

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 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=13	,       __snake_case=7	,       __snake_case=True	,       __snake_case=True	,       __snake_case=True	,       __snake_case=True	,       __snake_case=99	,       __snake_case=32	,       __snake_case=5	,       __snake_case=4	,       __snake_case=37	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=16	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=False	,       __snake_case=True	,       __snake_case="None"	,       __snake_case=3	,       __snake_case=4	,       __snake_case=None	,       ):
			_UpperCamelCase   :		Optional[int]			=						parent
			_UpperCamelCase   :		List[str]			=						batch_size
			_UpperCamelCase   :		Optional[int]			=						seq_length
			_UpperCamelCase   :		Union[str, Any]			=						is_training
			_UpperCamelCase   :		Any			=						use_input_mask
			_UpperCamelCase   :		Dict			=						use_token_type_ids
			_UpperCamelCase   :		Any			=						use_labels
			_UpperCamelCase   :		Any			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		int			=						num_hidden_layers
			_UpperCamelCase   :		Tuple			=						num_attention_heads
			_UpperCamelCase   :		Optional[Any]			=						intermediate_size
			_UpperCamelCase   :		List[str]			=						hidden_act
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		Optional[int]			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Union[str, Any]			=						max_position_embeddings
			_UpperCamelCase   :		str			=						type_vocab_size
			_UpperCamelCase   :		Tuple			=						type_sequence_label_size
			_UpperCamelCase   :		Union[str, Any]			=						initializer_range
			_UpperCamelCase   :		str			=						num_labels
			_UpperCamelCase   :		str			=						num_choices
			_UpperCamelCase   :		Optional[Any]			=						relative_attention
			_UpperCamelCase   :		str			=						position_biased_input
			_UpperCamelCase   :		Any			=						pos_att_type
			_UpperCamelCase   :		Optional[Any]			=						scope


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size)

			_UpperCamelCase   :		List[str]			=						None
			if self.use_input_mask:
				_UpperCamelCase   :		Union[str, Any]			=						ids_tensor([self.batch_size, self.seq_length]	,       vocab_size=2)

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

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

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

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


		def 		A__					(				self):
			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 		A__					(				self	,       __snake_case):
			self.parent.assertListEqual(list(result.loss.size())	,       [])


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						DebertaVaModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		str			=						model(__snake_case	,       attention_mask=__snake_case	,       token_type_ids=__snake_case)[0]
			_UpperCamelCase   :		Dict			=						model(__snake_case	,       token_type_ids=__snake_case)[0]
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)[0]

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


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		str			=						DebertaVaForMaskedLM(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Tuple			=						model(__snake_case	,       attention_mask=__snake_case	,       token_type_ids=__snake_case	,       labels=__snake_case)

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


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[int]			=						self.num_labels
			_UpperCamelCase   :		Any			=						DebertaVaForSequenceClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case	,       attention_mask=__snake_case	,       token_type_ids=__snake_case	,       labels=__snake_case)
			self.parent.assertListEqual(list(result.logits.size())	,       [self.batch_size, self.num_labels])
			self.check_loss_output(__snake_case)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						self.num_labels
			_UpperCamelCase   :		Union[str, Any]			=						DebertaVaForTokenClassification(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		List[str]			=						model(__snake_case	,       attention_mask=__snake_case	,       token_type_ids=__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.seq_length, self.num_labels))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Tuple			=						DebertaVaForQuestionAnswering(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       token_type_ids=__snake_case	,       start_positions=__snake_case	,       end_positions=__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	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						DebertaVaForMultipleChoice(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		int			=						input_ids.unsqueeze(1).expand(-1	,       self.num_choices	,       -1).contiguous()
			_UpperCamelCase   :		List[Any]			=						token_type_ids.unsqueeze(1).expand(-1	,       self.num_choices	,       -1).contiguous()
			_UpperCamelCase   :		Union[str, Any]			=						input_mask.unsqueeze(1).expand(-1	,       self.num_choices	,       -1).contiguous()
			_UpperCamelCase   :		List[Any]			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       token_type_ids=__snake_case	,       labels=__snake_case	,       )
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_choices))






		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.prepare_config_and_inputs()
			(
			    (
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		
			)   :		Union[str, Any]			=						config_and_inputs
			_UpperCamelCase   :		Tuple			=						{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
			return config, inputs_dict







@require_torch
class 	lowercase    (				_lowercase    ,							_lowercase    ,							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 		A__					(				self):
			_UpperCamelCase   :		Dict			=						DebertaVaModelTester(self)
			_UpperCamelCase   :		Optional[Any]			=						ConfigTester(self	,       config_class=__snake_case	,       hidden_size=37)


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


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_sequence_classification(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_masked_lm(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_question_answering(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_token_classification(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_multiple_choice(*__snake_case)






		@slow
		def 		A__					(				self):
			for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		List[str]			=						DebertaVaModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)







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


		"""simple docstring"""




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






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge')

			_UpperCamelCase   :		List[str]			=						torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
			_UpperCamelCase   :		List[str]			=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
			with torch.no_grad():
				_UpperCamelCase   :		List[Any]			=						model(__snake_case	,       attention_mask=__snake_case)[0]
			# compare the actual values for a slice.
			_UpperCamelCase   :		int			=						torch.tensor(
			    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
			self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,       __snake_case	,       atol=1e-4)	,       f'''{output[:, 1:4, 1:4]}''')







import functools






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[int]							,       UpperCAmelCase_							:     list[int]	)						->				int:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for day in days	):
		raise ValueError('The parameter days should be a list of integers'	)

	if len(UpperCAmelCase_	) != 3 or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for cost in costs	):
		raise ValueError('The parameter costs should be a list of three integers'	)

	if len(UpperCAmelCase_	) == 0:
		return 0

	if min(UpperCAmelCase_	) <= 0:
		raise ValueError('All days elements should be greater than 0'	)

	if max(UpperCAmelCase_	) >= 3_6_6:
		raise ValueError('All days elements should be less than 366'	)

	_UpperCamelCase   :		Union[str, Any]			=						set(UpperCAmelCase_	)

	@functools.cache
	def dynamic_programming(UpperCAmelCase_							:     int	) -> int:
		if index > 3_6_5:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1	)

		return min(
		    costs[0] + dynamic_programming(index + 1	)							,       costs[1] + dynamic_programming(index + 7	)							,       costs[2] + dynamic_programming(index + 3_0	)							,       )

	return dynamic_programming(1	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




