






'''simple docstring'''







import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
		from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Dict=2	,    __SCREAMING_SNAKE_CASE   :      Any=32	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=16	,    __SCREAMING_SNAKE_CASE   :      List[Any]=3	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=True	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=True	,    __SCREAMING_SNAKE_CASE   :      int=32	,    __SCREAMING_SNAKE_CASE   :      Dict=4	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=[0, 1, 2, 3]	,    __SCREAMING_SNAKE_CASE   :      str=4	,    __SCREAMING_SNAKE_CASE   :      Any=37	,    __SCREAMING_SNAKE_CASE   :      Dict="gelu"	,    __SCREAMING_SNAKE_CASE   :      Any=0.1	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=0.1	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=0.02	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=3	,    __SCREAMING_SNAKE_CASE   :      List[str]=[1, 384, 24, 24]	,    __SCREAMING_SNAKE_CASE   :      List[str]=True	,    __SCREAMING_SNAKE_CASE   :      Any=None	,    )      ->      Any:
							a_		:	Dict									=   parent
							a_		:	Dict									=   batch_size
							a_		:	str									=   image_size
							a_		:	int									=   patch_size
							a_		:	Optional[Any]									=   num_channels
							a_		:	Any									=   is_training
							a_		:	str									=   use_labels
							a_		:	Optional[Any]									=   hidden_size
							a_		:	Union[str, Any]									=   num_hidden_layers
							a_		:	int									=   backbone_out_indices
							a_		:	Union[str, Any]									=   num_attention_heads
							a_		:	Optional[int]									=   intermediate_size
							a_		:	Union[str, Any]									=   hidden_act
							a_		:	List[Any]									=   hidden_dropout_prob
							a_		:	Union[str, Any]									=   attention_probs_dropout_prob
							a_		:	Union[str, Any]									=   initializer_range
							a_		:	str									=   num_labels
							a_		:	Any									=   backbone_featmap_shape
							a_		:	Optional[Any]									=   scope
							a_		:	Union[str, Any]									=   is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							a_		:	Optional[int]									=   (image_size // patch_size) ** 2
							a_		:	List[Any]									=   num_patches + 1






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      int:
							a_		:	Any									=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							a_		:	Optional[int]									=   None
							if self.use_labels:
											a_		:	Dict									=   ids_tensor([self.batch_size, self.image_size, self.image_size]	,    self.num_labels       )

							a_		:	List[str]									=   self.get_config()

							return config, pixel_values, labels






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Any:
							a_		:	Dict									=   {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [96, 192, 384, 768],
							    '''num_groups''': 2,
							}

							return DPTConfig(
							    image_size=self.image_size	,    patch_size=self.patch_size	,    num_channels=self.num_channels	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    backbone_out_indices=self.backbone_out_indices	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    is_decoder=__SCREAMING_SNAKE_CASE	,    initializer_range=self.initializer_range	,    is_hybrid=self.is_hybrid	,    backbone_config=__SCREAMING_SNAKE_CASE	,    backbone_featmap_shape=self.backbone_featmap_shape	,    )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Dict:
							a_		:	Optional[int]									=   DPTModel(config=__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	Tuple									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      Tuple:
							a_		:	Optional[Any]									=   self.num_labels
							a_		:	int									=   DPTForDepthEstimation(__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	Union[str, Any]									=   model(__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(result.predicted_depth.shape	,    (self.batch_size, self.image_size, self.image_size)       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Union[str, Any]:
							a_		:	Union[str, Any]									=   self.num_labels
							a_		:	Optional[int]									=   DPTForSemanticSegmentation(__SCREAMING_SNAKE_CASE       )
							model.to(__SCREAMING_SNAKE_CASE       )
							model.eval()
							a_		:	Any									=   model(__SCREAMING_SNAKE_CASE	,    labels=__SCREAMING_SNAKE_CASE       )
							self.parent.assertEqual(
							    result.logits.shape	,    (self.batch_size, self.num_labels, self.image_size, self.image_size)       )







			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	List[Any]									=   self.prepare_config_and_inputs()
							a_					,      a_					,      a_		:	int									=   config_and_inputs
							a_		:	Dict									=   {'''pixel_values''': pixel_values}
							return config, inputs_dict





@require_torch
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			snake_case__	=					(
			    {
			        "depth-estimation": DPTForDepthEstimation,
			        "feature-extraction": DPTModel,
			        "image-segmentation": DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			snake_case__	=					False
			snake_case__	=					False
			snake_case__	=					False






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Optional[int]:
							a_		:	int									=   DPTModelTester(self       )
							a_		:	Union[str, Any]									=   ConfigTester(self	,    config_class=__SCREAMING_SNAKE_CASE	,    has_text_modality=__SCREAMING_SNAKE_CASE	,    hidden_size=37       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Tuple:
							self.config_tester.run_common_tests()






			@unittest.skip(reason='''DPT does not use inputs_embeds'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[str]:
							pass






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Dict:
							a_					,      a_		:	List[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:	List[Any]									=   model_class(__SCREAMING_SNAKE_CASE       )
											self.assertIsInstance(model.get_input_embeddings()	,    (nn.Module)       )
											a_		:	Optional[Any]									=   model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE	,    nn.Linear       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Tuple:
							a_					,      a_		:	Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											a_		:	str									=   model_class(__SCREAMING_SNAKE_CASE       )
											a_		:	Union[str, Any]									=   inspect.signature(model.forward       )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_		:	int									=   [*signature.parameters.keys()]

											a_		:	List[Any]									=   ['''pixel_values''']
											self.assertListEqual(arg_names[:1]	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Any:
							a_		:	int									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Tuple:
							a_		:	Optional[Any]									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      str:
							a_		:	List[str]									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[str]:
							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											a_					,      a_		:	Union[str, Any]									=   self.model_tester.prepare_config_and_inputs_for_common()
											a_		:	Optional[Any]									=   True

											if model_class in get_values(__SCREAMING_SNAKE_CASE       ):
															continue

											a_		:	Optional[int]									=   model_class(__SCREAMING_SNAKE_CASE       )
											model.to(__SCREAMING_SNAKE_CASE       )
											model.train()
											a_		:	List[str]									=   self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    return_labels=__SCREAMING_SNAKE_CASE       )
											a_		:	str									=   model(**__SCREAMING_SNAKE_CASE       ).loss
											loss.backward()






