


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 					_a							(				SCREAMING_SNAKE_CASE_):




	def __init__(					self						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Dict=13		,					_SCREAMING_SNAKE_CASE						:		List[str]=7		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]=True		,					_SCREAMING_SNAKE_CASE						:		List[Any]=True		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]=True		,					_SCREAMING_SNAKE_CASE						:		Any=True		,					_SCREAMING_SNAKE_CASE						:		List[str]=99		,					_SCREAMING_SNAKE_CASE						:		List[Any]=32		,					_SCREAMING_SNAKE_CASE						:		List[str]=5		,					_SCREAMING_SNAKE_CASE						:		str=4		,					_SCREAMING_SNAKE_CASE						:		Optional[int]=37		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		,					_SCREAMING_SNAKE_CASE						:		str=0.1		,					_SCREAMING_SNAKE_CASE						:		str=0.1		,					_SCREAMING_SNAKE_CASE						:		Tuple=512		,					_SCREAMING_SNAKE_CASE						:		int=16		,					_SCREAMING_SNAKE_CASE						:		str=2		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]=0.02		,					_SCREAMING_SNAKE_CASE						:		int=False		,					_SCREAMING_SNAKE_CASE						:		List[str]=True		,					_SCREAMING_SNAKE_CASE						:		Tuple="None"		,					_SCREAMING_SNAKE_CASE						:		Tuple=3		,					_SCREAMING_SNAKE_CASE						:		int=4		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		,					)->       Dict:
			lowerCAmelCase__   :			Dict         =					parent
			lowerCAmelCase__   :			str         =					batch_size
			lowerCAmelCase__   :			List[Any]         =					seq_length
			lowerCAmelCase__   :			Dict         =					is_training
			lowerCAmelCase__   :			Dict         =					use_input_mask
			lowerCAmelCase__   :			int         =					use_token_type_ids
			lowerCAmelCase__   :			int         =					use_labels
			lowerCAmelCase__   :			Optional[int]         =					vocab_size
			lowerCAmelCase__   :			Tuple         =					hidden_size
			lowerCAmelCase__   :			Tuple         =					num_hidden_layers
			lowerCAmelCase__   :			Dict         =					num_attention_heads
			lowerCAmelCase__   :			Union[str, Any]         =					intermediate_size
			lowerCAmelCase__   :			str         =					hidden_act
			lowerCAmelCase__   :			Any         =					hidden_dropout_prob
			lowerCAmelCase__   :			List[str]         =					attention_probs_dropout_prob
			lowerCAmelCase__   :			List[str]         =					max_position_embeddings
			lowerCAmelCase__   :			List[str]         =					type_vocab_size
			lowerCAmelCase__   :			Tuple         =					type_sequence_label_size
			lowerCAmelCase__   :			List[Any]         =					initializer_range
			lowerCAmelCase__   :			Union[str, Any]         =					num_labels
			lowerCAmelCase__   :			Optional[Any]         =					num_choices
			lowerCAmelCase__   :			Tuple         =					relative_attention
			lowerCAmelCase__   :			Tuple         =					position_biased_input
			lowerCAmelCase__   :			Dict         =					pos_att_type
			lowerCAmelCase__   :			Any         =					scope




	def 							UpperCAmelCase__(					self						:		Dict		)->       Union[str, Any]:
			lowerCAmelCase__   :			int         =					ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size		)

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

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

			lowerCAmelCase__   :			Union[str, Any]         =					None
			lowerCAmelCase__   :			Union[str, Any]         =					None
			lowerCAmelCase__   :			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.seq_length]		,					self.num_labels		)
					lowerCAmelCase__   :			str         =					ids_tensor([self.batch_size]		,					self.num_choices		)

			lowerCAmelCase__   :			List[str]         =					self.get_config()

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




	def 							UpperCAmelCase__(					self						:		int		)->       Union[str, Any]:
			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 							UpperCAmelCase__(					self						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Any		)->       Optional[int]:
			self.parent.assertListEqual(list(result.loss.size()		)		,					[]		)




	def 							UpperCAmelCase__(					self						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		str		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		int		,					_SCREAMING_SNAKE_CASE						:		Dict		)->       str:
			lowerCAmelCase__   :			Union[str, Any]         =					DebertaVaModel(config=a__		)
			model.to(a__		)
			model.eval()
			lowerCAmelCase__   :			List[Any]         =					model(a__		,					attention_mask=a__		,					token_type_ids=a__		)[0]
			lowerCAmelCase__   :			List[Any]         =					model(a__		,					token_type_ids=a__		)[0]
			lowerCAmelCase__   :			Any         =					model(a__		)[0]

