


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   :    int     =		"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase   :    Optional[int]     =		direct_transformers_import(PATH_TO_TRANSFORMERS)

UpperCAmelCase   :    Any     =		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 	__lowerCamelCase						(							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  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
									):
												lowerCamelCase    		=  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
									):
												lowerCamelCase    		=  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:
															lowerCamelCase    		=  True
									if attribute_used:
												break
						if attribute_used:
									break

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

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

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

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

			return attribute_used or case_allowed






def 	__lowerCamelCase						(							lowerCamelCase__		:						str      ):





			'''simple docstring'''




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

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

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

			lowerCamelCase    		=  []
			for config_param, default_value in zip(lowerCamelCase__  ,							lowerCamelCase__      ):
						# `attributes` here is all the variant names for `config_param`
						lowerCamelCase    		=  [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						(							):





			'''simple docstring'''




			lowerCamelCase    		=  {}
			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.)
						lowerCamelCase    		=  [
						    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:
									lowerCamelCase    		=  check_config_attributes_being_used(lowerCamelCase__      )
									if len(lowerCamelCase__      ) > 0:
												lowerCamelCase    		=  unused_attributes

			if len(lowerCamelCase__      ) > 0:
						lowerCamelCase    		=  "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()





'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


