
"""simple docstring"""







import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
							    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
							    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
							    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
							    LayoutLMvaConfig,
							    LayoutLMvaForQuestionAnswering,
							    LayoutLMvaForSequenceClassification,
							    LayoutLMvaForTokenClassification,
							    LayoutLMvaModel,
							)
							from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import LayoutLMvaImageProcessor




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=36 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=6 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=10_00 ,	):
			__lowerCAmelCase    :		Any		    =   parent
			__lowerCAmelCase    :		Optional[Any]		    =   batch_size
			__lowerCAmelCase    :		Optional[Any]		    =   num_channels
			__lowerCAmelCase    :		Optional[int]		    =   image_size
			__lowerCAmelCase    :		str		    =   patch_size
			__lowerCAmelCase    :		Tuple		    =   text_seq_length
			__lowerCAmelCase    :		Any		    =   is_training
			__lowerCAmelCase    :		Tuple		    =   use_input_mask
			__lowerCAmelCase    :		Optional[int]		    =   use_token_type_ids
			__lowerCAmelCase    :		Optional[int]		    =   use_labels
			__lowerCAmelCase    :		List[Any]		    =   vocab_size
			__lowerCAmelCase    :		Optional[Any]		    =   hidden_size
			__lowerCAmelCase    :		Union[str, Any]		    =   num_hidden_layers
			__lowerCAmelCase    :		int		    =   num_attention_heads
			__lowerCAmelCase    :		Dict		    =   intermediate_size
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		int		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Any		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Dict		    =   max_position_embeddings
			__lowerCAmelCase    :		str		    =   type_vocab_size
			__lowerCAmelCase    :		List[str]		    =   type_sequence_label_size
			__lowerCAmelCase    :		List[Any]		    =   initializer_range
			__lowerCAmelCase    :		Any		    =   coordinate_size
			__lowerCAmelCase    :		Any		    =   shape_size
			__lowerCAmelCase    :		str		    =   num_labels
			__lowerCAmelCase    :		int		    =   num_choices
			__lowerCAmelCase    :		str		    =   scope
			__lowerCAmelCase    :		Dict		    =   range_bbox

			# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
			__lowerCAmelCase    :		Dict		    =   text_seq_length
			__lowerCAmelCase    :		Union[str, Any]		    =   (image_size // patch_size) ** 2 + 1
			__lowerCAmelCase    :		List[str]		    =   self.text_seq_length + self.image_seq_length

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   ids_tensor([self.batch_size, self.text_seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		int		    =   ids_tensor([self.batch_size, self.text_seq_length, 4] ,	self.range_bbox				)
			# Ensure that bbox is legal
			for i in range(bbox.shape[0]				):
				for j in range(bbox.shape[1]				):
					if bbox[i, j, 3] < bbox[i, j, 1]:
						__lowerCAmelCase    :		Optional[Any]		    =   bbox[i, j, 3]
						__lowerCAmelCase    :		List[str]		    =   bbox[i, j, 1]
						__lowerCAmelCase    :		Tuple		    =   t
					if bbox[i, j, 2] < bbox[i, j, 0]:
						__lowerCAmelCase    :		Any		    =   bbox[i, j, 2]
						__lowerCAmelCase    :		Union[str, Any]		    =   bbox[i, j, 0]
						__lowerCAmelCase    :		Dict		    =   t

			__lowerCAmelCase    :		int		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

			__lowerCAmelCase    :		Dict		    =   None
			if self.use_input_mask:
				__lowerCAmelCase    :		List[Any]		    =   random_attention_mask([self.batch_size, self.text_seq_length]				)

			__lowerCAmelCase    :		Any		    =   None
			if self.use_token_type_ids:
				__lowerCAmelCase    :		Tuple		    =   ids_tensor([self.batch_size, self.text_seq_length] ,	self.type_vocab_size				)

			__lowerCAmelCase    :		List[Any]		    =   None
			__lowerCAmelCase    :		Union[str, Any]		    =   None
			if self.use_labels:
				__lowerCAmelCase    :		str		    =   ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)
				__lowerCAmelCase    :		int		    =   ids_tensor([self.batch_size, self.text_seq_length] ,	self.num_labels				)

			__lowerCAmelCase    :		Dict		    =   LayoutLMvaConfig(
			    vocab_size=self.vocab_size ,	hidden_size=self.hidden_size ,	num_hidden_layers=self.num_hidden_layers ,	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 ,	max_position_embeddings=self.max_position_embeddings ,	type_vocab_size=self.type_vocab_size ,	initializer_range=self.initializer_range ,	coordinate_size=self.coordinate_size ,	shape_size=self.shape_size ,	input_size=self.image_size ,	patch_size=self.patch_size ,	)

			return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   LayoutLMvaModel(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()

