



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def        a						(      _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		if "img_encoder.pos_embed" in name:
				__UpperCAmelCase    :     str													=	name.replace('''img_encoder.pos_embed''' ,   '''vision_model.embeddings.position_embeddings'''						)
		if "img_encoder.patch_embed.proj" in name:
				__UpperCAmelCase    :     Tuple													=	name.replace('''img_encoder.patch_embed.proj''' ,   '''vision_model.embeddings.patch_embeddings.projection'''						)
		if "img_encoder.patch_embed.norm" in name:
				__UpperCAmelCase    :     Optional[Any]													=	name.replace('''img_encoder.patch_embed.norm''' ,   '''vision_model.embeddings.layernorm'''						)
		if "img_encoder.layers" in name:
				__UpperCAmelCase    :     Optional[int]													=	name.replace('''img_encoder.layers''' ,   '''vision_model.encoder.stages'''						)
		if "blocks" in name and "res" not in name:
				__UpperCAmelCase    :     Union[str, Any]													=	name.replace('''blocks''' ,   '''layers'''						)
		if "attn" in name and "pre_assign" not in name:
				__UpperCAmelCase    :     int													=	name.replace('''attn''' ,   '''self_attn'''						)
		if "proj" in name and "self_attn" in name and "text" not in name:
				__UpperCAmelCase    :     Optional[Any]													=	name.replace('''proj''' ,   '''out_proj'''						)
		if "pre_assign_attn.attn.proj" in name:
				__UpperCAmelCase    :     str													=	name.replace('''pre_assign_attn.attn.proj''' ,   '''pre_assign_attn.attn.out_proj'''						)
		if "norm1" in name:
				__UpperCAmelCase    :     List[Any]													=	name.replace('''norm1''' ,   '''layer_norm1'''						)
		if "norm2" in name and "pre_assign" not in name:
				__UpperCAmelCase    :     str													=	name.replace('''norm2''' ,   '''layer_norm2'''						)
		if "img_encoder.norm" in name:
				__UpperCAmelCase    :     Optional[Any]													=	name.replace('''img_encoder.norm''' ,   '''vision_model.layernorm'''						)
		# text encoder
		if "text_encoder.token_embedding" in name:
				__UpperCAmelCase    :     List[Any]													=	name.replace('''text_encoder.token_embedding''' ,   '''text_model.embeddings.token_embedding'''						)
		if "text_encoder.positional_embedding" in name:
				__UpperCAmelCase    :     Optional[Any]													=	name.replace('''text_encoder.positional_embedding''' ,   '''text_model.embeddings.position_embedding.weight'''						)
		if "text_encoder.transformer.resblocks." in name:
				__UpperCAmelCase    :     List[Any]													=	name.replace('''text_encoder.transformer.resblocks.''' ,   '''text_model.encoder.layers.'''						)
		if "ln_1" in name:
				__UpperCAmelCase    :     Tuple													=	name.replace('''ln_1''' ,   '''layer_norm1'''						)
		if "ln_2" in name:
				__UpperCAmelCase    :     Any													=	name.replace('''ln_2''' ,   '''layer_norm2'''						)
		if "c_fc" in name:
				__UpperCAmelCase    :     Any													=	name.replace('''c_fc''' ,   '''fc1'''						)
		if "c_proj" in name:
				__UpperCAmelCase    :     Any													=	name.replace('''c_proj''' ,   '''fc2'''						)
		if "text_encoder" in name:
				__UpperCAmelCase    :     Optional[int]													=	name.replace('''text_encoder''' ,   '''text_model'''						)
		if "ln_final" in name:
				__UpperCAmelCase    :     int													=	name.replace('''ln_final''' ,   '''final_layer_norm'''						)
		# projection layers
		if "img_projector.linear_hidden." in name:
				__UpperCAmelCase    :     Union[str, Any]													=	name.replace('''img_projector.linear_hidden.''' ,   '''visual_projection.'''						)
		if "img_projector.linear_out." in name:
				__UpperCAmelCase    :     Tuple													=	name.replace('''img_projector.linear_out.''' ,   '''visual_projection.3.'''						)
		if "text_projector.linear_hidden" in name:
				__UpperCAmelCase    :     List[str]													=	name.replace('''text_projector.linear_hidden''' ,   '''text_projection'''						)
		if "text_projector.linear_out" in name:
				__UpperCAmelCase    :     Optional[Any]													=	name.replace('''text_projector.linear_out''' ,   '''text_projection.3'''						)

