
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      :				Dict          = "src/transformers"


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

_UpperCAmelCase      :				List[str]          = transformers.models.auto.configuration_auto.CONFIG_MAPPING

_UpperCAmelCase      :				str          = {
    # 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    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		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}\""					, lowercase					, )
						    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    A (      lowercase				)     ->						List[Any]:
		'''simple docstring'''

		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(lowercase				)
		UpperCamelCase						=  os.path.dirname(lowercase				)
		# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
		UpperCamelCase						=  [os.path.join(lowercase					, lowercase				) for fn in os.listdir(lowercase				) if fn.startswith('modeling_'				)]

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

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

		return sorted(lowercase				)


def    A (      )     ->						Tuple:
		'''simple docstring'''

		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 lowercase				: inspect.isclass(lowercase				)
				        and issubclass(lowercase					, lowercase				)
				        and inspect.getmodule(lowercase				) == inspect.getmodule(_config_class				)					, )
				]
				for config_class in config_classes_in_module:
						UpperCamelCase						=  check_config_attributes_being_used(lowercase				)
						if len(lowercase				) > 0:
								UpperCamelCase						=  unused_attributes

		if len(lowercase				) > 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(lowercase				)


if __name__ == "__main__":
					check_config_attributes()


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowercase				)
		UpperCamelCase			,						UpperCamelCase						=  divmod(lowercase					, 2				)
		return binary_recursive(lowercase				) + str(lowercase				)


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  str(lowercase				).strip()
		if not number:
				raise ValueError('No input value was provided'				)
		UpperCamelCase						=  '-' if number.startswith('-'				) else ''
		UpperCamelCase						=  number.lstrip('-'				)
		if not number.isnumeric():
				raise ValueError('Input value is not an integer'				)
		return f'''{negative}0b{binary_recursive(int(lowercase				)				)}'''


if __name__ == "__main__":
					from doctest import testmod

					testmod()
