






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 		_snake_case	(	A__       ):
			_lowercase		:     Any        =				['''image_processor''', '''tokenizer''']
			_lowercase		:     Tuple        =				'''LayoutLMv3ImageProcessor'''
			_lowercase		:     int        =				('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')






			def __init__(							self		,       a=None		,       a=None		,       **a)   ->							int:
						SCREAMING_SNAKE_CASE							=			None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'		,       a		,       )
									SCREAMING_SNAKE_CASE							=			kwargs.pop('feature_extractor')

						SCREAMING_SNAKE_CASE							=			image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError('You need to specify an `image_processor`.')
						if tokenizer is None:
									raise ValueError('You need to specify a `tokenizer`.')

						super().__init__(a		,       a)






			def __call__(							self		,       a		,       a = None		,       a = None		,       a = None		,       a = None		,       a = True		,       a = False		,       a = None		,       a = None		,       a = 0		,       a = None		,       a = None		,       a = None		,       a = False		,       a = False		,       a = False		,       a = False		,       a = True		,       a = None		,       **a		,       )   ->							BatchEncoding:
						# verify input
						if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.')

						if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

						# first, apply the image processor
						SCREAMING_SNAKE_CASE							=			self.image_processor(images=a		,       return_tensors=a)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(a		,       a):
												SCREAMING_SNAKE_CASE							=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
									SCREAMING_SNAKE_CASE							=			features['words']

						SCREAMING_SNAKE_CASE							=			self.tokenizer(
						    text=text if text is not None else features['words']		,       text_pair=text_pair if text_pair is not None else None		,       boxes=boxes if boxes is not None else features['boxes']		,       word_labels=a		,       add_special_tokens=a		,       padding=a		,       truncation=a		,       max_length=a		,       stride=a		,       pad_to_multiple_of=a		,       return_token_type_ids=a		,       return_attention_mask=a		,       return_overflowing_tokens=a		,       return_special_tokens_mask=a		,       return_offsets_mapping=a		,       return_length=a		,       verbose=a		,       return_tensors=a		,       **a		,       )

						# add pixel values
						SCREAMING_SNAKE_CASE							=			features.pop('pixel_values')
						if return_overflowing_tokens is True:
									SCREAMING_SNAKE_CASE							=			self.get_overflowing_images(a		,       encoded_inputs['overflow_to_sample_mapping'])
						SCREAMING_SNAKE_CASE							=			images

						return encoded_inputs






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							str:
						# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
						SCREAMING_SNAKE_CASE							=			[]
						for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx])

						if len(a) != len(a):
									raise ValueError(
									    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
									    f''' {len(a)} and {len(a)}''')

						return images_with_overflow






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							Union[str, Any]:
						return self.tokenizer.batch_decode(*a		,       **a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							Optional[Any]:
						return self.tokenizer.decode(*a		,       **a)






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						return ["input_ids", "bbox", "attention_mask", "pixel_values"]






			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,       a		,       )
						return self.image_processor_class







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,       a		,       )
						return self.image_processor











import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST






class 						UpperCamelCase_  (  snake_case__							):



