

'''simple docstring'''



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			@property
			def lowercase__						(				self	:						Any			)      ->		List[str]:





									'''simple docstring'''
									torch.manual_seed(0			)
									A__					:       Tuple     =UNetaDModel(
									    block_out_channels=(32, 64)		,      layers_per_block=2		,      sample_size=32		,      in_channels=3		,      out_channels=3		,      down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")		,      up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")		,      )
									return model



			def lowercase__						(				self	:						Union[str, Any]			)      ->		int:





									'''simple docstring'''
									A__					:       Dict     =self.dummy_uncond_unet
									A__					:       Dict     =KarrasVeScheduler()

									A__					:       List[str]     =KarrasVePipeline(unet=lowerCAmelCase_		,      scheduler=lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =torch.manual_seed(0			)
									A__					:       Optional[Any]     =pipe(num_inference_steps=2		,      generator=lowerCAmelCase_		,      output_type="""numpy"""			).images

									A__					:       List[Any]     =torch.manual_seed(0			)
									A__					:       Union[str, Any]     =pipe(num_inference_steps=2		,      generator=lowerCAmelCase_		,      output_type="""numpy"""		,      return_dict=lowerCAmelCase_			)[0]

									A__					:       Dict     =image[0, -3:, -3:, -1]
									A__					:       Optional[Any]     =image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									A__					:       Any     =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2



@slow
@require_torch
class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def lowercase__						(				self	:						Optional[Any]			)      ->		str:





									'''simple docstring'''
									A__					:       str     ="""google/ncsnpp-celebahq-256"""
									A__					:       List[str]     =UNetaDModel.from_pretrained(lowerCAmelCase_			)
									A__					:       Dict     =KarrasVeScheduler()

									A__					:       str     =KarrasVePipeline(unet=lowerCAmelCase_		,      scheduler=lowerCAmelCase_			)
									pipe.to(lowerCAmelCase_			)
									pipe.set_progress_bar_config(disable=lowerCAmelCase_			)

									A__					:       Union[str, Any]     =torch.manual_seed(0			)
									A__					:       Optional[int]     =pipe(num_inference_steps=20		,      generator=lowerCAmelCase_		,      output_type="""numpy"""			).images

									A__					:       Optional[int]     =image[0, -3:, -3:, -1]
									assert image.shape == (1, 2_56, 2_56, 3)
									A__					:       Any     =np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]			)

									assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




'''simple docstring'''



from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def 						__lowerCamelCase	(       )	->							List[Any]:






						"""simple docstring"""





						A__					:       List[Any]     =ArgumentParser("""Transformers CLI tool""",		usage="""transformers-cli <command> [<args>]"""	)
						A__					:       List[Any]     =parser.add_subparsers(help="""transformers-cli command helpers"""	)

						# Register commands
						ConvertCommand.register_subcommand(__snake_case	)
						DownloadCommand.register_subcommand(__snake_case	)
						EnvironmentCommand.register_subcommand(__snake_case	)
						RunCommand.register_subcommand(__snake_case	)
						ServeCommand.register_subcommand(__snake_case	)
						UserCommands.register_subcommand(__snake_case	)
						AddNewModelCommand.register_subcommand(__snake_case	)
						AddNewModelLikeCommand.register_subcommand(__snake_case	)
						LfsCommands.register_subcommand(__snake_case	)
						PTtoTFCommand.register_subcommand(__snake_case	)

						# Let's go
						A__					:       List[str]     =parser.parse_args()

						if not hasattr(__snake_case,		"""func"""	):
												parser.print_help()
												exit(1	)

						# Run
						A__					:       Optional[Any]     =args.func(__snake_case	)
						service.run()


if __name__ == "__main__":
		main()

