import unittest

from transformers import DebertaVaConfig, 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 (
			    DebertaVaForMaskedLM,
			    DebertaVaForMultipleChoice,
			    DebertaVaForQuestionAnswering,
			    DebertaVaForSequenceClassification,
			    DebertaVaForTokenClassification,
			    DebertaVaModel,
			)
			from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST




class  _snake_case		(    __lowerCAmelCase ):







				"""simple docstring"""






				def __init__(      self							:							List[Any]      ,    _A							:							Dict      ,    _A							:							Union[str, Any]=1_3      ,    _A							:							str=7      ,    _A							:							Optional[Any]=True      ,    _A							:							Any=True      ,    _A							:							Any=True      ,    _A							:							Union[str, Any]=True      ,    _A							:							List[str]=9_9      ,    _A							:							Tuple=3_2      ,    _A							:							List[str]=5      ,    _A							:							Optional[Any]=4      ,    _A							:							int=3_7      ,    _A							:							Union[str, Any]="gelu"      ,    _A							:							Optional[int]=0.1      ,    _A							:							Optional[int]=0.1      ,    _A							:							str=5_1_2      ,    _A							:							Union[str, Any]=1_6      ,    _A							:							Any=2      ,    _A							:							Union[str, Any]=0.02      ,    _A							:							Union[str, Any]=False      ,    _A							:							Union[str, Any]=True      ,    _A							:							Tuple="None"      ,    _A							:							Optional[Any]=3      ,    _A							:							List[Any]=4      ,    _A							:							Dict=None      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    parent
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						int			  =    seq_length
											_SCREAMING_SNAKE_CASE		:						str			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    use_input_mask
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    use_token_type_ids
											_SCREAMING_SNAKE_CASE		:						str			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						Any			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						str			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						str			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						int			  =    type_vocab_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    type_sequence_label_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						Any			  =    num_labels
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    num_choices
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    relative_attention
											_SCREAMING_SNAKE_CASE		:						str			  =    position_biased_input
											_SCREAMING_SNAKE_CASE		:						int			  =    pos_att_type
											_SCREAMING_SNAKE_CASE		:						str			  =    scope





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)

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

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
											if self.use_token_type_ids:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size)

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

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.get_config()

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





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											return DebertaVaConfig(
											    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 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							str):



											"""simple docstring"""







											self.parent.assertListEqual(list(result.loss.size())      ,    [])





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							Any      ,    _A							:							Any      ,    _A							:							int      ,    _A							:							Optional[int]      ,    _A							:							List[Any]      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    DebertaVaModel(config=lowerCAmelCase_)
											model.to(lowerCAmelCase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(lowerCAmelCase_      ,    attention_mask=lowerCAmelCase_      ,    token_type_ids=lowerCAmelCase_)[0]
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(lowerCAmelCase_      ,    token_type_ids=lowerCAmelCase_)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(lowerCAmelCase_)[0]

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





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							Tuple      ,    _A							:							int      ,    _A							:							str      ,    _A							:							Union[str, Any]      ,    _A							:							Any      ,    _A							:							Dict      ,    _A							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    DebertaVaForMaskedLM(config=lowerCAmelCase_)
											model.to(lowerCAmelCase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						List[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 		_lowerCAmelCase		(      self							:							str      ,    _A							:							Any      ,    _A							:							Tuple      ,    _A							:							str      ,    _A							:							Union[str, Any]      ,    _A							:							Any      ,    _A							:							Optional[int]      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Any			  =    DebertaVaForSequenceClassification(lowerCAmelCase_)
											model.to(lowerCAmelCase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							List[Any]      ,    _A							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							Tuple      ,    _A							:							List[str]      ,    _A							:							Tuple      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    DebertaVaForTokenClassification(config=lowerCAmelCase_)
											model.to(lowerCAmelCase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Optional[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.num_labels))





				def 		_lowerCAmelCase		(      self							:							List[Any]      ,    _A							:							List[Any]      ,    _A							:							List[str]      ,    _A							:							int      ,    _A							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							Tuple      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    DebertaVaForQuestionAnswering(config=lowerCAmelCase_)
											model.to(lowerCAmelCase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						Union[str, 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 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							List[Any]      ,    _A							:							str      ,    _A							:							Dict      ,    _A							:							List[Any]      ,    _A							:							Optional[int]      ,    _A							:							Optional[int]      ,    _A							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    DebertaVaForMultipleChoice(config=lowerCAmelCase_)
											model.to(lowerCAmelCase_)
											model.eval()
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    input_ids.unsqueeze(1).expand(-1      ,    self.num_choices      ,    -1).contiguous()
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    token_type_ids.unsqueeze(1).expand(-1      ,    self.num_choices      ,    -1).contiguous()
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    input_mask.unsqueeze(1).expand(-1      ,    self.num_choices      ,    -1).contiguous()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(
											    lowerCAmelCase_      ,    attention_mask=lowerCAmelCase_      ,    token_type_ids=lowerCAmelCase_      ,    labels=lowerCAmelCase_      ,    )
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_choices))





