


"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=IFInpaintingPipeline
		lowerCamelCase__				=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
		lowerCamelCase__				=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		lowerCamelCase__				=PipelineTesterMixin.required_optional_params - {'latents'}



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self._get_dummy_components()



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=0					):





			'''simple docstring'''

			if str(a_					).startswith('''mps'''					):
				__snake_case	:						int						=				torch.manual_seed(a_					)
			else:
				__snake_case	:						Optional[Any]						=				torch.Generator(device=a_					).manual_seed(a_					)

			__snake_case	:						Any						=				floats_tensor((1, 3, 32, 32)				,       rng=random.Random(a_					)					).to(a_					)
			__snake_case	:						List[Any]						=				floats_tensor((1, 3, 32, 32)				,       rng=random.Random(a_					)					).to(a_					)

			__snake_case	:						Any						=				{
			    '''prompt''': '''A painting of a squirrel eating a burger''',
			    '''image''': image,
			    '''mask_image''': mask_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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''				,       reason='''float16 requires CUDA'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_save_load_local()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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









"""simple docstring"""
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(_snake_case     ,   _snake_case     ,   _snake_case     )

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--openai_checkpoint_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the TensorFlow checkpoint path.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--openai_config_file""",
			    default="""""",
			    type=str,
			    help=(
			        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





