


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


A   :					Tuple	=      logging.get_logger(__name__)


if is_vision_available():
    import PIL







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["pixel_values"]





       def __init__(     self							:    str      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Union[int, float] = 1 / 255      ,   __lowerCamelCase							:    bool = True      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    bool = True      ,   **__lowerCamelCase							:    List[Any]      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )
             lowerCamelCase__	:       int					=						size if size is not None else {"shortest_edge": 224}
             lowerCamelCase__	:       Optional[Any]					=						get_size_dict(__lowerCamelCase      ,   default_to_square=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						crop_size if crop_size is not None else {"height": 224, "width": 224}
             lowerCamelCase__	:       Union[str, Any]					=						get_size_dict(__lowerCamelCase      ,   default_to_square=__lowerCamelCase      ,   param_name="crop_size" )

             lowerCamelCase__	:       List[str]					=						do_resize
             lowerCamelCase__	:       int					=						size
             lowerCamelCase__	:       Optional[int]					=						resample
             lowerCamelCase__	:       List[str]					=						do_center_crop
             lowerCamelCase__	:       Dict					=						crop_size
             lowerCamelCase__	:       List[Any]					=						do_rescale
             lowerCamelCase__	:       Optional[int]					=						rescale_factor
             lowerCamelCase__	:       Optional[Any]					=						do_normalize
             lowerCamelCase__	:       Tuple					=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             lowerCamelCase__	:       Optional[Any]					=						image_std if image_std is not None else OPENAI_CLIP_STD
             lowerCamelCase__	:       Optional[Any]					=						do_convert_rgb





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Dict[str, int]      ,   __lowerCamelCase							:    PILImageResampling = PILImageResampling.BICUBIC      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    str      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						get_size_dict(__lowerCamelCase      ,   default_to_square=__lowerCamelCase )
             if "shortest_edge" not in size:
                   raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
             lowerCamelCase__	:       Any					=						get_resize_output_image_size(__lowerCamelCase      ,   size=size["shortest_edge"]      ,   default_to_square=__lowerCamelCase )
             return resize(__lowerCamelCase      ,   size=__lowerCamelCase      ,   resample=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    List[Any]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Dict[str, int]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						get_size_dict(__lowerCamelCase )
             if "height" not in size or "width" not in size:
                   raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}" )
             return center_crop(__lowerCamelCase      ,   size=(size["height"], size["width"])      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Union[int, float]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    List[str]      ,   ):


             '''simple docstring'''



             return rescale(__lowerCamelCase      ,   scale=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    np.ndarray      ,   __lowerCamelCase							:    Union[float, List[float]]      ,   __lowerCamelCase							:    Union[float, List[float]]      ,   __lowerCamelCase							:    Optional[Union[str, ChannelDimension]] = None      ,   **__lowerCamelCase							:    Dict      ,   ):


             '''simple docstring'''



             return normalize(__lowerCamelCase      ,   mean=__lowerCamelCase      ,   std=__lowerCamelCase      ,   data_format=__lowerCamelCase      ,   **__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    ImageInput      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Dict[str, int] = None      ,   __lowerCamelCase							:    PILImageResampling = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    int = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    float = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    Optional[Union[float, List[float]]] = None      ,   __lowerCamelCase							:    bool = None      ,   __lowerCamelCase							:    Optional[Union[str, TensorType]] = None      ,   __lowerCamelCase							:    Optional[ChannelDimension] = ChannelDimension.FIRST      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						do_resize if do_resize is not None else self.do_resize
             lowerCamelCase__	:       Dict					=						size if size is not None else self.size
             lowerCamelCase__	:       str					=						get_size_dict(__lowerCamelCase      ,   param_name="size"      ,   default_to_square=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						resample if resample is not None else self.resample
             lowerCamelCase__	:       List[str]					=						do_center_crop if do_center_crop is not None else self.do_center_crop
             lowerCamelCase__	:       int					=						crop_size if crop_size is not None else self.crop_size
             lowerCamelCase__	:       List[str]					=						get_size_dict(__lowerCamelCase      ,   param_name="crop_size"      ,   default_to_square=__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						do_rescale if do_rescale is not None else self.do_rescale
             lowerCamelCase__	:       Dict					=						rescale_factor if rescale_factor is not None else self.rescale_factor
             lowerCamelCase__	:       List[str]					=						do_normalize if do_normalize is not None else self.do_normalize
             lowerCamelCase__	:       Tuple					=						image_mean if image_mean is not None else self.image_mean
             lowerCamelCase__	:       Optional[int]					=						image_std if image_std is not None else self.image_std
             lowerCamelCase__	:       Optional[Any]					=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             lowerCamelCase__	:       Optional[int]					=						make_list_of_images(__lowerCamelCase )

             if not valid_images(__lowerCamelCase ):
                   raise ValueError(
                       "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                       "torch.Tensor, tf.Tensor or jax.ndarray." )

             if do_resize and size is None:
                   raise ValueError("Size must be specified if do_resize is True." )

             if do_center_crop and crop_size is None:
                   raise ValueError("Crop size must be specified if do_center_crop is True." )

             if do_rescale and rescale_factor is None:
                   raise ValueError("Rescale factor must be specified if do_rescale is True." )

             if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError("Image mean and std must be specified if do_normalize is True." )

             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                   lowerCamelCase__	:       Union[str, Any]					=						[convert_to_rgb(__lowerCamelCase ) for image in images]

