
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    lowerCAmelCase__     (	__lowercase			):






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






		def  __A       (		self		:							Any  )     ->							str:
						__lowerCamelCase    					=	ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size  )

						__lowerCamelCase    					=	None
						if self.use_input_mask:
										__lowerCamelCase    					=	ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2  )

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

						__lowerCamelCase    					=	None
						__lowerCamelCase    					=	None
						__lowerCamelCase    					=	None
						if self.use_labels:
										__lowerCamelCase    					=	ids_tensor([self.batch_size]							,			self.type_sequence_label_size  )
										__lowerCamelCase    					=	ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels  )
										__lowerCamelCase    					=	ids_tensor([self.batch_size]							,			self.num_choices  )

						__lowerCamelCase    					=	self.get_config()

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






		def  __A       (		self		:							Any  )     ->							List[str]:
						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		:							Dict  )     ->							Dict:
						__lowerCamelCase    					=	self.get_config()
						__lowerCamelCase    					=	3_00
						return config






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Union[str, Any]:
						self.parent.assertListEqual(list(result.loss.size()  )							,			[]  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							Optional[int]:
						__lowerCamelCase    					=	DebertaModel(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			token_type_ids=SCREAMING_SNAKE_CASE__  )[0]
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			token_type_ids=SCREAMING_SNAKE_CASE__  )[0]
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__  )[0]

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






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							List[str]:
						__lowerCamelCase    					=	DebertaForMaskedLM(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(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.seq_length, self.vocab_size)  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							List[str]:
						__lowerCamelCase    					=	self.num_labels
						__lowerCamelCase    					=	DebertaForSequenceClassification(SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__							,			token_type_ids=SCREAMING_SNAKE_CASE__							,			labels=SCREAMING_SNAKE_CASE__  )
						self.parent.assertListEqual(list(result.logits.size()  )							,			[self.batch_size, self.num_labels]  )
						self.check_loss_output(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Any:
						__lowerCamelCase    					=	self.num_labels
						__lowerCamelCase    					=	DebertaForTokenClassification(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(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.seq_length, self.num_labels)  )






		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Any:
						__lowerCamelCase    					=	DebertaForQuestionAnswering(config=SCREAMING_SNAKE_CASE__  )
						model.to(SCREAMING_SNAKE_CASE__  )
						model.eval()
						__lowerCamelCase    					=	model(
						    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  __A       (		self		:							str  )     ->							str:
						__lowerCamelCase    					=	self.prepare_config_and_inputs()
						(
						    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    (
						    __lowerCamelCase
						)					,    
						)    					=	config_and_inputs
						__lowerCamelCase    					=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
						return config, inputs_dict





@require_torch
class    lowerCAmelCase__     (	__lowercase  ,	__lowercase  ,	unittest.TestCase			):
		a__       :						Optional[int]        =		(
		    (
		        DebertaModel,
		        DebertaForMaskedLM,
		        DebertaForSequenceClassification,
		        DebertaForTokenClassification,
		        DebertaForQuestionAnswering,
		    )
		    if is_torch_available()
		    else ()
		)
		a__       :						Union[str, 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__       :						Dict        =		True
		a__       :						Dict        =		False
		a__       :						str        =		False
		a__       :						str        =		False
		a__       :						int        =		False






		def  __A       (		self		:							List[str]  )     ->							Tuple:
						__lowerCamelCase    					=	DebertaModelTester(self  )
						__lowerCamelCase    					=	ConfigTester(self							,			config_class=SCREAMING_SNAKE_CASE__							,			hidden_size=37  )






		def  __A       (		self		:							Optional[int]  )     ->							Any:
						self.config_tester.run_common_tests()






		def  __A       (		self		:							Optional[int]  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_model(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[str]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_sequence_classification(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							List[Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_masked_lm(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_question_answering(*SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							Dict:
						__lowerCamelCase    					=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_token_classification(*SCREAMING_SNAKE_CASE__  )







		@slow
		def  __A       (		self		:							Tuple  )     ->							Optional[int]:
						for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										__lowerCamelCase    					=	DebertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )





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






		@unittest.skip(reason='''Model not available yet'''  )
		def  __A       (		self		:							Dict  )     ->							List[str]:
						pass







		@slow
		def  __A       (		self		:							Optional[Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	DebertaModel.from_pretrained('''microsoft/deberta-base'''  )

						__lowerCamelCase    					=	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    					=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
						with torch.no_grad():
										__lowerCamelCase    					=	model(SCREAMING_SNAKE_CASE__							,			attention_mask=SCREAMING_SNAKE_CASE__  )[0]
						# compare the actual values for a slice.
						__lowerCamelCase    					=	torch.tensor(
						    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]  )
						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,			SCREAMING_SNAKE_CASE__							,			atol=1e-4  )							,			f'''{output[:, 1:4, 1:4]}'''  )





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

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    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()



