






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


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

__UpperCamelCase					:		Union[str, Any]	  =						transformers.models.auto.configuration_auto.CONFIG_MAPPING

__UpperCamelCase					:		Union[str, Any]	  =						{
    # 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_ (      _A      ,   _A      ,   _A      ,   _A  )			->			Any:







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

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

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

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

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

				return attribute_used or case_allowed
def      a_ (      _A  )			->			Optional[int]:







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

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

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

				snake_case__									=     []
				for config_param, default_value in zip(_A      ,   _A  ):
								# `attributes` here is all the variant names for `config_param`
								snake_case__									=     [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      a_ (      )			->			int:







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

				if len(_A  ) > 0:
								snake_case__									=     '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()












def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				if digit_amount > 0:
								return round(number - int(_A  )      ,   _A  )
				return number - int(_A  )


if __name__ == "__main__":
							print(decimal_isolate(1.5_3, 0))
							print(decimal_isolate(3_5.3_4_5, 1))
							print(decimal_isolate(3_5.3_4_5, 2))
							print(decimal_isolate(3_5.3_4_5, 3))
							print(decimal_isolate(-1_4.7_8_9, 3))
							print(decimal_isolate(0, 2))
							print(decimal_isolate(-1_4.1_2_3, 1))
							print(decimal_isolate(-1_4.1_2_3, 2))
							print(decimal_isolate(-1_4.1_2_3, 3))




