

'''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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       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}"''',    UpperCAmelCase__,    )
                         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  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       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(UpperCAmelCase__			)
       A_		   = os.path.dirname(UpperCAmelCase__			)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       A_		   = [os.path.join(UpperCAmelCase__,    UpperCAmelCase__			) for fn in os.listdir(UpperCAmelCase__			) if fn.startswith("""modeling_"""			)]

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

       A_		   = []
       for config_param, default_value in zip(UpperCAmelCase__,    UpperCAmelCase__			):
              # `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(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			):
                     unused_attributes.append(attributes[0]			)

       return sorted(UpperCAmelCase__			)
def  UpperCAmelCase__      ( )     ->     Union[str, Any]:
       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 UpperCAmelCase__			: inspect.isclass(UpperCAmelCase__			)
                      and issubclass(UpperCAmelCase__,    UpperCAmelCase__			)
                      and inspect.getmodule(UpperCAmelCase__			) == inspect.getmodule(_config_class			),    )
              ]
              for config_class in config_classes_in_module:
                     A_		   = check_config_attributes_being_used(UpperCAmelCase__			)
                     if len(UpperCAmelCase__			) > 0:
                            A_		   = unused_attributes

       if len(UpperCAmelCase__			) > 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(UpperCAmelCase__			)


if __name__ == "__main__":
      check_config_attributes()



'''simple docstring'''







import sys

__lowerCamelCase						    =      (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def  UpperCAmelCase__      ( UpperCAmelCase__ = N			)     ->     int:
       A_		   = -sys.maxsize - 1
       for i in range(len(UpperCAmelCase__			) - 12			):
              A_		   = 1
              for j in range(13			):
                     product *= int(n[i + j]			)
              if product > largest_product:
                     A_		   = product
       return largest_product


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