




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_						="""src/transformers"""


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

UpperCAmelCase_						=transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase_						={
    # 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       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							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}\""""						, _snake_case						, )
																					    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       UpperCAmelCase       (     _snake_case							):
							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(_snake_case							)
							lowerCAmelCase   =		os.path.dirname(_snake_case							)
							# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
							lowerCAmelCase   =		[os.path.join(_snake_case						, _snake_case							) for fn in os.listdir(_snake_case							) if fn.startswith('''modeling_'''							)]

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

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

							return sorted(_snake_case							)



def       UpperCAmelCase       (     ):
							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 _snake_case							: inspect.isclass(_snake_case							)
														        and issubclass(_snake_case						, _snake_case							)
														        and inspect.getmodule(_snake_case							) == inspect.getmodule(_config_class							)						, )
														]
														for config_class in config_classes_in_module:
																					lowerCAmelCase   =		check_config_attributes_being_used(_snake_case							)
																					if len(_snake_case							) > 0:
																												lowerCAmelCase   =		unused_attributes

							if len(_snake_case							) > 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(_snake_case							)


if __name__ == "__main__":
		check_config_attributes()









from typing import TYPE_CHECKING

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


UpperCAmelCase_						={
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=["""EfficientFormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EfficientFormerForImageClassification""",
		    """EfficientFormerForImageClassificationWithTeacher""",
		    """EfficientFormerModel""",
		    """EfficientFormerPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEfficientFormerForImageClassification""",
		    """TFEfficientFormerForImageClassificationWithTeacher""",
		    """TFEfficientFormerModel""",
		    """TFEfficientFormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