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Tuple:
							for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
															continue

											a_					,      a_		:	List[Any]									=   self.model_tester.prepare_config_and_inputs_for_common()
											a_		:	Union[str, Any]									=   False
											a_		:	Tuple									=   True

											if model_class in get_values(__SCREAMING_SNAKE_CASE       ) or not model_class.supports_gradient_checkpointing:
															continue
											a_		:	List[str]									=   model_class(__SCREAMING_SNAKE_CASE       )
											model.to(__SCREAMING_SNAKE_CASE       )
											model.gradient_checkpointing_enable()
											model.train()
											a_		:	Optional[Any]									=   self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    return_labels=__SCREAMING_SNAKE_CASE       )
											a_		:	Optional[int]									=   model(**__SCREAMING_SNAKE_CASE       ).loss
											loss.backward()






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Any:
							a_					,      a_		:	Tuple									=   self.model_tester.prepare_config_and_inputs_for_common()

							a_		:	Union[str, Any]									=   _config_zero_init(__SCREAMING_SNAKE_CASE       )
							for model_class in self.all_model_classes:
											a_		:	List[str]									=   model_class(config=__SCREAMING_SNAKE_CASE       )
											# Skip the check for the backbone
											a_		:	Union[str, Any]									=   []
											for name, module in model.named_modules():
															if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																			a_		:	Any									=   [f'{name}.{key}' for key in module.state_dict().keys()]
																			break

											for name, param in model.named_parameters():
															if param.requires_grad:
																			if name in backbone_params:
																							continue
																			self.assertIn(
																			    ((param.data.mean() * 1e9).round() / 1e9).item()	,    [0.0, 1.0]	,    msg=f'Parameter {name} of model {model_class} seems not properly initialized'	,    )






			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Union[str, Any]:
							pass






			@slow
			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[Any]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											a_		:	Dict									=   DPTModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
											self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      List[str]:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							a_					,      a_		:	str									=   self.model_tester.prepare_config_and_inputs_for_common()
							a_		:	Optional[Any]									=   '''add'''
							with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
											a_		:	Dict									=   DPTForDepthEstimation(__SCREAMING_SNAKE_CASE       )





def 		_UpperCAmelCase (				):
				a_		:	List[Any]									=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
				return image





@require_torch
@require_vision
@slow
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Optional[int]:
							a_		:	Any									=   DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''       )
							a_		:	List[str]									=   DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''       ).to(__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   prepare_img()
							a_		:	Tuple									=   image_processor(images=__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).to(__SCREAMING_SNAKE_CASE       )

							# forward pass
							with torch.no_grad():
											a_		:	Optional[Any]									=   model(**__SCREAMING_SNAKE_CASE       )
											a_		:	Any									=   outputs.predicted_depth

							# verify the predicted depth
							a_		:	Tuple									=   torch.Size((1, 384, 384)       )
							self.assertEqual(predicted_depth.shape	,    __SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   torch.tensor(
							    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]       ).to(__SCREAMING_SNAKE_CASE       )

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100	,    __SCREAMING_SNAKE_CASE	,    atol=1e-4       )       )












'''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
__lowerCAmelCase									=   'src/transformers'


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

__lowerCAmelCase									=   transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCAmelCase									=   {
    # 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 (				__A				: Optional[Any]    ,		__A				: List[str]    ,		__A				: List[str]    ,		__A				: Union[str, Any]   ):
				a_		:	List[str]									=   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
												):
																a_		:	Tuple									=   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}"'    ,		__A    ,		)
												    is not None
												):
																a_		:	Union[str, Any]									=   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:
																				a_		:	Any									=   True
												if attribute_used:
																break
								if attribute_used:
												break

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

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

												# Allow cases without checking the default value in the configuration class
												elif attribute in attributes_to_allow + attributes_used_in_generation:
																a_		:	Optional[int]									=   True
												elif attribute.endswith('''_token_id'''   ):
																a_		:	Tuple									=   True

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

				return attribute_used or case_allowed





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

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

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

				a_		:	List[Any]									=   []
				for config_param, default_value in zip(__A    ,		__A   ):
								# `attributes` here is all the variant names for `config_param`
								a_		:	int									=   [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(__A    ,		__A    ,		__A    ,		__A   ):
												unused_attributes.append(attributes[0]   )

				return sorted(__A   )





def 		_UpperCAmelCase (				):
				a_		:	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.)
								a_		:	int									=   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )    ,		lambda __A   : inspect.isclass(__A   )
								        and issubclass(__A    ,		__A   )
								        and inspect.getmodule(__A   ) == inspect.getmodule(_config_class   )    ,		)
								]
								for config_class in config_classes_in_module:
												a_		:	int									=   check_config_attributes_being_used(__A   )
												if len(__A   ) > 0:
																a_		:	Dict									=   unused_attributes

				if len(__A   ) > 0:
								a_		:	Dict									=   '''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(__A   )


if __name__ == "__main__":
		check_config_attributes()




