


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_					(				__SCREAMING_SNAKE_CASE :					Optional[int]	,   __SCREAMING_SNAKE_CASE :					Optional[int]	,   __SCREAMING_SNAKE_CASE :					Tuple       )     ->  List[Any]:
					__SCREAMING_SNAKE_CASE					:							Any			   = {'''small''': (5_12, 8, 24), '''medium''': (10_24, 8, 36), '''large''': (15_36, 16, 48)}
					__SCREAMING_SNAKE_CASE					:							Tuple			   = MODELS[model_size]  # set model hyperparameters
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = ImageGPTConfig(n_embd=UpperCAmelCase_	,   n_layer=UpperCAmelCase_	,   n_head=UpperCAmelCase_       )
					__SCREAMING_SNAKE_CASE					:							int			   = ImageGPTForCausalLM(UpperCAmelCase_       )

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

					# Save pytorch-model
					__SCREAMING_SNAKE_CASE					:							Tuple			   = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					__SCREAMING_SNAKE_CASE					:							str			   = 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__":
						_A      =     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."""
						)
						_A      =     parser.parse_args()
						convert_imagegpt_checkpoint_to_pytorch(
						    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
						)






'''simple docstring'''






import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      Dict					=  IFSuperResolutionPipeline
			__a					:      Dict					=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
			__a					:      Tuple					=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__a					:      Tuple					=  PipelineTesterMixin.required_optional_params - {'''latents'''}




			def 			_snake_case   (      self       )			->						Dict:


								'''simple docstring'''






								return self._get_superresolution_dummy_components()




			def 			_snake_case   (      self					,      lowercase					,      lowercase=0       )			->						Union[str, Any]:


								'''simple docstring'''






								if str(lowercase       ).startswith('''mps'''       ):
													__SCREAMING_SNAKE_CASE					:							Optional[int]			   = torch.manual_seed(lowercase       )
								else:
													__SCREAMING_SNAKE_CASE					:							int			   = torch.Generator(device=lowercase       ).manual_seed(lowercase       )

								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = floats_tensor((1, 3, 3_2, 3_2)					,      rng=random.Random(lowercase       )       ).to(lowercase       )

								__SCREAMING_SNAKE_CASE					:							List[str]			   = {
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}

								return inputs




			@unittest.skipIf(
			    torch_device != '''cuda''' or not is_xformers_available()					,      reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,      )
			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								self._test_save_load_optional_components()




			@unittest.skipIf(torch_device != '''cuda'''					,      reason='''float16 requires CUDA'''       )
			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






								super().test_save_load_floataa(expected_max_diff=1e-1       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )




			def 			_snake_case   (      self       )			->						Optional[int]:


								'''simple docstring'''






								self._test_save_load_local()




			def 			_snake_case   (      self       )			->						List[str]:


								'''simple docstring'''






								self._test_inference_batch_single_identical(
								    expected_max_diff=1e-2					,      )


