




'''simple docstring'''


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    HfArgumentParser(UpperCamelCase				)
							_a     					=    parser.parse_args_into_dataclasses()[0]
							_a     					=    TensorFlowBenchmark(args=UpperCamelCase				)
							try:
														_a     					=    parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														_a     					=    '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
														_a     					=    ''' '''.join(str(UpperCamelCase				).split(''' '''				)[:-1]				)
														_a     					=    ''''''
														_a     					=    eval(str(UpperCamelCase				).split(''' '''				)[-1]				)
														_a     					=    []
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in TensorFlowBenchmark.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]				)
																					else:
																												wrong_args.append(UpperCamelCase				)
														if len(UpperCamelCase				) > 0:
																					_a     					=    full_error_msg + begin_error_msg + str(UpperCamelCase				)
														raise ValueError(UpperCamelCase				)
							benchmark.run()


if __name__ == "__main__":
				main()







'''simple docstring'''


from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    42


if is_transformers_available() and is_torch_available():
				from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline

