



"""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 			snake_case_    ( A_   :						Optional[int],							A_   :						Any,							A_   :						int,							A_   :						int		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					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		:							Union[str, Any]            =					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}"''',							A_,							)
                         is not None
                     ):
                            _lowerCamelCase		:							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:
                                   _lowerCamelCase		:							Optional[Any]            =					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		:							str            =					[
           '''bos_index''',
           '''eos_index''',
           '''pad_index''',
           '''unk_index''',
           '''mask_index''',
           '''image_size''',
           '''use_cache''',
           '''out_features''',
           '''out_indices''',
       ]
       _lowerCamelCase		:							Optional[Any]            =					['''encoder_no_repeat_ngram_size''']

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

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            _lowerCamelCase		:							List[str]            =					True
                     elif attribute.endswith('''_token_id'''		):
                            _lowerCamelCase		:							Optional[int]            =					True

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

       return attribute_used or case_allowed






def 			snake_case_    ( A_   :						Optional[int]		):


       '''simple docstring'''





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

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

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

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

       return sorted(A_		)






def 			snake_case_    ( ):


       '''simple docstring'''





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

       if len(A_		) > 0:
              _lowerCamelCase		:							Union[str, 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(A_		)


if __name__ == "__main__":
   check_config_attributes()





from math import sqrt





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5  ,				int(sqrt(UpperCamelCase__ ) + 1 )  ,				6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True





def    __lowerCamelCase      (					UpperCamelCase__ = 10001 ):







							'''simple docstring'''






							snake_case_     							=       0
							snake_case_     							=       1
							while count != nth and number < 3:
														number += 1
														if is_prime(UpperCamelCase__ ):
																					count += 1
							while count != nth:
														number += 2
														if is_prime(UpperCamelCase__ ):
																					count += 1
							return number


if __name__ == "__main__":
					print(F'''{solution() = }''')




