"""simple docstring"""
import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int = 3   )   ->			qiskit.result.counts.Counts:





						"""simple docstring"""






						if isinstance(_UpperCamelCase	,     _UpperCamelCase   ):
												raise TypeError('number of qubits must be a integer.'   )
						if number_of_qubits <= 0:
												raise ValueError('number of qubits must be > 0.'   )
						if math.floor(_UpperCamelCase   ) != number_of_qubits:
												raise ValueError('number of qubits must be exact integer.'   )
						if number_of_qubits > 1_0:
												raise ValueError('number of qubits too large to simulate(>10).'   )

						snake_case        =     QuantumRegister(_UpperCamelCase	,     'qr'   )
						snake_case        =     ClassicalRegister(_UpperCamelCase	,     'cr'   )

						snake_case        =     QuantumCircuit(_UpperCamelCase	,     _UpperCamelCase   )

						snake_case        =     number_of_qubits

						for i in range(_UpperCamelCase   ):
												quantum_circuit.h(number_of_qubits - i - 1   )
												counter -= 1
												for j in range(_UpperCamelCase   ):
																		quantum_circuit.cp(np.pi / 2 ** (counter - j)	,     _UpperCamelCase	,     _UpperCamelCase   )

						for k in range(number_of_qubits // 2   ):
												quantum_circuit.swap(_UpperCamelCase	,     number_of_qubits - k - 1   )

						# measure all the qubits
						quantum_circuit.measure(_UpperCamelCase	,     _UpperCamelCase   )
						# simulate with 10000 shots
						snake_case        =     Aer.get_backend('qasm_simulator'   )
						snake_case        =     execute(_UpperCamelCase	,     _UpperCamelCase	,     shots=1_0_0_0_0   )

						return job.result().get_counts(_UpperCamelCase   )


if __name__ == "__main__":
					print(
					    f"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
					)



"""simple docstring"""
import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
					    BioGptForCausalLM,
					    BioGptForSequenceClassification,
					    BioGptForTokenClassification,
					    BioGptModel,
					    BioGptTokenizer,
					)
					from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST



class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase						,      lowerCAmelCase=13						,      lowerCAmelCase=7						,      lowerCAmelCase=True						,      lowerCAmelCase=True						,      lowerCAmelCase=False						,      lowerCAmelCase=True						,      lowerCAmelCase=99						,      lowerCAmelCase=32						,      lowerCAmelCase=5						,      lowerCAmelCase=4						,      lowerCAmelCase=37						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=0.1						,      lowerCAmelCase=0.1						,      lowerCAmelCase=5_12						,      lowerCAmelCase=16						,      lowerCAmelCase=2						,      lowerCAmelCase=0.02						,      lowerCAmelCase=3						,      lowerCAmelCase=4						,      lowerCAmelCase=None						,      ):






										"""simple docstring"""

										snake_case        =     parent
										snake_case        =     batch_size
										snake_case        =     seq_length
										snake_case        =     is_training
										snake_case        =     use_input_mask
										snake_case        =     use_token_type_ids
										snake_case        =     use_labels
										snake_case        =     vocab_size
										snake_case        =     hidden_size
										snake_case        =     num_hidden_layers
										snake_case        =     num_attention_heads
										snake_case        =     intermediate_size
										snake_case        =     hidden_act
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     max_position_embeddings
										snake_case        =     type_vocab_size
										snake_case        =     type_sequence_label_size
										snake_case        =     initializer_range
										snake_case        =     num_labels
										snake_case        =     num_choices
										snake_case        =     scope

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size      )

										snake_case        =     None
										if self.use_input_mask:
																snake_case        =     random_attention_mask([self.batch_size, self.seq_length]      )

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

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

										snake_case        =     self.get_config()

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

				def 						snake_case	(			self      ):






										"""simple docstring"""

										return BioGptConfig(
										    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						,      is_decoder=lowerCAmelCase						,      initializer_range=self.initializer_range						,      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )
										snake_case        =     model(lowerCAmelCase      )
										self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()

										# create attention mask
										snake_case        =     torch.ones(input_ids.shape						,      dtype=torch.long						,      device=lowerCAmelCase      )
										snake_case        =     self.seq_length // 2
										snake_case        =     0

										# first forward pass
										snake_case   ,snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      ).to_tuple()

										# create hypothetical next token and extent to next_input_ids
										snake_case        =     ids_tensor((self.batch_size, 1)						,      config.vocab_size      )

