
'''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
_lowercase			:  Optional[Any]   							=	"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_lowercase			:  Union[str, Any]   							=	direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowercase			:  Optional[Any]   							=	transformers.models.auto.configuration_auto.CONFIG_MAPPING

_lowercase			:  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  snake_case_		(    __SCREAMING_SNAKE_CASE :			Union[str, Any]				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			Union[str, Any]							):



						"""simple docstring"""





						lowercase_     :					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
																		):
																								lowercase_     :					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
																		):
																								lowercase_     :					List[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:
																														lowercase_     :					Tuple													=	True
																		if attribute_used:
																								break
												if attribute_used:
																		break

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

						# Special cases to be allowed
						lowercase_     :					List[str]													=	True
						if not attribute_used:
												lowercase_     :					Union[str, Any]													=	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:
																								lowercase_     :					List[Any]													=	True
																		elif attribute in ["tie_word_embeddings"] and default_value is False:
																								lowercase_     :					List[str]													=	True

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

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

						return attribute_used or case_allowed
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





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

						# Get the path to modeling source files
						lowercase_     :					Optional[int]													=	inspect.getsourcefile(__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Optional[Any]													=	os.path.dirname(__SCREAMING_SNAKE_CASE							)
						# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
						lowercase_     :					int													=	[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
						lowercase_     :					Optional[Any]													=	[]
						for path in modeling_paths:
												if os.path.isfile(__SCREAMING_SNAKE_CASE							):
																		with open(__SCREAMING_SNAKE_CASE							) as fp:
																								modeling_sources.append(fp.read()							)

						lowercase_     :					Tuple													=	[]
						for config_param, default_value in zip(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							):
												# `attributes` here is all the variant names for `config_param`
												lowercase_     :					Dict													=	[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  snake_case_		(    ):



						"""simple docstring"""





						lowercase_     :					Union[str, Any]													=	{}
						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.)
												lowercase_     :					int													=	[
												    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:
																		lowercase_     :					List[str]													=	check_config_attributes_being_used(__SCREAMING_SNAKE_CASE							)
																		if len(__SCREAMING_SNAKE_CASE							) > 0:
																								lowercase_     :					Dict													=	unused_attributes

						if len(__SCREAMING_SNAKE_CASE							) > 0:
												lowercase_     :					Optional[int]													=	'''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()





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__SCREAMING_SNAKE_CASE							)							)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
							__import__("doctest").testmod()



