


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
_snake_case  :	Dict			      =       "src/transformers"


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

_snake_case  :	Any			      =       transformers.models.auto.configuration_auto.CONFIG_MAPPING

_snake_case  :	Any			      =       {
    # 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_  (      __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Optional[Any]											=     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	:    Optional[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	:    Optional[int]											=     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	:    Tuple											=     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	:    Dict											=     [
							    "bos_index",
							    "eos_index",
							    "pad_index",
							    "unk_index",
							    "mask_index",
							    "image_size",
							    "use_cache",
							    "out_features",
							    "out_indices",
							]
							__snake_case	:    Union[str, Any]											=     ["encoder_no_repeat_ngram_size"]

							# Special cases to be allowed
							__snake_case	:    Optional[Any]											=     True
							if not attribute_used:
														__snake_case	:    Dict											=     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	:    Any											=     True
																					elif attribute in ["tie_word_embeddings"] and default_value is False:
																												__snake_case	:    List[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[str]											=     True
																					elif attribute.endswith("_token_id"			):
																												__snake_case	:    List[str]											=     True

																					# configuration class specific cases
																					if not case_allowed:
																												__snake_case	:    Dict											=     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        lowerCAmelCase_  (      __lowerCamelCase			):
							__snake_case	:    Dict											=     dict(inspect.signature(config_class.__init__			).parameters			)
							__snake_case	:    str											=     [x for x in list(signature.keys()			) if x not in ["self", "kwargs"]]
							__snake_case	:    Dict											=     [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	:    str											=     {}
							if len(config_class.attribute_map			) > 0:
														__snake_case	:    Any											=     {v: k for k, v in config_class.attribute_map.items()}

							# Get the path to modeling source files
							__snake_case	:    Any											=     inspect.getsourcefile(__lowerCamelCase			)
							__snake_case	:    List[Any]											=     os.path.dirname(__lowerCamelCase			)
							# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
							__snake_case	:    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[int]											=     []
							for config_param, default_value in zip(__lowerCamelCase							,			__lowerCamelCase			):
														# `attributes` here is all the variant names for `config_param`
														__snake_case	:    Any											=     [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        lowerCAmelCase_  (      ):
							__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	:    Dict											=     [
														    cls
														    for name, cls in inspect.getmembers(
														        inspect.getmodule(_config_class			)							,			lambda __lowerCamelCase			: 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	:    List[Any]											=     check_config_attributes_being_used(__lowerCamelCase			)
																					if len(__lowerCamelCase			) > 0:
																												__snake_case	:    Tuple											=     unused_attributes

							if len(__lowerCamelCase			) > 0:
														__snake_case	:    Optional[Any]											=     "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()





import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


_snake_case  :	List[str]			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Any					,			**lowerCamelCase :						Union[str, Any]       )			->					None:
														warnings.warn(
														    "The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use PoolFormerImageProcessor instead."					,			lowerCamelCase					,			)
														super().__init__(*lowerCamelCase					,			**lowerCamelCase       )