             # All transformations expect numpy arrays.
             lowerCamelCase__	:       Tuple					=						[to_numpy_array(__lowerCamelCase ) for image in images]

             if do_resize:
                   lowerCamelCase__	:       int					=						[self.resize(image=__lowerCamelCase      ,   size=__lowerCamelCase      ,   resample=__lowerCamelCase ) for image in images]

             if do_center_crop:
                   lowerCamelCase__	:       Any					=						[self.center_crop(image=__lowerCamelCase      ,   size=__lowerCamelCase ) for image in images]

             if do_rescale:
                   lowerCamelCase__	:       List[str]					=						[self.rescale(image=__lowerCamelCase      ,   scale=__lowerCamelCase ) for image in images]

             if do_normalize:
                   lowerCamelCase__	:       Any					=						[self.normalize(image=__lowerCamelCase      ,   mean=__lowerCamelCase      ,   std=__lowerCamelCase ) for image in images]

             lowerCamelCase__	:       Optional[Any]					=						[to_channel_dimension_format(__lowerCamelCase      ,   __lowerCamelCase ) for image in images]

             lowerCamelCase__	:       Dict					=						{"pixel_values": images}
             return BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase )






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							:    Dict      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Optional[int]=13      ,   __lowerCamelCase							:    List[str]=7      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Union[str, Any]=99      ,   __lowerCamelCase							:    List[Any]=32      ,   __lowerCamelCase							:    List[Any]=5      ,   __lowerCamelCase							:    Optional[Any]=4      ,   __lowerCamelCase							:    Optional[int]=37      ,   __lowerCamelCase							:    List[str]="gelu"      ,   __lowerCamelCase							:    List[str]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    List[str]=512      ,   __lowerCamelCase							:    Optional[Any]=16      ,   __lowerCamelCase							:    Optional[Any]=2      ,   __lowerCamelCase							:    str=0.0_2      ,   __lowerCamelCase							:    List[str]=3      ,   __lowerCamelCase							:    Tuple=4      ,   __lowerCamelCase							:    Optional[int]=None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						parent
             lowerCamelCase__	:       int					=						batch_size
             lowerCamelCase__	:       List[Any]					=						seq_length
             lowerCamelCase__	:       Union[str, Any]					=						is_training
             lowerCamelCase__	:       Any					=						use_token_type_ids
             lowerCamelCase__	:       Union[str, Any]					=						use_labels
             lowerCamelCase__	:       List[str]					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						hidden_size
             lowerCamelCase__	:       List[Any]					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       Any					=						intermediate_size
             lowerCamelCase__	:       str					=						hidden_act
             lowerCamelCase__	:       str					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       List[str]					=						max_position_embeddings
             lowerCamelCase__	:       Optional[int]					=						type_vocab_size
             lowerCamelCase__	:       List[Any]					=						type_sequence_label_size
             lowerCamelCase__	:       List[str]					=						initializer_range
             lowerCamelCase__	:       List[str]					=						num_labels
             lowerCamelCase__	:       List[Any]					=						num_choices
             lowerCamelCase__	:       Optional[Any]					=						scope
             lowerCamelCase__	:       List[Any]					=						self.vocab_size - 1





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

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

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

             lowerCamelCase__	:       Union[str, Any]					=						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      ,   )

             lowerCamelCase__	:       Optional[int]					=						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							:    str      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    int      ,   *__lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						OpenAIGPTModel(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       Tuple					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   head_mask=__lowerCamelCase )
             lowerCamelCase__	:       str					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						model(__lowerCamelCase )

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





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   *__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						OpenAIGPTLMHeadModel(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       List[str]					=						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 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Optional[int]      ,   *__lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						OpenAIGPTDoubleHeadsModel(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       Optional[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 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[Any]      ,   *__lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						self.num_labels
             lowerCamelCase__	:       Tuple					=						OpenAIGPTForSequenceClassification(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()

             lowerCamelCase__	:       Dict					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
             lowerCamelCase__	:       List[str]					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels) )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Any					=						config_and_inputs
             lowerCamelCase__	:       Union[str, Any]					=						{
                 "input_ids": input_ids,
                 "token_type_ids": token_type_ids,
                 "head_mask": head_mask,
             }

             return config, inputs_dict







@require_torch
class     _lowercase				(							lowercase__							,						lowercase__							,						lowercase__							,						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 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''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 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple=False ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						super()._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase      ,   return_labels=__lowerCamelCase )

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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						OpenAIGPTModelTester(self )
             lowerCamelCase__	:       Union[str, Any]					=						ConfigTester(self      ,   config_class=__lowerCamelCase      ,   n_embd=37 )





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_double_lm_head_model(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__	:       Any					=						OpenAIGPTModel.from_pretrained(__lowerCamelCase )
                   self.assertIsNotNone(__lowerCamelCase )







@require_torch
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						OpenAIGPTLMHeadModel.from_pretrained("openai-gpt" )
             model.to(__lowerCamelCase )
             lowerCamelCase__	:       int					=						torch.tensor([[481, 4735, 544]]      ,   dtype=torch.long      ,   device=__lowerCamelCase )  # the president is
             lowerCamelCase__	:       Union[str, Any]					=						[
                 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

             lowerCamelCase__	:       int					=						model.generate(__lowerCamelCase      ,   do_sample=__lowerCamelCase )
             self.assertListEqual(output_ids[0].tolist()      ,   __lowerCamelCase )