			# text + image
			__lowerCAmelCase    :		Tuple		    =   model(_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   model(
			    _SCREAMING_SNAKE_CASE ,	bbox=_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	token_type_ids=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   model(_SCREAMING_SNAKE_CASE ,	bbox=_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE ,	token_type_ids=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   model(_SCREAMING_SNAKE_CASE ,	bbox=_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE				)

			self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)

			# text only
			__lowerCAmelCase    :		List[Any]		    =   model(_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(
			    result.last_hidden_state.shape ,	(self.batch_size, self.text_seq_length, self.hidden_size)				)

			# image only
			__lowerCAmelCase    :		int		    =   model(pixel_values=_SCREAMING_SNAKE_CASE				)
			self.parent.assertEqual(
			    result.last_hidden_state.shape ,	(self.batch_size, self.image_seq_length, self.hidden_size)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		int		    =   self.num_labels
			__lowerCAmelCase    :		Any		    =   LayoutLMvaForSequenceClassification(_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Optional[int]		    =   model(
			    _SCREAMING_SNAKE_CASE ,	bbox=_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	token_type_ids=_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE ,	)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.num_labels)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[int]		    =   self.num_labels
			__lowerCAmelCase    :		Tuple		    =   LayoutLMvaForTokenClassification(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		Optional[Any]		    =   model(
			    _SCREAMING_SNAKE_CASE ,	bbox=_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	token_type_ids=_SCREAMING_SNAKE_CASE ,	labels=_SCREAMING_SNAKE_CASE ,	)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.text_seq_length, self.num_labels)				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   LayoutLMvaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE				)
			model.to(_SCREAMING_SNAKE_CASE				)
			model.eval()
			__lowerCAmelCase    :		str		    =   model(
			    _SCREAMING_SNAKE_CASE ,	bbox=_SCREAMING_SNAKE_CASE ,	pixel_values=_SCREAMING_SNAKE_CASE ,	attention_mask=_SCREAMING_SNAKE_CASE ,	token_type_ids=_SCREAMING_SNAKE_CASE ,	start_positions=_SCREAMING_SNAKE_CASE ,	end_positions=_SCREAMING_SNAKE_CASE ,	)
			self.parent.assertEqual(result.start_logits.shape ,	(self.batch_size, self.seq_length)				)
			self.parent.assertEqual(result.end_logits.shape ,	(self.batch_size, self.seq_length)				)







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   self.prepare_config_and_inputs()
			(
			    (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     (
			    __lowerCAmelCase
			)    ,     
			)    :		Dict		    =   config_and_inputs
			__lowerCAmelCase    :		Optional[int]		    =   {
			    'input_ids': input_ids,
			    'bbox': bbox,
			    'pixel_values': pixel_values,
			    'token_type_ids': token_type_ids,
			    'attention_mask': input_mask,
			}
			return config, inputs_dict




@require_torch
class 				A__ (			_lowerCamelCase ,  _lowerCamelCase ,  unittest.TestCase):
		A_		: Tuple            =			False
		A_		: List[str]            =			False
		A_		: Union[str, Any]            =			False

		A_		: Dict            =			(
		    (
		        LayoutLMvaModel,
		        LayoutLMvaForSequenceClassification,
		        LayoutLMvaForTokenClassification,
		        LayoutLMvaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		A_		: int            =			(
		    {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
		    if is_torch_available()
		    else {}
		)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
			# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
			# the sequence dimension of the text embedding only.
			# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
			return True

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   LayoutLMvaModelTester(self				)
			__lowerCAmelCase    :		Dict		    =   ConfigTester(self ,	config_class=_SCREAMING_SNAKE_CASE ,	hidden_size=37				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=False				):
			__lowerCAmelCase    :		List[Any]		    =   copy.deepcopy(_SCREAMING_SNAKE_CASE				)
			if model_class in get_values(_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Union[str, Any]		    =   {
				    k: v.unsqueeze(1				).expand(-1 ,	self.model_tester.num_choices ,	-1				).contiguous()
				    if isinstance(_SCREAMING_SNAKE_CASE ,	torch.Tensor				) and v.ndim > 1
				    else v
				    for k, v in inputs_dict.items()
				}
			if return_labels:
				if model_class in get_values(_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		List[str]		    =   torch.ones(self.model_tester.batch_size ,	dtype=torch.long ,	device=_SCREAMING_SNAKE_CASE				)
				elif model_class in get_values(_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		List[str]		    =   torch.zeros(
					    self.model_tester.batch_size ,	dtype=torch.long ,	device=_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		Dict		    =   torch.zeros(
					    self.model_tester.batch_size ,	dtype=torch.long ,	device=_SCREAMING_SNAKE_CASE				)
				elif model_class in [
				    *get_values(_SCREAMING_SNAKE_CASE				),
				]:
					__lowerCAmelCase    :		int		    =   torch.zeros(
					    self.model_tester.batch_size ,	dtype=torch.long ,	device=_SCREAMING_SNAKE_CASE				)
				elif model_class in [
				    *get_values(_SCREAMING_SNAKE_CASE				),
				]:
					__lowerCAmelCase    :		str		    =   torch.zeros(
					    (self.model_tester.batch_size, self.model_tester.text_seq_length) ,	dtype=torch.long ,	device=_SCREAMING_SNAKE_CASE ,	)