					def __init__(		self     :     str       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     str=13       ,      lowerCamelCase     :     Optional[Any]=7       ,      lowerCamelCase     :     List[Any]=True       ,      lowerCamelCase     :     Dict=True       ,      lowerCamelCase     :     List[Any]=True       ,      lowerCamelCase     :     Any=True       ,      lowerCamelCase     :     Optional[Any]=99       ,      lowerCamelCase     :     Union[str, Any]=32       ,      lowerCamelCase     :     Any=5       ,      lowerCamelCase     :     Tuple=4       ,      lowerCamelCase     :     Optional[int]=37       ,      lowerCamelCase     :     Optional[Any]="gelu"       ,      lowerCamelCase     :     Optional[Any]=0.1       ,      lowerCamelCase     :     str=0.1       ,      lowerCamelCase     :     Tuple=5_12       ,      lowerCamelCase     :     Union[str, Any]=16       ,      lowerCamelCase     :     Union[str, Any]=2       ,      lowerCamelCase     :     Dict=0.02       ,      lowerCamelCase     :     Any=False       ,      lowerCamelCase     :     int=True       ,      lowerCamelCase     :     Optional[Any]="None"       ,      lowerCamelCase     :     Optional[int]=3       ,      lowerCamelCase     :     List[Any]=4       ,      lowerCamelCase     :     int=None       ,      ):
							lowerCamelCase_	:     int 						=      parent
							lowerCamelCase_	:     List[Any] 						=      batch_size
							lowerCamelCase_	:     Dict 						=      seq_length
							lowerCamelCase_	:     Union[str, Any] 						=      is_training
							lowerCamelCase_	:     str 						=      use_input_mask
							lowerCamelCase_	:     int 						=      use_token_type_ids
							lowerCamelCase_	:     Tuple 						=      use_labels
							lowerCamelCase_	:     str 						=      vocab_size
							lowerCamelCase_	:     int 						=      hidden_size
							lowerCamelCase_	:     Optional[int] 						=      num_hidden_layers
							lowerCamelCase_	:     Optional[Any] 						=      num_attention_heads
							lowerCamelCase_	:     Any 						=      intermediate_size
							lowerCamelCase_	:     List[Any] 						=      hidden_act
							lowerCamelCase_	:     List[Any] 						=      hidden_dropout_prob
							lowerCamelCase_	:     List[Any] 						=      attention_probs_dropout_prob
							lowerCamelCase_	:     List[str] 						=      max_position_embeddings
							lowerCamelCase_	:     Tuple 						=      type_vocab_size
							lowerCamelCase_	:     str 						=      type_sequence_label_size
							lowerCamelCase_	:     Optional[int] 						=      initializer_range
							lowerCamelCase_	:     List[Any] 						=      num_labels
							lowerCamelCase_	:     str 						=      num_choices
							lowerCamelCase_	:     Dict 						=      relative_attention
							lowerCamelCase_	:     Optional[int] 						=      position_biased_input
							lowerCamelCase_	:     List[Any] 						=      pos_att_type
							lowerCamelCase_	:     Optional[int] 						=      scope



					def   __a     (		self     :     List[Any]						):
							lowerCamelCase_	:     Tuple 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size						)

							lowerCamelCase_	:     Dict 						=      None
							if self.use_input_mask:
									lowerCamelCase_	:     Optional[int] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      vocab_size=2						)

							lowerCamelCase_	:     str 						=      None
							if self.use_token_type_ids:
									lowerCamelCase_	:     Dict 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.type_vocab_size						)

							lowerCamelCase_	:     Union[str, Any] 						=      None
							lowerCamelCase_	:     List[Any] 						=      None
							lowerCamelCase_	:     str 						=      None
							if self.use_labels:
									lowerCamelCase_	:     Any 						=      ids_tensor([self.batch_size]       ,      self.type_sequence_label_size						)
									lowerCamelCase_	:     Optional[Any] 						=      ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels						)
									lowerCamelCase_	:     Any 						=      ids_tensor([self.batch_size]       ,      self.num_choices						)

							lowerCamelCase_	:     Optional[Any] 						=      self.get_config()

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



					def   __a     (		self     :     Tuple						):
							return DebertaConfig(
							    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       ,      relative_attention=self.relative_attention       ,      position_biased_input=self.position_biased_input       ,      pos_att_type=self.pos_att_type       ,      )



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     int 						=      self.get_config()
							lowerCamelCase_	:     Any 						=      3_00
							return config



					def   __a     (		self     :     Optional[int]       ,      lowerCamelCase     :     Dict						):
							self.parent.assertListEqual(list(result.loss.size()						)       ,      []						)



					def   __a     (		self     :     Dict       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     List[str]						):
							lowerCamelCase_	:     str 						=      DebertaModel(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Optional[int] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase						)[0]
							lowerCamelCase_	:     Union[str, Any] 						=      model(lowerCamelCase       ,      token_type_ids=lowerCamelCase						)[0]
							lowerCamelCase_	:     List[Any] 						=      model(lowerCamelCase						)[0]

							self.parent.assertListEqual(list(sequence_output.size()						)       ,      [self.batch_size, self.seq_length, self.hidden_size]						)



					def   __a     (		self     :     Optional[Any]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Any						):
							lowerCamelCase_	:     Any 						=      DebertaForMaskedLM(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     Any 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      labels=lowerCamelCase						)

