






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 							SCREAMING_SNAKE_CASE__						:





					"""simple docstring"""






					def __init__( self					:  Dict							,		__A					:  Union[str, Any]							,		__A					:  Union[str, Any]=1_3							,		__A					:  str=7							,		__A					:  List[Any]=True							,		__A					:  Dict=True							,		__A					:  int=True							,		__A					:  int=9_9							,		__A					:  Tuple=3_2							,		__A					:  int=5							,		__A					:  str=4							,		__A					:  List[Any]=3_7							,		__A					:  Optional[Any]="gelu"							,		__A					:  Union[str, Any]=0.1							,		__A					:  Union[str, Any]=0.1							,		__A					:  List[Any]=5_1_2							,		__A					:  Optional[Any]=1_6							,		__A					:  int=2							,		__A					:  int=0.0_2							,		__A					:  Tuple=3							,		__A					:  int=4							,		__A					:  Optional[int]=None							,		):
						snake_case__			:				Union[str, Any]       =  parent
						snake_case__			:				str       =  batch_size
						snake_case__			:				Any       =  seq_length
						snake_case__			:				int       =  is_training
						snake_case__			:				Any       =  use_token_type_ids
						snake_case__			:				Optional[int]       =  use_labels
						snake_case__			:				Optional[Any]       =  vocab_size
						snake_case__			:				Tuple       =  hidden_size
						snake_case__			:				Tuple       =  num_hidden_layers
						snake_case__			:				List[str]       =  num_attention_heads
						snake_case__			:				List[Any]       =  intermediate_size
						snake_case__			:				List[Any]       =  hidden_act
						snake_case__			:				Dict       =  hidden_dropout_prob
						snake_case__			:				int       =  attention_probs_dropout_prob
						snake_case__			:				Tuple       =  max_position_embeddings
						snake_case__			:				int       =  type_vocab_size
						snake_case__			:				int       =  type_sequence_label_size
						snake_case__			:				Dict       =  initializer_range
						snake_case__			:				Tuple       =  num_labels
						snake_case__			:				int       =  num_choices
						snake_case__			:				List[Any]       =  scope
						snake_case__			:				Optional[int]       =  self.vocab_size - 1



					def 	_lowercase					( self					:  Optional[Any]	):
						snake_case__			:				int       =  ids_tensor([self.batch_size, self.seq_length]							,		self.vocab_size	)

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

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

						snake_case__			:				int       =  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__			:				List[Any]       =  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 	_lowercase					( self					:  str							,		__A					:  Optional[Any]							,		__A					:  Union[str, Any]							,		__A					:  str							,		__A					:  Tuple							,		*__A					:  str	):
						snake_case__			:				Optional[Any]       =  OpenAIGPTModel(config=__A	)
						model.to(__A	)
						model.eval()

						snake_case__			:				Tuple       =  model(__A							,		token_type_ids=__A							,		head_mask=__A	)
						snake_case__			:				Optional[int]       =  model(__A							,		token_type_ids=__A	)
						snake_case__			:				List[Any]       =  model(__A	)

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



					def 	_lowercase					( self					:  Any							,		__A					:  Any							,		__A					:  Tuple							,		__A					:  List[Any]							,		__A					:  Dict							,		*__A					:  Tuple	):
						snake_case__			:				int       =  OpenAIGPTLMHeadModel(__A	)
						model.to(__A	)
						model.eval()

						snake_case__			:				Any       =  model(__A							,		token_type_ids=__A							,		labels=__A	)
						self.parent.assertEqual(result.loss.shape							,		()	)
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size)	)



					def 	_lowercase					( self					:  int							,		__A					:  Dict							,		__A					:  int							,		__A					:  Any							,		__A					:  Dict							,		*__A					:  Optional[Any]	):
						snake_case__			:				int       =  OpenAIGPTDoubleHeadsModel(__A	)
						model.to(__A	)
						model.eval()

						snake_case__			:				int       =  model(__A							,		token_type_ids=__A							,		labels=__A	)
						self.parent.assertEqual(result.loss.shape							,		()	)
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length, self.vocab_size)	)