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




	def 							UpperCAmelCase__(					self						:		str		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		Dict		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		List[str]		)->       Dict:
			lowerCAmelCase__   :			List[str]         =					DebertaVaForMaskedLM(config=a__		)
			model.to(a__		)
			model.eval()
			lowerCAmelCase__   :			Optional[Any]         =					model(a__		,					attention_mask=a__		,					token_type_ids=a__		,					labels=a__		)

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




	def 							UpperCAmelCase__(					self						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		str		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		str		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Any		)->       Dict:
			lowerCAmelCase__   :			int         =					self.num_labels
			lowerCAmelCase__   :			int         =					DebertaVaForSequenceClassification(a__		)
			model.to(a__		)
			model.eval()
			lowerCAmelCase__   :			Optional[Any]         =					model(a__		,					attention_mask=a__		,					token_type_ids=a__		,					labels=a__		)
			self.parent.assertListEqual(list(result.logits.size()		)		,					[self.batch_size, self.num_labels]		)
			self.check_loss_output(a__		)




	def 							UpperCAmelCase__(					self						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		int		,					_SCREAMING_SNAKE_CASE						:		str		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Dict		)->       Optional[Any]:
			lowerCAmelCase__   :			List[Any]         =					self.num_labels
			lowerCAmelCase__   :			str         =					DebertaVaForTokenClassification(config=a__		)
			model.to(a__		)
			model.eval()
			lowerCAmelCase__   :			Optional[Any]         =					model(a__		,					attention_mask=a__		,					token_type_ids=a__		,					labels=a__		)
			self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)		)




	def 							UpperCAmelCase__(					self						:		str		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		)->       int:
			lowerCAmelCase__   :			Any         =					DebertaVaForQuestionAnswering(config=a__		)
			model.to(a__		)
			model.eval()
			lowerCAmelCase__   :			Dict         =					model(
			    a__		,					attention_mask=a__		,					token_type_ids=a__		,					start_positions=a__		,					end_positions=a__		,					)
			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 							UpperCAmelCase__(					self						:		int		,					_SCREAMING_SNAKE_CASE						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		Any		,					_SCREAMING_SNAKE_CASE						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		Dict		)->       List[str]:
			lowerCAmelCase__   :			Union[str, Any]         =					DebertaVaForMultipleChoice(config=a__		)
			model.to(a__		)
			model.eval()
			lowerCAmelCase__   :			Optional[int]         =					input_ids.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
			lowerCAmelCase__   :			Optional[Any]         =					token_type_ids.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
			lowerCAmelCase__   :			str         =					input_mask.unsqueeze(1		).expand(-1		,					self.num_choices		,					-1		).contiguous()
			lowerCAmelCase__   :			List[str]         =					model(
			    a__		,					attention_mask=a__		,					token_type_ids=a__		,					labels=a__		,					)
			self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)		)




	def 							UpperCAmelCase__(					self						:		int		)->       Dict:
			lowerCAmelCase__   :			Tuple         =					self.prepare_config_and_inputs()
			(
			    lowerCAmelCase__	
			)   :			Union[str, Any]         =					config_and_inputs
			lowerCAmelCase__   :			Union[str, Any]         =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
			return config, inputs_dict





@require_torch
class 					_a							(				SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						unittest.TestCase):
	_a  :  Union[str, Any]          =							(
	    (
	        DebertaVaModel,
	        DebertaVaForMaskedLM,
	        DebertaVaForSequenceClassification,
	        DebertaVaForTokenClassification,
	        DebertaVaForQuestionAnswering,
	        DebertaVaForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	_a  :  str          =							(
	    {
	        "feature-extraction": DebertaVaModel,
	        "fill-mask": DebertaVaForMaskedLM,
	        "question-answering": DebertaVaForQuestionAnswering,
	        "text-classification": DebertaVaForSequenceClassification,
	        "token-classification": DebertaVaForTokenClassification,
	        "zero-shot": DebertaVaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	_a  :  Optional[Any]          =							True
	_a  :  List[Any]          =							False
	_a  :  List[Any]          =							False
	_a  :  Dict          =							False
	_a  :  Tuple          =							False




	def 							UpperCAmelCase__(					self						:		Tuple		)->       Optional[int]:
			lowerCAmelCase__   :			Optional[Any]         =					DebertaVaModelTester(self		)
			lowerCAmelCase__   :			Any         =					ConfigTester(self		,					config_class=a__		,					hidden_size=37		)




	def 							UpperCAmelCase__(					self						:		int		)->       Union[str, Any]:
			self.config_tester.run_common_tests()




	def 							UpperCAmelCase__(					self						:		Tuple		)->       Tuple:
			lowerCAmelCase__   :			Optional[int]         =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_model(*a__		)




	def 							UpperCAmelCase__(					self						:		Union[str, Any]		)->       str:
			lowerCAmelCase__   :			List[str]         =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_sequence_classification(*a__		)




	def 							UpperCAmelCase__(					self						:		Tuple		)->       Any:
			lowerCAmelCase__   :			Any         =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_masked_lm(*a__		)




	def 							UpperCAmelCase__(					self						:		List[Any]		)->       Optional[Any]:
			lowerCAmelCase__   :			List[Any]         =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_question_answering(*a__		)




	def 							UpperCAmelCase__(					self						:		Any		)->       Union[str, Any]:
			lowerCAmelCase__   :			List[Any]         =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_token_classification(*a__		)




	def 							UpperCAmelCase__(					self						:		List[str]		)->       Optional[int]:
			lowerCAmelCase__   :			Union[str, Any]         =					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_deberta_for_multiple_choice(*a__		)