										# change a random masked slice from input_ids
										snake_case        =     ids_tensor((1,)						,      lowerCAmelCase      ).item() + 1
										snake_case        =     ids_tensor((self.batch_size, 1)						,      config.vocab_size      ).squeeze(-1      )
										snake_case        =     random_other_next_tokens

										# append to next input_ids and attn_mask
										snake_case        =     torch.cat([input_ids, next_tokens]						,      dim=-1      )
										snake_case        =     torch.cat(
										    [attn_mask, torch.ones((attn_mask.shape[0], 1)						,      dtype=torch.long						,      device=lowerCAmelCase      )]						,      dim=1						,      )

										# get two different outputs
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']
										snake_case        =     model(lowerCAmelCase						,      past_key_values=lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']

										# select random slice
										snake_case        =     ids_tensor((1,)						,      output_from_past.shape[-1]      ).item()
										snake_case        =     output_from_no_past[:, -1, random_slice_idx].detach()
										snake_case        =     output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase						,      lowerCAmelCase						,      atol=1E-3      )      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(config=lowerCAmelCase      ).to(lowerCAmelCase      ).eval()

										snake_case        =     torch.ones(input_ids.shape						,      dtype=torch.long						,      device=lowerCAmelCase      )

										# first forward pass
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      use_cache=lowerCAmelCase      )

										snake_case   ,snake_case        =     outputs.to_tuple()

										# create hypothetical multiple next token and extent to next_input_ids
										snake_case        =     ids_tensor((self.batch_size, 3)						,      config.vocab_size      )
										snake_case        =     ids_tensor((self.batch_size, 3)						,      2      )

										# append to next input_ids and
										snake_case        =     torch.cat([input_ids, next_tokens]						,      dim=-1      )
										snake_case        =     torch.cat([attention_mask, next_attn_mask]						,      dim=-1      )

										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase      )['last_hidden_state']
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      past_key_values=lowerCAmelCase      )[
										    'last_hidden_state'
										]

										# select random slice
										snake_case        =     ids_tensor((1,)						,      output_from_past.shape[-1]      ).item()
										snake_case        =     output_from_no_past[:, -3:, random_slice_idx].detach()
										snake_case        =     output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase						,      lowerCAmelCase						,      atol=1E-3      )      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase						,      lowerCAmelCase=False      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										if gradient_checkpointing:
																model.gradient_checkpointing_enable()

										snake_case        =     model(lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.parent.assertEqual(result.loss.shape						,      ()      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)      )
										result.loss.backward()

				def 						snake_case	(			self						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     BioGptModel(lowerCAmelCase      )
										snake_case        =     model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers      )
										for key in model.state_dict().keys():
																if "c_proj" in key and "weight" in key:
																						self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]      ) - model_std      )						,      0.0_01      )
																						self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]      ) - 0.0      )						,      0.01      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      *lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     self.num_labels
										snake_case        =     BioGptForTokenClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      token_type_ids=lowerCAmelCase      )
										self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.prepare_config_and_inputs()
										(
										    (
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,(
										    snake_case
										)   ,
										)        =     config_and_inputs
										snake_case        =     {'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict





@require_torch
class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase       ,      lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      List[Any]     					=  (
				    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
				    if is_torch_available()
				    else ()
				)
				_lowerCAmelCase       :      str     					=  (BioGptForCausalLM,) if is_torch_available() else ()
				_lowerCAmelCase       :      str     					=  (
				    {
				        """feature-extraction""": BioGptModel,
				        """text-classification""": BioGptForSequenceClassification,
				        """text-generation""": BioGptForCausalLM,
				        """token-classification""": BioGptForTokenClassification,
				        """zero-shot""": BioGptForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				_lowerCAmelCase       :      List[str]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptModelTester(self      )
										snake_case        =     ConfigTester(self						,      config_class=lowerCAmelCase						,      hidden_size=37      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																snake_case        =     type
																self.model_tester.create_and_check_model(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase						,      gradient_checkpointing=lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										model.to(lowerCAmelCase      )
										snake_case        =     BioGptTokenizer.from_pretrained('microsoft/biogpt'      )

										snake_case        =     'left'

										# Define PAD Token = EOS Token = 50256
										snake_case        =     tokenizer.eos_token
										snake_case        =     model.config.eos_token_id

