



import unittest

from transformers import SqueezeBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SqueezeBertForMaskedLM,
						    SqueezeBertForMultipleChoice,
						    SqueezeBertForQuestionAnswering,
						    SqueezeBertForSequenceClassification,
						    SqueezeBertForTokenClassification,
						    SqueezeBertModel,
						)


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=13 ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=False ,					__UpperCAmelCase=True ,					__UpperCAmelCase=99 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=5 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=64 ,					__UpperCAmelCase="gelu" ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=None ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=1 ,					):







											"""simple docstring"""



											a__	:							Optional[int]      						= parent
											a__	:							List[Any]      						= batch_size
											a__	:							Tuple      						= seq_length
											a__	:							Union[str, Any]      						= is_training
											a__	:							Dict      						= use_input_mask
											a__	:							Optional[Any]      						= use_token_type_ids
											a__	:							str      						= use_labels
											a__	:							int      						= vocab_size
											a__	:							int      						= hidden_size
											a__	:							List[Any]      						= num_hidden_layers
											a__	:							Union[str, Any]      						= num_attention_heads
											a__	:							Optional[int]      						= intermediate_size
											a__	:							int      						= hidden_act
											a__	:							Optional[Any]      						= hidden_dropout_prob
											a__	:							Tuple      						= attention_probs_dropout_prob
											a__	:							List[Any]      						= max_position_embeddings
											a__	:							Tuple      						= type_vocab_size
											a__	:							List[Any]      						= type_sequence_label_size
											a__	:							List[Any]      						= initializer_range
											a__	:							Tuple      						= num_labels
											a__	:							str      						= num_choices
											a__	:							Tuple      						= scope
											a__	:							str      						= q_groups
											a__	:							Union[str, Any]      						= k_groups
											a__	:							List[str]      						= v_groups
											a__	:							Dict      						= post_attention_groups
											a__	:							Optional[Any]      						= intermediate_groups
											a__	:							List[Any]      						= output_groups



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size				)

											a__	:							int      						= None
											if self.use_input_mask:
															a__	:							Tuple      						= random_attention_mask([self.batch_size, self.seq_length]				)

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

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

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



							def 		_A	(							self				):







											"""simple docstring"""



											return SqueezeBertConfig(
											    embedding_size=self.hidden_size ,					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 ,					attention_probs_dropout_prob=self.hidden_dropout_prob ,					attention_dropout=self.attention_probs_dropout_prob ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					q_groups=self.q_groups ,					k_groups=self.k_groups ,					v_groups=self.v_groups ,					post_attention_groups=self.post_attention_groups ,					intermediate_groups=self.intermediate_groups ,					output_groups=self.output_groups ,					)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= SqueezeBertModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Dict      						= model(__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							int      						= model(__UpperCAmelCase				)
											self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= SqueezeBertForMaskedLM(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							str      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= SqueezeBertForQuestionAnswering(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							List[Any]      						= model(
											    __UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					start_positions=__UpperCAmelCase ,					end_positions=__UpperCAmelCase				)
											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 ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Dict      						= self.num_labels
											a__	:							Dict      						= SqueezeBertForSequenceClassification(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Dict      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.num_labels
											a__	:							List[str]      						= SqueezeBertForTokenClassification(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()

											a__	:							Optional[int]      						= model(__UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.num_labels)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.num_choices
											a__	:							Optional[int]      						= SqueezeBertForMultipleChoice(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()
											a__	:							Tuple      						= input_ids.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							Optional[int]      						= input_mask.unsqueeze(1				).expand(-1 ,					self.num_choices ,					-1				).contiguous()
											a__	:							Tuple      						= model(
											    __UpperCAmelCase ,					attention_mask=__UpperCAmelCase ,					labels=__UpperCAmelCase ,					)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_choices)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= self.prepare_config_and_inputs()
											((a__)						,   (a__)						,   (a__)						,   (a__)						,   (a__)						,   (a__))	:							Union[str, Any]      						= config_and_inputs
											a__	:							Dict      						= {"input_ids": input_ids, "attention_mask": input_mask}
											return config, inputs_dict





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:List[Any] =       (
							    (
							        SqueezeBertModel,
							        SqueezeBertForMaskedLM,
							        SqueezeBertForMultipleChoice,
							        SqueezeBertForQuestionAnswering,
							        SqueezeBertForSequenceClassification,
							        SqueezeBertForTokenClassification,
							    )
							    if is_torch_available()
							    else None
							)
							A				:Dict =       (
							    {
							        "feature-extraction": SqueezeBertModel,
							        "fill-mask": SqueezeBertForMaskedLM,
							        "question-answering": SqueezeBertForQuestionAnswering,
							        "text-classification": SqueezeBertForSequenceClassification,
							        "token-classification": SqueezeBertForTokenClassification,
							        "zero-shot": SqueezeBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							A				:Union[str, Any] =       False
							A				:Optional[int] =       True
							A				:Tuple =       False



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= SqueezeBertModelTester(self				)
											a__	:							int      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					dim=37				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.config_tester.run_common_tests()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_masked_lm(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_question_answering(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_token_classification(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__UpperCAmelCase				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__	:							Optional[int]      						= SqueezeBertModel.from_pretrained(__UpperCAmelCase				)
															self.assertIsNotNone(__UpperCAmelCase				)





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





							'''simple docstring'''

							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"				)

											a__	:							List[Any]      						= torch.tensor([[1, 2_9414, 232, 328, 740, 1140, 1_2695, 69, 13, 1588, 2]]				)
											a__	:							str      						= model(__UpperCAmelCase				)[0]
											a__	:							List[str]      						= torch.Size((1, 3)				)
											self.assertEqual(output.shape ,					__UpperCAmelCase				)
											a__	:							List[Any]      						= torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]				)
											self.assertTrue(torch.allclose(__UpperCAmelCase ,					__UpperCAmelCase ,					atol=1E-4				)				)







import qiskit






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

				# Using Aer's simulator
				a__	:							Dict      						= qiskit.Aer.get_backend("aer_simulator" )

				# Creating a Quantum Circuit acting on the q register
				a__	:							Dict      						= qiskit.QuantumCircuit(__UpperCamelCase	,				__UpperCamelCase )

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0 )

				for i in range(1	,				__UpperCamelCase ):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1	,				__UpperCamelCase )

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(__UpperCamelCase ) )	,				list(range(__UpperCamelCase ) ) )

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


if __name__ == "__main__":
						print(F'Total count for various states are: {quantum_entanglement(3)}')


