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
A_      =      "src/transformers"


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

A_      =      transformers.models.auto.configuration_auto.CONFIG_MAPPING

A_      =      {
    # 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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       Tuple:



			"""simple docstring"""







			lowercase							=		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							=		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}"',		UpperCAmelCase,		)
									    is not None
									):
												lowercase							=		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							=		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							=		[
			    '''bos_index''',
			    '''eos_index''',
			    '''pad_index''',
			    '''unk_index''',
			    '''mask_index''',
			    '''image_size''',
			    '''use_cache''',
			    '''out_features''',
			    '''out_indices''',
			]
			lowercase							=		['''encoder_no_repeat_ngram_size''']

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

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

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

			return attribute_used or case_allowed






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







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

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

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

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

			return sorted(UpperCAmelCase       )






def        __UpperCAmelCase    (       )->       Union[str, Any]:



			"""simple docstring"""







			lowercase							=		{}
			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							=		[
						    cls
						    for name, cls in inspect.getmembers(
						        inspect.getmodule(_config_class       ),		lambda UpperCAmelCase       : inspect.isclass(UpperCAmelCase       )
						        and issubclass(UpperCAmelCase,		UpperCAmelCase       )
						        and inspect.getmodule(UpperCAmelCase       ) == inspect.getmodule(_config_class       ),		)
						]
						for config_class in config_classes_in_module:
									lowercase							=		check_config_attributes_being_used(UpperCAmelCase       )
									if len(UpperCAmelCase       ) > 0:
												lowercase							=		unused_attributes

			if len(UpperCAmelCase       ) > 0:
						lowercase							=		'''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(UpperCAmelCase       )


if __name__ == "__main__":
						check_config_attributes()






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