			return inputs_dict

		def 							__lowerCamelCase							(   self				):
			self.config_tester.run_common_tests()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
				__lowerCAmelCase    :		List[str]		    =   type
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Union[str, Any]		    =   LayoutLMvaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Dict		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




@require_torch
class 				A__ (			unittest.TestCase):

		@cached_property
		def 							__lowerCamelCase							(   self				):
			return LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE				) if is_vision_available() else None







		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'				).to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		int		    =   self.default_image_processor
			__lowerCAmelCase    :		Union[str, Any]		    =   prepare_img()
			__lowerCAmelCase    :		str		    =   image_processor(images=_SCREAMING_SNAKE_CASE ,	return_tensors='pt'				).pixel_values.to(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   torch.tensor([[1, 2]]				)
			__lowerCAmelCase    :		Optional[Any]		    =   torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]				).unsqueeze(0				)

			# forward pass
			__lowerCAmelCase    :		Any		    =   model(
			    input_ids=input_ids.to(_SCREAMING_SNAKE_CASE				) ,	bbox=bbox.to(_SCREAMING_SNAKE_CASE				) ,	pixel_values=pixel_values.to(_SCREAMING_SNAKE_CASE				) ,	)

			# verify the logits
			__lowerCAmelCase    :		List[str]		    =   torch.Size((1, 1_99, 7_68)				)
			self.assertEqual(outputs.last_hidden_state.shape ,	_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		str		    =   torch.tensor(
			    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]				).to(_SCREAMING_SNAKE_CASE				)

			self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,	_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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   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    :		Union[str, Any]		    =   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}\"" ,						_UpperCamelCase ,						)
			    is not None
			):
				__lowerCAmelCase    :		Optional[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:
					__lowerCAmelCase    :		int		    =   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    :		Optional[Any]		    =   [
	    'bos_index',
	    'eos_index',
	    'pad_index',
	    'unk_index',
	    'mask_index',
	    'image_size',
	    'use_cache',
	    'out_features',
	    'out_indices',
	]
	__lowerCAmelCase    :		Tuple		    =   ['encoder_no_repeat_ngram_size']

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

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

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

	return attribute_used or case_allowed
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   dict(inspect.signature(config_class.__init__	).parameters	)
	__lowerCAmelCase    :		Tuple		    =   [x for x in list(signature.keys()	) if x not in ['self', 'kwargs']]
	__lowerCAmelCase    :		Tuple		    =   [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    :		List[Any]		    =   {}
	if len(config_class.attribute_map	) > 0:
		__lowerCAmelCase    :		Union[str, Any]		    =   {v: k for k, v in config_class.attribute_map.items()}

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

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

	__lowerCAmelCase    :		Optional[int]		    =   []
	for config_param, default_value in zip(_UpperCamelCase ,						_UpperCamelCase	):
		# `attributes` here is all the variant names for `config_param`
		__lowerCAmelCase    :		Optional[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(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
			unused_attributes.append(attributes[0]	)

	return sorted(_UpperCamelCase	)
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   {}
	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    :		int		    =   [
		    cls
		    for name, cls in inspect.getmembers(
		        inspect.getmodule(_config_class	) ,						lambda _UpperCamelCase	: inspect.isclass(_UpperCamelCase	)
		        and issubclass(_UpperCamelCase ,						_UpperCamelCase	)
		        and inspect.getmodule(_UpperCamelCase	) == inspect.getmodule(_config_class	) ,						)
		]
		for config_class in config_classes_in_module:
			__lowerCAmelCase    :		List[Any]		    =   check_config_attributes_being_used(_UpperCamelCase	)
			if len(_UpperCamelCase	) > 0:
				__lowerCAmelCase    :		str		    =   unused_attributes

	if len(_UpperCamelCase	) > 0:
		__lowerCAmelCase    :		int		    =   '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(_UpperCamelCase	)


if __name__ == "__main__":
							check_config_attributes()