							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)						)



					def   __a     (		self     :     Tuple       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     Any       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Tuple						):
							lowerCamelCase_	:     List[str] 						=      self.num_labels
							lowerCamelCase_	:     str 						=      DebertaForSequenceClassification(lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertListEqual(list(result.logits.size()						)       ,      [self.batch_size, self.num_labels]						)
							self.check_loss_output(lowerCamelCase						)



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Dict       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     str       ,      lowerCamelCase     :     Optional[int]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     Optional[int]						):
							lowerCamelCase_	:     List[Any] 						=      self.num_labels
							lowerCamelCase_	:     str 						=      DebertaForTokenClassification(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[str] 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      labels=lowerCamelCase						)
							self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.num_labels)						)



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     int       ,      lowerCamelCase     :     Union[str, Any]       ,      lowerCamelCase     :     List[Any]       ,      lowerCamelCase     :     List[str]       ,      lowerCamelCase     :     str						):
							lowerCamelCase_	:     int 						=      DebertaForQuestionAnswering(config=lowerCamelCase						)
							model.to(lowerCamelCase						)
							model.eval()
							lowerCamelCase_	:     List[Any] 						=      model(
							    lowerCamelCase       ,      attention_mask=lowerCamelCase       ,      token_type_ids=lowerCamelCase       ,      start_positions=lowerCamelCase       ,      end_positions=lowerCamelCase       ,      )
							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   __a     (		self     :     int						):
							lowerCamelCase_	:     Any 						=      self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      (
							    lowerCamelCase_
							)						,      
							)	:     Dict 						=      config_and_inputs
							lowerCamelCase_	:     str 						=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						str						    =					(
					    (
					        DebertaModel,
					        DebertaForMaskedLM,
					        DebertaForSequenceClassification,
					        DebertaForTokenClassification,
					        DebertaForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					_a      :						Any						    =					(
					    {
					        'feature-extraction': DebertaModel,
					        'fill-mask': DebertaForMaskedLM,
					        'question-answering': DebertaForQuestionAnswering,
					        'text-classification': DebertaForSequenceClassification,
					        'token-classification': DebertaForTokenClassification,
					        'zero-shot': DebertaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					_a      :						Optional[int]						    =					True
					_a      :						List[str]						    =					False
					_a      :						List[str]						    =					False
					_a      :						List[str]						    =					False
					_a      :						List[Any]						    =					False



					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     List[Any] 						=      DebertaModelTester(self						)
							lowerCamelCase_	:     Any 						=      ConfigTester(self       ,      config_class=lowerCamelCase       ,      hidden_size=37						)



					def   __a     (		self     :     Any						):
							self.config_tester.run_common_tests()



					def   __a     (		self     :     List[str]						):
							lowerCamelCase_	:     int 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*lowerCamelCase						)



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     str 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase						)



					def   __a     (		self     :     Optional[int]						):
							lowerCamelCase_	:     Optional[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase						)



					def   __a     (		self     :     str						):
							lowerCamelCase_	:     List[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase						)



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[Any] 						=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase						)





					@slow
					def   __a     (		self     :     Optional[int]						):
							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_	:     Optional[int] 						=      DebertaModel.from_pretrained(lowerCamelCase						)
									self.assertIsNotNone(lowerCamelCase						)






@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCamelCase_  (  unittest.TestCase							):



					@unittest.skip(reason='Model not available yet'						)
					def   __a     (		self     :     str						):
							pass





					@slow
					def   __a     (		self     :     Dict						):
							lowerCamelCase_	:     List[str] 						=      DebertaModel.from_pretrained('microsoft/deberta-base'						)

							lowerCamelCase_	:     Optional[Any] 						=      torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
							lowerCamelCase_	:     List[str] 						=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
							with torch.no_grad():
									lowerCamelCase_	:     Dict 						=      model(lowerCamelCase       ,      attention_mask=lowerCamelCase						)[0]
							# compare the actual values for a slice.
							lowerCamelCase_	:     str 						=      torch.tensor(
							    [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]						)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,      lowerCamelCase       ,      atol=1E-4						)       ,      F"{output[:, 1:4, 1:4]}"						)