		return name

def        a						(      _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		for key in orig_state_dict.copy().keys():
				__UpperCAmelCase    :     str													=	orig_state_dict.pop(__A						)

				if "qkv" in key:
						# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						__UpperCAmelCase    :     Optional[int]													=	key.split('''.'''						)
						__UpperCAmelCase    :     List[str]													=	int(key_split[2]						), int(key_split[4]						)
						__UpperCAmelCase    :     str													=	config.vision_config.hidden_size
						if "weight" in key:
								__UpperCAmelCase    :     Dict													=	val[:dim, :]
								__UpperCAmelCase    :     List[Any]													=	val[dim : dim * 2, :]
								__UpperCAmelCase    :     str													=	val[-dim:, :]
						else:
								__UpperCAmelCase    :     List[str]													=	val[:dim]
								__UpperCAmelCase    :     Union[str, Any]													=	val[dim : dim * 2]
								__UpperCAmelCase    :     Any													=	val[-dim:]
				elif "in_proj" in key:
						# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
						# we need to split them up into separate matrices/vectors
						__UpperCAmelCase    :     Optional[int]													=	key.split('''.'''						)
						__UpperCAmelCase    :     Optional[Any]													=	int(key_split[3]						)
						__UpperCAmelCase    :     List[str]													=	config.text_config.hidden_size
						if "weight" in key:
								__UpperCAmelCase    :     int													=	val[:dim, :]
								__UpperCAmelCase    :     str													=	val[
								    dim : dim * 2, :
								]
								__UpperCAmelCase    :     List[Any]													=	val[-dim:, :]
						else:
								__UpperCAmelCase    :     str													=	val[:dim]
								__UpperCAmelCase    :     Union[str, Any]													=	val[dim : dim * 2]
								__UpperCAmelCase    :     Dict													=	val[-dim:]
				else:
						__UpperCAmelCase    :     List[Any]													=	rename_key(__A						)
						# squeeze if necessary
						if (
						    "text_projection.0" in new_name
						    or "text_projection.3" in new_name
						    or "visual_projection.0" in new_name
						    or "visual_projection.3" in new_name
						):
								__UpperCAmelCase    :     Tuple													=	val.squeeze_()
						else:
								__UpperCAmelCase    :     List[str]													=	val

		return orig_state_dict

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	'http://images.cocodataset.org/val2017/000000039769.jpg'
		__UpperCAmelCase    :     List[str]													=	Image.open(requests.get(__A ,   stream=__A						).raw						)
		return im

@torch.no_grad()
def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					Union[str, Any]="groupvit-gcc-yfcc" ,   _UpperCAmelCase							:					Optional[Any]=False						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	GroupViTConfig()
		__UpperCAmelCase    :     Union[str, Any]													=	GroupViTModel(__A						).eval()

		__UpperCAmelCase    :     List[str]													=	torch.load(__A ,   map_location='''cpu'''						)['model']
		__UpperCAmelCase    :     Dict													=	convert_state_dict(__A ,   __A						)
		__UpperCAmelCase    :     Tuple													=	model.load_state_dict(__A ,   strict=__A						)
		assert missing_keys == ["text_model.embeddings.position_ids"]
		assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__A						) == 0)

		# verify result
		__UpperCAmelCase    :     List[Any]													=	CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''						)
		__UpperCAmelCase    :     Optional[int]													=	prepare_img()
		__UpperCAmelCase    :     Tuple													=	processor(text=['''a photo of a cat''', '''a photo of a dog'''] ,   images=__A ,   padding=__A ,   return_tensors='''pt'''						)

		with torch.no_grad():
				__UpperCAmelCase    :     List[str]													=	model(**__A						)

