
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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			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}"'''       ,							SCREAMING_SNAKE_CASE_       ,							)
									    is not None
									):
												lowerCAmelCase__      :      Optional[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:
															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__      :      Optional[Any]         =		[
			    'bos_index',
			    'eos_index',
			    'pad_index',
			    'unk_index',
			    'mask_index',
			    'image_size',
			    'use_cache',
			    'out_features',
			    'out_indices',
			]
			lowerCAmelCase__      :      Tuple         =		['encoder_no_repeat_ngram_size']

			# Special cases to be allowed
			lowerCAmelCase__      :      Dict         =		True
			if not attribute_used:
						lowerCAmelCase__      :      Optional[int]         =		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__      :      Any         =		True
									elif attribute in ["tie_word_embeddings"] and default_value is False:
												lowerCAmelCase__      :      Optional[Any]         =		True

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

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

			return attribute_used or case_allowed
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Dict:
			lowerCAmelCase__      :      Dict         =		dict(inspect.signature(config_class.__init__     ).parameters     )
			lowerCAmelCase__      :      List[Any]         =		[x for x in list(signature.keys()     ) if x not in ['self', 'kwargs']]
			lowerCAmelCase__      :      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__      :      Tuple         =		{}
			if len(config_class.attribute_map     ) > 0:
						lowerCAmelCase__      :      int         =		{v: k for k, v in config_class.attribute_map.items()}

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

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

			lowerCAmelCase__      :      Union[str, Any]         =		[]
			for config_param, default_value in zip(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						# `attributes` here is all the variant names for `config_param`
						lowerCAmelCase__      :      Optional[Any]         =		[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(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
									unused_attributes.append(attributes[0]     )

			return sorted(SCREAMING_SNAKE_CASE_     )
def  lowerCAmelCase__			(  )				->		int:
			lowerCAmelCase__      :      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.)
						lowerCAmelCase__      :      Optional[Any]         =		[
						    cls
						    for name, cls in inspect.getmembers(
						        inspect.getmodule(_config_class     )       ,							lambda SCREAMING_SNAKE_CASE_     : inspect.isclass(SCREAMING_SNAKE_CASE_     )
						        and issubclass(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )
						        and inspect.getmodule(SCREAMING_SNAKE_CASE_     ) == inspect.getmodule(_config_class     )       ,							)
						]
						for config_class in config_classes_in_module:
									lowerCAmelCase__      :      Optional[int]         =		check_config_attributes_being_used(SCREAMING_SNAKE_CASE_     )
									if len(SCREAMING_SNAKE_CASE_     ) > 0:
												lowerCAmelCase__      :      Tuple         =		unused_attributes

			if len(SCREAMING_SNAKE_CASE_     ) > 0:
						lowerCAmelCase__      :      Optional[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(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
					check_config_attributes()

import re
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:

			if len(re.findall('[ATCG]'       ,							SCREAMING_SNAKE_CASE_     )     ) != len(SCREAMING_SNAKE_CASE_     ):
						raise ValueError('Invalid Strand'     )

			return dna.translate(dna.maketrans('ATCG'       ,							'TAGC'     )     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()