

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(lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 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_      		= 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
			):
				snake_case_      		= 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_      		= 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_      		= [
	    'bos_index',
	    'eos_index',
	    'pad_index',
	    'unk_index',
	    'mask_index',
	    'image_size',
	    'use_cache',
	    'out_features',
	    'out_indices',
	]
	snake_case_      		= ['encoder_no_repeat_ngram_size']

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

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

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

	return attribute_used or case_allowed





def a(lowercase__	):





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

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

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

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





	'''simple docstring'''
	snake_case_      		= {}
	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_      		= [
		    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:
			snake_case_      		= check_config_attributes_being_used(lowercase__	)
			if len(lowercase__	) > 0:
				snake_case_      		= unused_attributes

	if len(lowercase__	) > 0:
		snake_case_      		= '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()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
