




'''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
_SCREAMING_SNAKE_CASE			=						"src/transformers"


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

_SCREAMING_SNAKE_CASE			=						transformers.models.auto.configuration_auto.CONFIG_MAPPING

_SCREAMING_SNAKE_CASE			=						{
    # 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_     )		->		Tuple:
			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}"'       ,SCREAMING_SNAKE_CASE_       ,)
									    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 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Dict:
			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(SCREAMING_SNAKE_CASE_     )
			A_									= os.path.dirname(SCREAMING_SNAKE_CASE_     )
			# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
			A_									= [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
			A_									= []
			for path in modeling_paths:
						if os.path.isfile(SCREAMING_SNAKE_CASE_     ):
									with open(SCREAMING_SNAKE_CASE_     ) as fp:
												modeling_sources.append(fp.read()     )

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

			return sorted(SCREAMING_SNAKE_CASE_     )





def 			lowerCamelCase(							)		->		Dict:
			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 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:
									A_									= check_config_attributes_being_used(SCREAMING_SNAKE_CASE_     )
									if len(SCREAMING_SNAKE_CASE_     ) > 0:
												A_									= unused_attributes

			if len(SCREAMING_SNAKE_CASE_     ) > 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(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
							check_config_attributes()










'''simple docstring'''
def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_     )		->		tuple[float, float]:

			# Check if the input is valid
			if not len(SCREAMING_SNAKE_CASE_     ) == len(SCREAMING_SNAKE_CASE_     ) == 3:
						raise ValueError('Please enter a valid equation.'     )
			if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
						raise ValueError('Both a & b of two equations can\'t be zero.'     )

			# Extract the coefficients
			A_		, A_		, A_									= equationa
			A_		, A_		, A_									= equationa

			# Calculate the determinants of the matrices
			A_									= aa * ba - aa * ba
			A_									= ca * ba - ca * ba
			A_									= aa * ca - aa * ca

			# Check if the system of linear equations has a solution (using Cramer's rule)
			if determinant == 0:
						if determinant_x == determinant_y == 0:
									raise ValueError('Infinite solutions. (Consistent system)'     )
						else:
									raise ValueError('No solution. (Inconsistent system)'     )
			else:
						if determinant_x == determinant_y == 0:
									# Trivial solution (Inconsistent system)
									return (0.0, 0.0)
						else:
									A_									= determinant_x / determinant
									A_									= determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)