	@slow
	def 							UpperCAmelCase__(					self						:		Any		)->       Tuple:
			for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCAmelCase__   :			Tuple         =					DebertaVaModel.from_pretrained(a__		)
					self.assertIsNotNone(a__		)





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




	@unittest.skip(reason='''Model not available yet'''		)
	def 							UpperCAmelCase__(					self						:		Dict		)->       Tuple:
			pass




	@slow
	def 							UpperCAmelCase__(					self						:		List[str]		)->       List[Any]:
			lowerCAmelCase__   :			Union[str, Any]         =					DebertaVaModel.from_pretrained('''microsoft/deberta-v2-xlarge'''		)

			lowerCAmelCase__   :			Dict         =					torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
			lowerCAmelCase__   :			Any         =					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
			with torch.no_grad():
					lowerCAmelCase__   :			Optional[Any]         =					model(a__		,					attention_mask=a__		)[0]
			# compare the actual values for a slice.
			lowerCAmelCase__   :			str         =					torch.tensor(
			    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]		)
			self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,					a__		,					atol=1E-4		)		,					F'{output[:, 1:4, 1:4]}'		)





"""simple docstring"""




import numpy as np
import qiskit





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 8			,_lowerCamelCase						:       int | None = None	)       ->      str:
  _lowerCAmelCase						:       int  			=     np.random.default_rng(seed=_lowerCamelCase	)

  # Roughly 25% of the qubits will contribute to the key.
  # So we take more than we need.
  _lowerCAmelCase						:       Tuple  			=     6 * key_len
  # Measurement basis for Alice's qubits.
  _lowerCAmelCase						:       Dict  			=     rng.integers(2			,size=_lowerCamelCase	)
  # The set of states Alice will prepare.
  _lowerCAmelCase						:       Tuple  			=     rng.integers(2			,size=_lowerCamelCase	)
  # Measurement basis for Bob's qubits.
  _lowerCAmelCase						:       Union[str, Any]  			=     rng.integers(2			,size=_lowerCamelCase	)

  # Quantum Circuit to simulate BB84
  _lowerCAmelCase						:       Dict  			=     qiskit.QuantumCircuit(_lowerCamelCase			,name="""BB84"""	)

  # Alice prepares her qubits according to rules above.
  for index, _ in enumerate(_lowerCamelCase	):
    if alice_state[index] == 1:
      bbaa_circ.x(_lowerCamelCase	)
    if alice_basis[index] == 1:
      bbaa_circ.h(_lowerCamelCase	)
  bbaa_circ.barrier()

  # Bob measures the received qubits according to rules above.
  for index, _ in enumerate(_lowerCamelCase	):
    if bob_basis[index] == 1:
      bbaa_circ.h(_lowerCamelCase	)

  bbaa_circ.barrier()
  bbaa_circ.measure_all()

  # Simulate the quantum circuit.
  _lowerCAmelCase						:       int  			=     qiskit.Aer.get_backend("""aer_simulator"""	)
  # We only need to run one shot because the key is unique.
  # Multiple shots will produce the same key.
  _lowerCAmelCase						:       List[str]  			=     qiskit.execute(_lowerCamelCase			,_lowerCamelCase			,shots=1			,seed_simulator=_lowerCamelCase	)
  # Returns the result of measurement.
  _lowerCAmelCase						:       List[Any]  			=     job.result().get_counts(_lowerCamelCase	).most_frequent()

  # Extracting the generated key from the simulation results.
  # Only keep measurement results where Alice and Bob chose the same basis.
  _lowerCAmelCase						:       str  			=     """""".join(
      [
          result_bit
          for alice_basis_bit, bob_basis_bit, result_bit in zip(
              _lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
          if alice_basis_bit == bob_basis_bit
      ]	)

  # Get final key. Pad with 0 if too short, otherwise truncate.
  _lowerCAmelCase						:       List[Any]  			=     gen_key[:key_len] if len(_lowerCamelCase	) >= key_len else gen_key.ljust(_lowerCamelCase			,"""0"""	)
  return key


if __name__ == "__main__":
     print(F"""The generated key is : {bbaa(8, seed=0)}""")
     from doctest import testmod

     testmod()




