



import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     {'''small''': (512, 8, 24), '''medium''': (1024, 8, 36), '''large''': (1536, 16, 48)}
						UpperCAmelCase_							,      UpperCAmelCase_							,      UpperCAmelCase_     							=     MODELS[model_size]  # set model hyperparameters
						UpperCAmelCase_     							=     ImageGPTConfig(n_embd=__UpperCAmelCase							,					n_layer=__UpperCAmelCase							,					n_head=__UpperCAmelCase	)
						UpperCAmelCase_     							=     ImageGPTForCausalLM(__UpperCAmelCase	)

						# Load weights from numpy
						load_tf_weights_in_imagegpt(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)

						# Save pytorch-model
						UpperCAmelCase_     							=     pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
						UpperCAmelCase_     							=     pytorch_dump_folder_path + '''/''' + CONFIG_NAME
						print(f"Save PyTorch model to {pytorch_weights_dump_path}"	)
						torch.save(model.state_dict()							,					__UpperCAmelCase	)
						print(f"Save configuration file to {pytorch_config_dump_path}"	)
						with open(__UpperCAmelCase							,					'''w'''							,					encoding='''utf-8'''	) as f:
												f.write(config.to_json_string()	)


if __name__ == "__main__":
							UpperCamelCase_	       =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCamelCase_	       =   parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)










import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image







class 							a_  (		unittest.TestCase					, _snake_case				):

						def 	__a       (    self	:Union[str, Any])		->					Optional[Any]:
												UpperCAmelCase_     							=     load_tool('''image-question-answering''')
												self.tool.setup()
												UpperCAmelCase_     							=     load_tool('''image-question-answering'''   ,						remote=_lowercase)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:int)		->					Dict:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(_lowercase   ,						'''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')

						def 	__a       (    self	:Optional[int])		->					int:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')




						def 	__a       (    self	:Optional[Any])		->					List[str]:
												UpperCAmelCase_     							=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''')) / '''000000039769.png''')
												UpperCAmelCase_     							=     self.remote_tool(image=_lowercase   ,						question='''How many cats are sleeping on the couch?''')
												self.assertEqual(_lowercase   ,						'''2''')





