

'''simple docstring'''





import unittest

from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)
class        A  :





						def __init__(	self	,    snake_case_	,    snake_case_=1_3	,    snake_case_=7	,    snake_case_=True	,    snake_case_=True	,    snake_case_=True	,    snake_case_=9_9	,    snake_case_=3_2	,    snake_case_=5	,    snake_case_=4	,    snake_case_=3_7	,    snake_case_="gelu"	,    snake_case_=0.1	,    snake_case_=0.1	,    snake_case_=5_1_2	,    snake_case_=1_6	,    snake_case_=2	,    snake_case_=0.02	,    snake_case_=3	,    snake_case_=4	,    snake_case_=None	,    )			->							List[Any]:
										_a        =   parent
										_a        =   batch_size
										_a        =   seq_length
										_a        =   is_training
										_a        =   use_token_type_ids
										_a        =   use_labels
										_a        =   vocab_size
										_a        =   hidden_size
										_a        =   num_hidden_layers
										_a        =   num_attention_heads
										_a        =   intermediate_size
										_a        =   hidden_act
										_a        =   hidden_dropout_prob
										_a        =   attention_probs_dropout_prob
										_a        =   max_position_embeddings
										_a        =   type_vocab_size
										_a        =   type_sequence_label_size
										_a        =   initializer_range
										_a        =   num_labels
										_a        =   num_choices
										_a        =   scope
										_a        =   self.vocab_size - 1





						def __lowerCAmelCase   (	self			)			->							List[str]:
										_a        =   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size			)

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

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

										_a        =   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	,    )

										_a        =   ids_tensor([self.num_hidden_layers, self.num_attention_heads]	,    2			)

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





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    *snake_case_			)			->							Optional[Any]:
										_a        =   OpenAIGPTModel(config=snake_case_			)
										model.to(snake_case_			)
										model.eval()

										_a        =   model(snake_case_	,    token_type_ids=snake_case_	,    head_mask=snake_case_			)
										_a        =   model(snake_case_	,    token_type_ids=snake_case_			)
										_a        =   model(snake_case_			)

										self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    *snake_case_			)			->							Dict:
										_a        =   OpenAIGPTLMHeadModel(snake_case_			)
										model.to(snake_case_			)
										model.eval()

										_a        =   model(snake_case_	,    token_type_ids=snake_case_	,    labels=snake_case_			)
										self.parent.assertEqual(result.loss.shape	,    ()			)
										self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    *snake_case_			)			->							Union[str, Any]:
										_a        =   OpenAIGPTDoubleHeadsModel(snake_case_			)
										model.to(snake_case_			)
										model.eval()

										_a        =   model(snake_case_	,    token_type_ids=snake_case_	,    labels=snake_case_			)
										self.parent.assertEqual(result.loss.shape	,    ()			)
										self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)			)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    *snake_case_			)			->							Optional[int]:
										_a        =   self.num_labels
										_a        =   OpenAIGPTForSequenceClassification(snake_case_			)
										model.to(snake_case_			)
										model.eval()

										_a        =   ids_tensor([self.batch_size]	,    self.type_sequence_label_size			)
										_a        =   model(snake_case_	,    token_type_ids=snake_case_	,    labels=snake_case_			)
										self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)			)





						def __lowerCAmelCase   (	self			)			->							Any:
										_a        =   self.prepare_config_and_inputs()
										(
										    _a					
										)        =   config_and_inputs
										_a        =   {
										    'input_ids': input_ids,
										    'token_type_ids': token_type_ids,
										    'head_mask': head_mask,
										}

										return config, inputs_dict



@require_torch
class        A  (	a  ,    a  ,    a  ,    unittest.TestCase ):
						__UpperCAmelCase				:	Tuple   =   (
						    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
						    if is_torch_available()
						    else ()
						)
						__UpperCAmelCase				:	Union[str, Any]   =   (
						    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
						)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
						__UpperCAmelCase				:	Optional[int]   =   (
						    {
						        """feature-extraction""": OpenAIGPTModel,
						        """text-classification""": OpenAIGPTForSequenceClassification,
						        """text-generation""": OpenAIGPTLMHeadModel,
						        """zero-shot""": OpenAIGPTForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)





						def __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_			)			->							List[str]:
										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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_=False			)			->							Any:
										_a        =   super()._prepare_for_class(snake_case_	,    snake_case_	,    return_labels=snake_case_			)

										if return_labels:
														if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																		_a        =   torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)	,    dtype=torch.long	,    device=snake_case_	,    )
																		_a        =   inputs_dict['labels']
																		_a        =   inputs_dict['labels']
																		_a        =   torch.zeros(
																		    (self.model_tester.batch_size, self.model_tester.num_choices)	,    dtype=torch.long	,    device=snake_case_	,    )
																		_a        =   torch.zeros(
																		    self.model_tester.batch_size	,    dtype=torch.long	,    device=snake_case_			)
										return inputs_dict





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   OpenAIGPTModelTester(self			)
										_a        =   ConfigTester(self	,    config_class=snake_case_	,    n_embd=3_7			)





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										self.config_tester.run_common_tests()





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*snake_case_			)





						def __lowerCAmelCase   (	self			)			->							str:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*snake_case_			)





						def __lowerCAmelCase   (	self			)			->							int:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*snake_case_			)





						def __lowerCAmelCase   (	self			)			->							Any:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*snake_case_			)





						@slow
						def __lowerCAmelCase   (	self			)			->							Tuple:
										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_a        =   OpenAIGPTModel.from_pretrained(snake_case_			)
														self.assertIsNotNone(snake_case_			)



@require_torch
class        A  (	unittest.TestCase ):





						@slow
						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"			)
										model.to(snake_case_			)
										_a        =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]	,    dtype=torch.long	,    device=snake_case_			)  # the president is
										_a        =   [
										    4_8_1,
										    4_7_3_5,
										    5_4_4,
										    2_4_6,
										    9_6_3,
										    8_7_0,
										    7_6_2,
										    2_3_9,
										    2_4_4,
										    4_0_4_7_7,
										    2_4_4,
										    2_4_9,
										    7_1_9,
										    8_8_1,
										    4_8_7,
										    5_4_4,
										    2_4_0,
										    2_4_4,
										    6_0_3,
										    4_8_1,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										_a        =   model.generate(snake_case_	,    do_sample=snake_case_			)
										self.assertListEqual(output_ids[0].tolist()	,    snake_case_			)











"""simple docstring"""






import re







def 	_snake_case     (							lowercase__						):

				if len(re.findall('[ATCG]'				,       lowercase__						)						) != len(lowercase__						):
								raise ValueError('Invalid Strand'						)

				return dna.translate(dna.maketrans('ATCG'				,       'TAGC'						)						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()