



'''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				(lowercase__,	lowercase__,	unittest.TestCase      ):
			A__								=  IFSuperResolutionPipeline
			A__								=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
			A__								=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			A__								=  PipelineTesterMixin.required_optional_params - {'latents'}



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							return self._get_superresolution_dummy_components()



			def 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						List[str]=0			)      ->      List[str]:
							"""simple docstring"""





							if str(__UpperCamelCase			).startswith('''mps'''			):
											snake_case__      :   Optional[Any]      =    torch.manual_seed(__UpperCamelCase			)
							else:
											snake_case__      :   Any      =    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)

							snake_case__      :   Dict      =    floats_tensor((1, 3, 32, 32)    ,			rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)

							snake_case__      :   List[Any]      =    {
							    '''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 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



			def 				lowerCAmelCase (     self		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != '''cuda'''    ,			reason='''float16 requires CUDA'''			)
			def 				lowerCAmelCase (     self		:						str			)      ->      List[Any]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Tuple			)      ->      Optional[Any]:
							"""simple docstring"""





							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



			def 				lowerCAmelCase (     self		:						str			)      ->      Optional[int]:
							"""simple docstring"""





							self._test_save_load_local()







			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





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












"""simple docstring"""




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 							lowerCAmelCase			(   UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Any       )							->				Tuple:







  '''simple docstring'''







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

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

  # Save pytorch-model
  _a					      =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  _a					      =						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
     )


