



'''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
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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Dict      							=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
									):
												snake_case:    int      							=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}"'''				,							__lowerCAmelCase				,							)
									    is not None
									):
												snake_case:    Dict      							=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:
															snake_case:    str      							=True
									if attribute_used:
												break
						if attribute_used:
									break

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

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

									# Allow cases without checking the default value in the configuration class
									elif attribute in attributes_to_allow + attributes_used_in_generation:
												snake_case:    List[Any]      							=True
									elif attribute.endswith('_token_id'					):
												snake_case:    List[str]      							=True

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

			return attribute_used or case_allowed







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=dict(inspect.signature(config_class.__init__					).parameters					)
			snake_case:    int      							=[x for x in list(signature.keys()					) if x not in ["""self""", """kwargs"""]]
			snake_case:    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
			snake_case:    List[str]      							={}
			if len(config_class.attribute_map					) > 0:
						snake_case:    Dict      							={v: k for k, v in config_class.attribute_map.items()}

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

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

			snake_case:    Optional[Any]      							=[]
			for config_param, default_value in zip(__lowerCAmelCase				,							__lowerCAmelCase					):
						# `attributes` here is all the variant names for `config_param`
						snake_case:    str      							=[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(__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase				,							__lowerCAmelCase					):
									unused_attributes.append(attributes[0]					)

			return sorted(__lowerCAmelCase					)







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





			"""simple docstring"""







			snake_case:    Optional[int]      							={}
			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.)
						snake_case:    Optional[Any]      							=[
						    cls
						    for name, cls in inspect.getmembers(
						        inspect.getmodule(_config_class					)				,							lambda __UpperCAmelCase					: inspect.isclass(__lowerCAmelCase					)
						        and issubclass(__lowerCAmelCase				,							__lowerCAmelCase					)
						        and inspect.getmodule(__lowerCAmelCase					) == inspect.getmodule(_config_class					)				,							)
						]
						for config_class in config_classes_in_module:
									snake_case:    Any      							=check_config_attributes_being_used(__lowerCAmelCase					)
									if len(__lowerCAmelCase					) > 0:
												snake_case:    Dict      							=unused_attributes

			if len(__lowerCAmelCase					) > 0:
						snake_case:    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(__lowerCAmelCase					)


if __name__ == "__main__":
	check_config_attributes()












from __future__ import annotations


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple							)     ->  Optional[Any]:  # noqa: E741
   while r - l > 1:
      __UpperCamelCase	:			int					=					(l + r) // 2
      if v[m] >= key:
         __UpperCamelCase	:			Dict					=					m
      else:
         __UpperCamelCase	:			Optional[Any]					=					m  # noqa: E741
   return r


def 				__lowerCamelCase     (							__lowerCAmelCase							:					list[int]							)     ->  int:
   if len(__lowerCAmelCase							) == 0:
      return 0

   __UpperCamelCase	:			Optional[Any]					=					[0] * len(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					1

   __UpperCamelCase	:			Union[str, Any]					=					v[0]

   for i in range(1					,							len(__lowerCAmelCase							)							):
      if v[i] < tail[0]:
         __UpperCamelCase	:			Dict					=					v[i]
      elif v[i] > tail[length - 1]:
         __UpperCamelCase	:			Optional[int]					=					v[i]
         length += 1
      else:
         __UpperCamelCase	:			List[Any]					=					v[i]

   return length


if __name__ == "__main__":
       import doctest

       doctest.testmod()



