


from typing import Dict, Optional

import numpy as np

import datasets


__lowerCAmelCase	: Dict  				='\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

__lowerCAmelCase	: List[Any]  				='\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

__lowerCAmelCase	: Optional[int]  				='\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__ = None    ,       lowercase__ = False    ,       ):
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		__SCREAMING_SNAKE_CASE						:     Dict					=		new_id

    # turn into Numpy arrays
						__SCREAMING_SNAKE_CASE						:     List[str]					=		np.array(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Any					=		np.array(lowercase__      )

						if reduce_labels:
												__SCREAMING_SNAKE_CASE						:     int					=		255
												__SCREAMING_SNAKE_CASE						:     List[str]					=		label - 1
												__SCREAMING_SNAKE_CASE						:     Any					=		255

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		label != ignore_index
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		np.not_equal(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     str					=		pred_label[mask]
						__SCREAMING_SNAKE_CASE						:     int					=		np.array(lowercase__      )[mask]

						__SCREAMING_SNAKE_CASE						:     List[Any]					=		pred_label[pred_label == label]

						__SCREAMING_SNAKE_CASE						:     str					=		np.histogram(lowercase__    ,       bins=lowercase__    ,       range=(0, num_labels - 1)      )[0]
						__SCREAMING_SNAKE_CASE						:     List[str]					=		np.histogram(lowercase__    ,       bins=lowercase__    ,       range=(0, num_labels - 1)      )[0]
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		np.histogram(lowercase__    ,       bins=lowercase__    ,       range=(0, num_labels - 1)      )[0]

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__ = None    ,       lowercase__ = False    ,       ):
						__SCREAMING_SNAKE_CASE						:     int					=		np.zeros((num_labels,)    ,       dtype=np.floataa      )
						__SCREAMING_SNAKE_CASE						:     Any					=		np.zeros((num_labels,)    ,       dtype=np.floataa      )
						__SCREAMING_SNAKE_CASE						:     str					=		np.zeros((num_labels,)    ,       dtype=np.floataa      )
						__SCREAMING_SNAKE_CASE						:     Any					=		np.zeros((num_labels,)    ,       dtype=np.floataa      )
						for result, gt_seg_map in zip(lowercase__    ,       lowercase__      ):
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		intersect_and_union(
												    lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__ = None    ,       lowercase__ = None    ,       lowercase__ = False    ,       ):
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		total_intersect_and_union(
						    lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )

						# compute metrics
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{}

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		total_area_intersect.sum() / total_area_label.sum()
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		total_area_intersect / total_area_union
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		total_area_intersect / total_area_label

						__SCREAMING_SNAKE_CASE						:     Dict					=		np.nanmean(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		np.nanmean(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		all_acc
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		iou
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		acc

						if nan_to_num is not None:
												__SCREAMING_SNAKE_CASE						:     List[str]					=		{metric: np.nan_to_num(lowercase__    ,       nan=lowercase__      ) for metric, metric_value in metrics.items()}

						return metrics
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION       )
class        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[Any]:
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
										        # 1st Seq - height dim, 2nd - width dim
										        {
										            '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''	)	)	),
										            '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''	)	)	),
										        }	)			,			reference_urls=[
										        '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
										    ]			,			)



				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:bool			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:Optional[Dict[int, int]] = None			,			lowerCAmelCase__			:bool = False			,			)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Any					=		mean_iou(
										    results=lowerCAmelCase__			,			gt_seg_maps=lowerCAmelCase__			,			num_labels=lowerCAmelCase__			,			ignore_index=lowerCAmelCase__			,			nan_to_num=lowerCAmelCase__			,			label_map=lowerCAmelCase__			,			reduce_labels=lowerCAmelCase__			,			)
										return iou_result




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        _lowercase      :






				'''simple docstring'''



				def __init__(       self			:Optional[int]			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:int=13			,			lowerCAmelCase__			:List[str]=7			,			lowerCAmelCase__			:Dict=True			,			lowerCAmelCase__			:List[str]=True			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[Any]=99			,			lowerCAmelCase__			:List[str]=32			,			lowerCAmelCase__			:Any=5			,			lowerCAmelCase__			:List[str]=4			,			lowerCAmelCase__			:int=37			,			lowerCAmelCase__			:Optional[int]="gelu"			,			lowerCAmelCase__			:str=0.1			,			lowerCAmelCase__			:str=0.1			,			lowerCAmelCase__			:Optional[Any]=512			,			lowerCAmelCase__			:Union[str, Any]=16			,			lowerCAmelCase__			:Dict=2			,			lowerCAmelCase__			:Tuple=0.02			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Tuple=4			,			lowerCAmelCase__			:int=None			,			)		->			int:
										__SCREAMING_SNAKE_CASE						:     Dict					=		parent
										__SCREAMING_SNAKE_CASE						:     Any					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		seq_length
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		is_training
										__SCREAMING_SNAKE_CASE						:     int					=		use_token_type_ids
										__SCREAMING_SNAKE_CASE						:     Any					=		use_labels
										__SCREAMING_SNAKE_CASE						:     Any					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     int					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     str					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Tuple					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Dict					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     int					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		type_vocab_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:     List[str]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Tuple					=		num_labels
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_choices
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		scope
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.vocab_size - 1


				def 		__magic_name__(       self			:Optional[Any]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										if self.use_token_type_ids:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size	)