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.prepare_config_and_inputs()
											(
											    (
											    _SCREAMING_SNAKE_CASE						
											)  ,				(
											    _SCREAMING_SNAKE_CASE						
											)  ,				(
											    _SCREAMING_SNAKE_CASE						
											)  ,				(
											    _SCREAMING_SNAKE_CASE						
											)  ,				(
											    _SCREAMING_SNAKE_CASE						
											)  ,				(
											    _SCREAMING_SNAKE_CASE						
											)  ,				(
											    _SCREAMING_SNAKE_CASE						
											)  ,										
											)		:						Optional[int]			  =    config_and_inputs
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
											return config, inputs_dict




@require_torch
class  _snake_case		(    __lowerCAmelCase   ,       __lowerCAmelCase   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						(
				    (
				        DebertaVaModel,
				        DebertaVaForMaskedLM,
				        DebertaVaForSequenceClassification,
				        DebertaVaForTokenClassification,
				        DebertaVaForQuestionAnswering,
				        DebertaVaForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				a			=						(
				    {
				        '''feature-extraction''': DebertaVaModel,
				        '''fill-mask''': DebertaVaForMaskedLM,
				        '''question-answering''': DebertaVaForQuestionAnswering,
				        '''text-classification''': DebertaVaForSequenceClassification,
				        '''token-classification''': DebertaVaForTokenClassification,
				        '''zero-shot''': DebertaVaForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)

				a			=						True
				a			=						False
				a			=						False
				a			=						False
				a			=						False





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    DebertaVaModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ConfigTester(self      ,    config_class=lowerCAmelCase_      ,    hidden_size=3_7)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_model(*lowerCAmelCase_)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase_)





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase_)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase_)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase_)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCAmelCase_)





				@slow
				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    DebertaVaModel.from_pretrained(lowerCAmelCase_)
																		self.assertIsNotNone(lowerCAmelCase_)




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







				"""simple docstring"""






				@unittest.skip(reason="""Model not available yet""")
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											pass





				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge""")

											_SCREAMING_SNAKE_CASE		:						int			  =    torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]])
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						int			  =    model(lowerCAmelCase_      ,    attention_mask=lowerCAmelCase_)[0]
											# compare the actual values for a slice.
											_SCREAMING_SNAKE_CASE		:						Any			  =    torch.tensor(
											    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]])
											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,    lowerCAmelCase_      ,    atol=1e-4)      ,    f"""{output[:, 1:4, 1:4]}""")



"""simple docstring"""





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							int      ,    _A							:							List[Any]      ,    _A							:							int      ,    _A							:							int):



											"""simple docstring"""







											if dst_width < 0 or dst_height < 0:
																		raise ValueError("""Destination width/height should be > 0""")

											_SCREAMING_SNAKE_CASE		:						str			  =    img
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    img.shape[1]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    img.shape[0]
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_width
											_SCREAMING_SNAKE_CASE		:						Any			  =    dst_height

											_SCREAMING_SNAKE_CASE		:						Any			  =    self.src_w / self.dst_w
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.src_h / self.dst_h

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
											    np.ones((self.dst_h, self.dst_w, 3)      ,    np.uinta) * 2_5_5
											)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											for i in range(self.dst_h):
																		for j in range(self.dst_w):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    self.img[self.get_y(_A)][self.get_x(_A)]





				def 		_lowerCAmelCase		(      self							:							int      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_x * x)





				def 		_lowerCAmelCase		(      self							:							str      ,    _A							:							int):



											"""simple docstring"""







											return int(self.ratio_y * y)


if __name__ == "__main__":
			lowerCAmelCase_ ,					lowerCAmelCase_     =	800, 600
			lowerCAmelCase_     =	imread('''image_data/lena.jpg''', 1)
			lowerCAmelCase_     =	NearestNeighbour(im, dst_w, dst_h)
			n.process()

			imshow(
			    F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
			)
			waitKey(0)
			destroyAllWindows()


