





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	:					List[str]			  =					"""src/transformers"""


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

__lowerCamelCase	:					Dict			  =					transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCamelCase	:					List[str]			  =					{
    # 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					)						->			str:






 """simple docstring"""




 UpperCamelCase     : Union[str, Any]	     =       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
   ):
    UpperCamelCase     : 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}\""""							,       _lowerCAmelCase							,       )
       is not None
   ):
    UpperCamelCase     : List[Any]	     =       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:
     UpperCamelCase     : Optional[int]	     =       True
   if attribute_used:
    break
  if attribute_used:
   break

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

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

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

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

 return attribute_used or case_allowed



def 		A_    (					_lowerCAmelCase					)						->			List[str]:






 """simple docstring"""




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

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

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

 UpperCamelCase     : List[str]	     =       []
 for config_param, default_value in zip(_lowerCAmelCase							,       _lowerCAmelCase					):
  # `attributes` here is all the variant names for `config_param`
  UpperCamelCase     : 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(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					):
   unused_attributes.append(attributes[0]					)

 return sorted(_lowerCAmelCase					)



def 		A_    (					)						->			Optional[Any]:






 """simple docstring"""




 UpperCamelCase     : Dict	     =       {}
 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.)
  UpperCamelCase     : Union[str, Any]	     =       [
      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:
   UpperCamelCase     : Optional[int]	     =       check_config_attributes_being_used(_lowerCAmelCase					)
   if len(_lowerCAmelCase					) > 0:
    UpperCamelCase     : Tuple	     =       unused_attributes

 if len(_lowerCAmelCase					) > 0:
  UpperCamelCase     : Dict	     =       "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()







import math



def 		A_    (					_lowerCAmelCase					)						->			list:
 UpperCamelCase     : str	     =       [True] * n
 UpperCamelCase     : Optional[int]	     =       False
 UpperCamelCase     : str	     =       False
 UpperCamelCase     : List[Any]	     =       True

 for i in range(3							,       int(n**0.5 + 1					)							,       2					):
  UpperCamelCase     : int	     =       i * 2
  while index < n:
   UpperCamelCase     : Optional[Any]	     =       False
   UpperCamelCase     : Optional[Any]	     =       index + i

 UpperCamelCase     : Optional[Any]	     =       [2]

 for i in range(3							,       _lowerCAmelCase							,       2					):
  if is_prime[i]:
   primes.append(_lowerCAmelCase					)

 return primes



def 		A_    (					_lowerCAmelCase = 9999_6666_3333					)						->			int:
 UpperCamelCase     : Tuple	     =       math.floor(math.sqrt(_lowerCAmelCase					)					) + 100
 UpperCamelCase     : Dict	     =       prime_sieve(_lowerCAmelCase					)

 UpperCamelCase     : Optional[Any]	     =       0
 UpperCamelCase     : Tuple	     =       0
 UpperCamelCase     : List[str]	     =       primes[prime_index]

 while (last_prime**2) <= limit:
  UpperCamelCase     : Dict	     =       primes[prime_index + 1]

  UpperCamelCase     : Any	     =       last_prime**2
  UpperCamelCase     : Union[str, Any]	     =       next_prime**2

  # Get numbers divisible by lps(current)
  UpperCamelCase     : Dict	     =       lower_bound + last_prime
  while upper_bound > current <= limit:
   matches_sum += current
   current += last_prime

  # Reset the upper_bound
  while (upper_bound - next_prime) > limit:
   upper_bound -= next_prime

  # Add the numbers divisible by ups(current)
  UpperCamelCase     : List[str]	     =       upper_bound - next_prime
  while current > lower_bound:
   matches_sum += current
   current -= next_prime

  # Remove the numbers divisible by both ups and lps
  UpperCamelCase     : Dict	     =       0
  while upper_bound > current <= limit:
   if current <= lower_bound:
    # Increment the current number
    current += last_prime * next_prime
    continue

   if current > limit:
    break

   # Remove twice since it was added by both ups and lps
   matches_sum -= current * 2

   # Increment the current number
   current += last_prime * next_prime

  # Setup for next pair
  UpperCamelCase     : str	     =       next_prime
  prime_index += 1

 return matches_sum


if __name__ == "__main__":
    print(solution())