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[str]							,		__A					:  List[Any]							,		__A					:  Union[str, Any]							,		__A					:  Any							,		*__A					:  List[str]	):
						snake_case__			:				Any       =  self.num_labels
						snake_case__			:				Tuple       =  OpenAIGPTForSequenceClassification(__A	)
						model.to(__A	)
						model.eval()

						snake_case__			:				Optional[Any]       =  ids_tensor([self.batch_size]							,		self.type_sequence_label_size	)
						snake_case__			:				Optional[int]       =  model(__A							,		token_type_ids=__A							,		labels=__A	)
						self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels)	)






					def 	_lowercase					( self					:  int	):
						snake_case__			:				Optional[Any]       =  self.prepare_config_and_inputs()
						(
						    (
						    snake_case__
						),						(
						    snake_case__
						),						(
						    snake_case__
						),						(
						    snake_case__
						),						(
						    snake_case__
						),						(
						    snake_case__
						),						(
						    snake_case__
						),						
						)			:				Union[str, Any]       =  config_and_inputs
						snake_case__			:				Any       =  {
						    "input_ids": input_ids,
						    "token_type_ids": token_type_ids,
						    "head_mask": head_mask,
						}

						return config, inputs_dict



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		(
					    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
					    if is_torch_available()
					    else ()
					)
					a_     =		(
					    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
					)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
					a_     =		(
					    {
					        "feature-extraction": OpenAIGPTModel,
					        "text-classification": OpenAIGPTForSequenceClassification,
					        "text-generation": OpenAIGPTLMHeadModel,
					        "zero-shot": OpenAIGPTForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)



					def 	_lowercase					( self					:  int							,		__A					:  Optional[int]							,		__A					:  Dict							,		__A					:  Union[str, Any]							,		__A					:  int							,		__A					:  int	):
						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 	_lowercase					( self					:  str							,		__A					:  Tuple							,		__A					:  Tuple							,		__A					:  Dict=False	):
						snake_case__			:				Union[str, Any]       =  super()._prepare_for_class(__A							,		__A							,		return_labels=__A	)

						if return_labels:
							if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
								snake_case__			:				int       =  torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)							,		dtype=torch.long							,		device=__A							,		)
								snake_case__			:				Optional[int]       =  inputs_dict["labels"]
								snake_case__			:				Tuple       =  inputs_dict["labels"]
								snake_case__			:				Optional[int]       =  torch.zeros(
								    (self.model_tester.batch_size, self.model_tester.num_choices)							,		dtype=torch.long							,		device=__A							,		)
								snake_case__			:				Union[str, Any]       =  torch.zeros(
								    self.model_tester.batch_size							,		dtype=torch.long							,		device=__A	)
						return inputs_dict



					def 	_lowercase					( self					:  Optional[int]	):
						snake_case__			:				Optional[int]       =  OpenAIGPTModelTester(self	)
						snake_case__			:				List[Any]       =  ConfigTester(self							,		config_class=__A							,		n_embd=3_7	)



					def 	_lowercase					( self					:  Tuple	):
						self.config_tester.run_common_tests()



					def 	_lowercase					( self					:  Any	):
						snake_case__			:				List[Any]       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_model(*__A	)



					def 	_lowercase					( self					:  Dict	):
						snake_case__			:				int       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_lm_head_model(*__A	)



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				str       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_double_lm_head_model(*__A	)



					def 	_lowercase					( self					:  Union[str, Any]	):
						snake_case__			:				int       =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__A	)






					@slow
					def 	_lowercase					( self					:  List[Any]	):
						for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							snake_case__			:				int       =  OpenAIGPTModel.from_pretrained(__A	)
							self.assertIsNotNone(__A	)



@require_torch
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					@slow
					def 	_lowercase					( self					:  int	):
						snake_case__			:				List[str]       =  OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"	)
						model.to(__A	)
						snake_case__			:				List[str]       =  torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]							,		dtype=torch.long							,		device=__A	)  # the president is
						snake_case__			:				Tuple       =  [
						    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

						snake_case__			:				Any       =  model.generate(__A							,		do_sample=__A	)
						self.assertListEqual(output_ids[0].tolist()							,		__A	)











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