										# use different length sentences to test batching
										snake_case        =     [
										    'Hello, my dog is a little',
										    'Today, I',
										]

										snake_case        =     tokenizer(lowerCAmelCase						,      return_tensors='pt'						,      padding=lowerCAmelCase      )
										snake_case        =     inputs['input_ids'].to(lowerCAmelCase      )

										snake_case        =     model.generate(
										    input_ids=lowerCAmelCase						,      attention_mask=inputs['attention_mask'].to(lowerCAmelCase      )						,      )

										snake_case        =     tokenizer(sentences[0]						,      return_tensors='pt'      ).input_ids.to(lowerCAmelCase      )
										snake_case        =     model.generate(input_ids=lowerCAmelCase      )

										snake_case        =     inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
										snake_case        =     tokenizer(sentences[1]						,      return_tensors='pt'      ).input_ids.to(lowerCAmelCase      )
										snake_case        =     model.generate(input_ids=lowerCAmelCase						,      max_length=model.config.max_length - num_paddings      )

										snake_case        =     tokenizer.batch_decode(lowerCAmelCase						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=lowerCAmelCase      )
										snake_case        =     tokenizer.decode(output_padded[0]						,      skip_special_tokens=lowerCAmelCase      )

										snake_case        =     [
										    'Hello, my dog is a little bit bigger than a little bit.',
										    'Today, I have a good idea of how to use the information',
										]
										self.assertListEqual(lowerCAmelCase						,      lowerCAmelCase      )
										self.assertListEqual(lowerCAmelCase						,      [non_padded_sentence, padded_sentence]      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case        =     BioGptModel.from_pretrained(lowerCAmelCase      )
																self.assertIsNotNone(lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     3
										snake_case        =     input_dict['input_ids']
										snake_case        =     input_ids.ne(1      ).to(lowerCAmelCase      )
										snake_case        =     ids_tensor([self.model_tester.batch_size]						,      self.model_tester.type_sequence_label_size      )
										snake_case        =     BioGptForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.assertEqual(result.logits.shape						,      (self.model_tester.batch_size, self.model_tester.num_labels)      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case   ,snake_case        =     self.model_tester.prepare_config_and_inputs_for_common()
										snake_case        =     3
										snake_case        =     'multi_label_classification'
										snake_case        =     input_dict['input_ids']
										snake_case        =     input_ids.ne(1      ).to(lowerCAmelCase      )
										snake_case        =     ids_tensor(
										    [self.model_tester.batch_size, config.num_labels]						,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
										snake_case        =     BioGptForSequenceClassification(lowerCAmelCase      )
										model.to(lowerCAmelCase      )
										model.eval()
										snake_case        =     model(lowerCAmelCase						,      attention_mask=lowerCAmelCase						,      labels=lowerCAmelCase      )
										self.assertEqual(result.logits.shape						,      (self.model_tester.batch_size, self.model_tester.num_labels)      )





@require_torch
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										snake_case        =     torch.tensor([[2, 48_05, 9, 6_56, 21]]      )
										snake_case        =     model(lowerCAmelCase      )[0]

										snake_case        =     4_23_84

										snake_case        =     torch.Size((1, 5, vocab_size)      )
										self.assertEqual(output.shape						,      lowerCAmelCase      )

										snake_case        =     torch.tensor(
										    [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]      )

										self.assertTrue(torch.allclose(output[:, :3, :3]						,      lowerCAmelCase						,      atol=1E-4      )      )

				@slow
				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     BioGptTokenizer.from_pretrained('microsoft/biogpt'      )
										snake_case        =     BioGptForCausalLM.from_pretrained('microsoft/biogpt'      )
										model.to(lowerCAmelCase      )

										torch.manual_seed(0      )
										snake_case        =     tokenizer('COVID-19 is'						,      return_tensors='pt'      ).to(lowerCAmelCase      )
										snake_case        =     model.generate(
										    **lowerCAmelCase						,      min_length=1_00						,      max_length=10_24						,      num_beams=5						,      early_stopping=lowerCAmelCase						,      )
										snake_case        =     tokenizer.decode(output_ids[0]						,      skip_special_tokens=lowerCAmelCase      )

										snake_case        =     (
										    'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
										    ' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
										    ' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
										    ' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
										    ' more than 800,000 deaths.'
										)
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )


