



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 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = IFInpaintingPipeline
							_a			      = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
							_a			      = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_a			      = PipelineTesterMixin.required_optional_params - {'''latents'''}




							def    UpperCAmelCase__			(       self     :       Optional[int]):
									return self._get_dummy_components()




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Dict				,				A_     :       str=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_			:     Dict									= torch.manual_seed(A_)
									else:
											lowerCAmelCase_			:     int									= torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_			:     Optional[int]									= floats_tensor((1, 3, 3_2, 3_2)				,				rng=random.Random(A_)).to(A_)
									lowerCAmelCase_			:     List[str]									= floats_tensor((1, 3, 3_2, 3_2)				,				rng=random.Random(A_)).to(A_)

									lowerCAmelCase_			:     int									= {
									    '''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    UpperCAmelCase__			(       self     :       Any):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]):
									self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''				,				reason='''float16 requires CUDA''')
							def    UpperCAmelCase__			(       self     :       List[str]):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1e-1)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def    UpperCAmelCase__			(       self     :       Any):
									self._test_save_load_local()







							def    UpperCAmelCase__			(       self     :       List[str]):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2				,				)










from pathlib import Path

import fire


def 			UpperCamelCase(  __UpperCamelCase							:		str     ,__UpperCamelCase							:		str     ,__UpperCamelCase							:		int ):
		lowerCAmelCase_			:     List[str]									= Path(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= Path(__UpperCamelCase )
		dest_dir.mkdir(exist_ok=__UpperCamelCase )
		for path in src_dir.iterdir():
				lowerCAmelCase_			:     Optional[Any]									= [x.rstrip() for x in list(path.open().readlines() )][:n]
				lowerCAmelCase_			:     List[str]									= dest_dir.joinpath(path.name )
				print(__UpperCamelCase )
				dest_path.open('''w''' ).write('''\n'''.join(__UpperCamelCase ) )


if __name__ == "__main__":
						fire.Fire(minify)





