import doctest

import projectq
from projectq.ops import H, Measure
def 			UpperCamelCase__						(    A__   )       -> int:
				snake_case__ :     Optional[Any] 							=				quantum_engine.allocate_qubit()
				H | qubit
				Measure | qubit
				return int(A__   )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				lowerCAmelCase__					:       Optional[Any]		=       projectq.MainEngine()

				# Generate a list of 10 random numbers
				lowerCAmelCase__					:       List[Any]		=       [get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print('''Random numbers''', random_numbers_list)



from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.openai.modeling_tf_openai import (
				    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFOpenAIGPTDoubleHeadsModel,
				    TFOpenAIGPTForSequenceClassification,
				    TFOpenAIGPTLMHeadModel,
				    TFOpenAIGPTModel,
				)





class 			__snake_case  :




					def __init__(       self   ,	__UpperCamelCase   ,	)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				parent
									snake_case__ :     Optional[int] 							=				13
									snake_case__ :     Tuple 							=				7
									snake_case__ :     Optional[int] 							=				True
									snake_case__ :     int 							=				True
									snake_case__ :     int 							=				True
									snake_case__ :     int 							=				True
									snake_case__ :     Union[str, Any] 							=				True
									snake_case__ :     Optional[Any] 							=				99
									snake_case__ :     Dict 							=				32
									snake_case__ :     Union[str, Any] 							=				2
									snake_case__ :     Tuple 							=				4
									snake_case__ :     List[str] 							=				37
									snake_case__ :     Optional[int] 							=				'gelu'
									snake_case__ :     List[str] 							=				0.1
									snake_case__ :     Union[str, Any] 							=				0.1
									snake_case__ :     str 							=				512
									snake_case__ :     str 							=				16
									snake_case__ :     Dict 							=				2
									snake_case__ :     List[Any] 							=				0.0_2
									snake_case__ :     Union[str, Any] 							=				3
									snake_case__ :     Optional[Any] 							=				4
									snake_case__ :     List[Any] 							=				None
									snake_case__ :     Union[str, Any] 							=				self.vocab_size - 1




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Any 							=				ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)

									snake_case__ :     List[Any] 							=				None
									if self.use_input_mask:
													snake_case__ :     str 							=				random_attention_mask([self.batch_size, self.seq_length]		)

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

									snake_case__ :     Dict 							=				None
									if self.use_mc_token_ids:
													snake_case__ :     Any 							=				ids_tensor([self.batch_size, self.num_choices]   ,	self.seq_length		)

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

									snake_case__ :     str 							=				OpenAIGPTConfig(
									    vocab_size=self.vocab_size   ,	n_embd=self.hidden_size   ,	n_layer=self.num_hidden_layers   ,	n_head=self.num_attention_heads   ,	n_positions=self.max_position_embeddings   ,	pad_token_id=self.pad_token_id   ,	)

									snake_case__ :     Union[str, Any] 							=				ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,	2		)

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	*__UpperCamelCase		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     List[Any] 							=				TFOpenAIGPTModel(config=__UpperCamelCase		)
									snake_case__ :     str 							=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
									snake_case__ :     Dict 							=				model(__UpperCamelCase		)

									snake_case__ :     Tuple 							=				[input_ids, input_mask]
									snake_case__ :     List[str] 							=				model(__UpperCamelCase		)

									snake_case__ :     Tuple 							=				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		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				TFOpenAIGPTLMHeadModel(config=__UpperCamelCase		)
									snake_case__ :     Optional[int] 							=				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
									snake_case__ :     List[str] 							=				model(__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   ,	*__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     Dict 							=				TFOpenAIGPTDoubleHeadsModel(config=__UpperCamelCase		)

									snake_case__ :     Optional[int] 							=				tf.tile(tf.expand_dims(__UpperCamelCase   ,	1		)   ,	(1, self.num_choices, 1)		)
									snake_case__ :     Tuple 							=				tf.tile(tf.expand_dims(__UpperCamelCase   ,	1		)   ,	(1, self.num_choices, 1)		)
									snake_case__ :     Any 							=				tf.tile(tf.expand_dims(__UpperCamelCase   ,	1		)   ,	(1, self.num_choices, 1)		)

									snake_case__ :     Dict 							=				{
									    'input_ids': multiple_choice_inputs_ids,
									    'mc_token_ids': mc_token_ids,
									    'attention_mask': multiple_choice_input_mask,
									    'token_type_ids': multiple_choice_token_type_ids,
									}
									snake_case__ :     Tuple 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(
									    result.logits.shape   ,	(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)		)
									self.parent.assertEqual(result.mc_logits.shape   ,	(self.batch_size, self.num_choices)		)




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	*__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     int 							=				self.num_labels
									snake_case__ :     int 							=				ids_tensor([self.batch_size]   ,	self.type_sequence_label_size		)
									snake_case__ :     str 							=				{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									    'labels': sequence_labels,
									}
									snake_case__ :     str 							=				TFOpenAIGPTForSequenceClassification(__UpperCamelCase		)
									snake_case__ :     int 							=				model(__UpperCamelCase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)		)




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				self.prepare_config_and_inputs()

									(
									    (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  (
									    snake_case__
									)							,  
									) :     Any 							=				config_and_inputs

									snake_case__ :     List[str] 							=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_tf
class 			__snake_case  (      _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       (
					    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
					    if is_tf_available()
					    else ()
					)
					__lowerCamelCase	 =       (
					    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
					)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
					__lowerCamelCase	 =       (
					    {
					        """feature-extraction""": TFOpenAIGPTModel,
					        """text-classification""": TFOpenAIGPTForSequenceClassification,
					        """text-generation""": TFOpenAIGPTLMHeadModel,
					        """zero-shot""": TFOpenAIGPTForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					__lowerCamelCase	 =       False
					__lowerCamelCase	 =       False




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
													# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
													# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
													# tiny config could not be created.
													return True

									return False




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				TFOpenAIGPTModelTester(self		)
									snake_case__ :     Any 							=				ConfigTester(self   ,	config_class=__UpperCamelCase   ,	n_embd=37		)




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									self.config_tester.run_common_tests()




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Optional[Any] 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_model(*__UpperCamelCase		)




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     str 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_lm_head(*__UpperCamelCase		)




					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_double_head(*__UpperCamelCase		)




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     str 							=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__UpperCamelCase		)




					@slow
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													snake_case__ :     Optional[Any] 							=				TFOpenAIGPTModel.from_pretrained(__UpperCamelCase		)
													self.assertIsNotNone(__UpperCamelCase		)





@require_tf
class 			__snake_case  (      unittest.TestCase     ):




					@slow
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__ :     Dict 							=				TFOpenAIGPTLMHeadModel.from_pretrained('openai-gpt'		)
									snake_case__ :     Any 							=				tf.convert_to_tensor([[481, 4735, 544]]   ,	dtype=tf.intaa		)  # the president is
									snake_case__ :     int 							=				[
									    481,
									    4735,
									    544,
									    246,
									    963,
									    870,
									    762,
									    239,
									    244,
									    40477,
									    244,
									    249,
									    719,
									    881,
									    487,
									    544,
									    240,
									    244,
									    603,
									    481,
									]  # the president is a very good man. " \n " i\'m sure he is, " said the

									snake_case__ :     int 							=				model.generate(__UpperCamelCase   ,	do_sample=__UpperCamelCase		)
									self.assertListEqual(output_ids[0].numpy().tolist()   ,	__UpperCamelCase		)


