


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    		=       'src/transformers'


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

__UpperCamelCase    		=       transformers.models.auto.configuration_auto.CONFIG_MAPPING

__UpperCamelCase    		=       {
    # 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_(				_A   :Optional[Any]				,     _A   :Optional[Any]				,     _A   :int				,     _A   :int  )->		Any:
			UpperCamelCase__					=       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
									):
												UpperCamelCase__					=       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
									):
												UpperCamelCase__					=       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:
															UpperCamelCase__					=       True
									if attribute_used:
												break
						if attribute_used:
									break

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

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

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

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

			return attribute_used or case_allowed
def        UpperCamelCase_(				_A   :Dict  )->		Optional[int]:
			UpperCamelCase__					=       dict(inspect.signature(config_class.__init__  ).parameters  )
			UpperCamelCase__					=       [x for x in list(signature.keys()  ) if x not in ["self", "kwargs"]]
			UpperCamelCase__					=       [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
			UpperCamelCase__					=       {}
			if len(config_class.attribute_map  ) > 0:
						UpperCamelCase__					=       {v: k for k, v in config_class.attribute_map.items()}

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

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

			UpperCamelCase__					=       []
			for config_param, default_value in zip(_A				,     _A  ):
						# `attributes` here is all the variant names for `config_param`
						UpperCamelCase__					=       [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        UpperCamelCase_(				)->		Optional[Any]:
			UpperCamelCase__					=       {}
			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.)
						UpperCamelCase__					=       [
						    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:
									UpperCamelCase__					=       check_config_attributes_being_used(_A  )
									if len(_A  ) > 0:
												UpperCamelCase__					=       unused_attributes

			if len(_A  ) > 0:
						UpperCamelCase__					=       "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()






from __future__ import annotations

import numpy as np
def        UpperCamelCase_(				_A   :list[float]  )->		Union[str, Any]:
			return np.maximum(0				,     _A  )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


