          + random(_snake_case    ,  quotient + remainder							)
          + random(_snake_case    ,  _snake_case							)
          + random(_snake_case    ,  _snake_case							)
      )
      _A        =       list(_snake_case							)
      shuffle(_snake_case							)
      return "".join(_snake_case							)

      # random is a generalised function for letters, characters and numbers






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: int							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int = 8							)   -> bool:






      '''simple docstring'''


      if len(_snake_case							) < min_length:
            # Your Password must be at least 8 characters long
            return False

      _A        =       any(char in ascii_uppercase for char in password							)
      _A        =       any(char in ascii_lowercase for char in password							)
      _A        =       any(char in digits for char in password							)
      _A        =       any(char in punctuation for char in password							)

      return upper and lower and num and spec_char
      # Passwords should contain UPPERCASE, lowerase
      # numbers, and special characters






def 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


      _A        =       int(input('Please indicate the max length of your password: '							).strip()							)
      _A        =       input(
          'Please indicate the characters that must be in your password: '							).strip()
      print('Password generated:'    ,  password_generator(_snake_case							)							)
      print(
          'Alternative Password generated:'    ,  alternative_password_generator(_snake_case    ,  _snake_case							)    ,  )
      print('[If you are thinking of using this passsword, You better save it.]'							)


if __name__ == "__main__":
       main()