		if model_name == "groupvit-gcc-yfcc":
				__UpperCAmelCase    :     Any													=	torch.tensor([[13.35_23, 6.36_29]]						)
		elif model_name == "groupvit-gcc-redcaps":
				__UpperCAmelCase    :     Optional[int]													=	torch.tensor([[16.18_73, 8.62_30]]						)
		else:
				raise ValueError(f'Model name {model_name} not supported.'						)
		assert torch.allclose(outputs.logits_per_image ,   __A ,   atol=1e-3						)

		processor.save_pretrained(__A						)
		model.save_pretrained(__A						)
		print('''Successfully saved processor and model to''' ,   __A						)

		if push_to_hub:
				print('''Pushing to the hub...'''						)
				processor.push_to_hub(__A ,   organization='''nielsr'''						)
				model.push_to_hub(__A ,   organization='''nielsr'''						)


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
				)
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
				parser.add_argument(
				    "--model_name",
				    default="groupvit-gccy-fcc",
				    type=str,
				    help="Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'",
				)
				parser.add_argument(
				    "--push_to_hub",
				    action="store_true",
				    help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
				)
				__A   			=parser.parse_args()

				convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)









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

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

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

             a_					:		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     ,							)

             a_					:		List[str]							=  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        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							*SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Union[str, Any]:
             a_					:		Dict							=  OpenAIGPTModel(config=SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		str							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							head_mask=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  model(SCREAMING_SNAKE_CASE__     )

             self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							*SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			Any:
             a_					:		str							=  OpenAIGPTLMHeadModel(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		Optional[int]							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.loss.shape     ,							()     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							*SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Dict:
             a_					:		int							=  OpenAIGPTDoubleHeadsModel(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		str							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.loss.shape     ,							()     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							*SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Any							=  self.num_labels
             a_					:		Dict							=  OpenAIGPTForSequenceClassification(SCREAMING_SNAKE_CASE__     )
             model.to(SCREAMING_SNAKE_CASE__     )
             model.eval()

             a_					:		Dict							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )
             a_					:		Any							=  model(SCREAMING_SNAKE_CASE__     ,							token_type_ids=SCREAMING_SNAKE_CASE__     ,							labels=SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             a_					:		Optional[Any]							=  self.prepare_config_and_inputs()
             (
                 (
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		(
                 a_
             )							,		
             )					:		Optional[Any]							=  config_and_inputs
             a_					:		Optional[int]							=  {
                 'input_ids': input_ids,
                 'token_type_ids': token_type_ids,
                 'head_mask': head_mask,
             }

             return config, inputs_dict
@require_torch
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Tuple        =						(
          (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
          if is_torch_available()
          else ()
      )
      snake_case__				:     List[str]        =						(
          (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
      snake_case__				:     Dict        =						(
          {
              '''feature-extraction''': OpenAIGPTModel,
              '''text-classification''': OpenAIGPTForSequenceClassification,
              '''text-generation''': OpenAIGPTLMHeadModel,
              '''zero-shot''': OpenAIGPTForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[str]     )		->			Dict:
             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        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any=False     )		->			List[str]:
             a_					:		str							=  super()._prepare_for_class(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							return_labels=SCREAMING_SNAKE_CASE__     )

             if return_labels:
                    if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                           a_					:		Optional[Any]							=  torch.zeros(
                               (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     ,							)
                           a_					:		str							=  inputs_dict['labels']
                           a_					:		Optional[int]							=  inputs_dict['labels']
                           a_					:		Optional[int]							=  torch.zeros(
                               (self.model_tester.batch_size, self.model_tester.num_choices)     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     ,							)
                           a_					:		Union[str, Any]							=  torch.zeros(
                               self.model_tester.batch_size     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     )
             return inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[Any]:
             a_					:		str							=  OpenAIGPTModelTester(self     )
             a_					:		int							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     ,							n_embd=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Tuple:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Tuple:
             a_					:		Dict							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             a_					:		Any							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_lm_head_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Optional[Any]:
             a_					:		Optional[int]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_double_lm_head_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             a_					:		int							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*SCREAMING_SNAKE_CASE__     )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			str:
             for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		str							=  OpenAIGPTModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )
@require_torch
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             a_					:		Dict							=  OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'     )
             model.to(SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]     ,							dtype=torch.long     ,							device=SCREAMING_SNAKE_CASE__     )  # the president is
             a_					:		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

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