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

										__SCREAMING_SNAKE_CASE						:     Optional[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			,			)

										__SCREAMING_SNAKE_CASE						:     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 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Any			,			*lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Any					=		OpenAIGPTModel(config=lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     Dict					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			head_mask=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		model(lowerCAmelCase__	)

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


				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Any			,			lowerCAmelCase__			:Dict			,			*lowerCAmelCase__			:List[Any]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		OpenAIGPTLMHeadModel(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			labels=lowerCAmelCase__	)
										self.parent.assertEqual(result.loss.shape			,			()	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size)	)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[Any]			,			lowerCAmelCase__			:List[str]			,			*lowerCAmelCase__			:Optional[Any]	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Any					=		OpenAIGPTDoubleHeadsModel(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

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


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:str			,			*lowerCAmelCase__			:Optional[int]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.num_labels
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		OpenAIGPTForSequenceClassification(lowerCAmelCase__	)
										model.to(lowerCAmelCase__	)
										model.eval()

										__SCREAMING_SNAKE_CASE						:     List[str]					=		ids_tensor([self.batch_size]			,			self.type_sequence_label_size	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		model(lowerCAmelCase__			,			token_type_ids=lowerCAmelCase__			,			labels=lowerCAmelCase__	)
										self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)	)



				def 		__magic_name__(       self			:Optional[Any]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     str					=		self.prepare_config_and_inputs()
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			(
										    __SCREAMING_SNAKE_CASE
										)			,			
										)						:     List[str]					=		config_and_inputs
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{
										    '''input_ids''': input_ids,
										    '''token_type_ids''': token_type_ids,
										    '''head_mask''': head_mask,
										}

										return config, inputs_dict




@require_torch
class        _lowercase      (			A__						,			A__						,			A__						,			unittest.TestCase       ):






				'''simple docstring'''



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


				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Union[str, Any]	)		->			Tuple:
										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 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:int=False	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		super()._prepare_for_class(lowerCAmelCase__			,			lowerCAmelCase__			,			return_labels=lowerCAmelCase__	)

										if return_labels:
																if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																						__SCREAMING_SNAKE_CASE						:     Any					=		torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)			,			dtype=torch.long			,			device=lowerCAmelCase__			,			)
																						__SCREAMING_SNAKE_CASE						:     Tuple					=		inputs_dict['''labels''']
																						__SCREAMING_SNAKE_CASE						:     Dict					=		inputs_dict['''labels''']
																						__SCREAMING_SNAKE_CASE						:     List[Any]					=		torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.num_choices)			,			dtype=torch.long			,			device=lowerCAmelCase__			,			)
																						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.zeros(
																						    self.model_tester.batch_size			,			dtype=torch.long			,			device=lowerCAmelCase__	)
										return inputs_dict


				def 		__magic_name__(       self			:Optional[int]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     int					=		OpenAIGPTModelTester(self	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		ConfigTester(self			,			config_class=lowerCAmelCase__			,			n_embd=37	)


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


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase__	)


				def 		__magic_name__(       self			:int	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase__	)


				def 		__magic_name__(       self			:List[str]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase__	)



				@slow
				def 		__magic_name__(       self			:Any	)		->			List[Any]:
										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:     Dict					=		OpenAIGPTModel.from_pretrained(lowerCAmelCase__	)
																self.assertIsNotNone(lowerCAmelCase__	)




@require_torch
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				@slow
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''	)
										model.to(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		torch.tensor([[481, 4_735, 544]]			,			dtype=torch.long			,			device=lowerCAmelCase__	)  # the president is
										__SCREAMING_SNAKE_CASE						:     Dict					=		[
										    481,
										    4_735,
										    544,
										    246,
										    963,
										    870,
										    762,
										    239,
										    244,
										    40_477,
										    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

										__SCREAMING_SNAKE_CASE						:     Dict					=		model.generate(lowerCAmelCase__			,			do_sample=lowerCAmelCase__	)
										self.assertListEqual(output_ids[0].tolist()			,			lowerCAmelCase__	)
