import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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    __lowerCAmelCase  (       a_,	a_,	unittest.TestCase						):



 '''simple docstring'''
 _lowerCamelCase						:	Union[str, Any]				    = IFImgaImgPipeline
 _lowerCamelCase						:	Any				    = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
 _lowerCamelCase						:	Tuple				    = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 _lowerCamelCase						:	int				    = PipelineTesterMixin.required_optional_params - {'latents'}

 def 					__snake_case					(			self       :		Optional[int]  ) ->							Dict:
        return self._get_dummy_components()

 def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Optional[int]       ,			lowerCamelCase       :		str=0  ) ->							str:
        if str(lowerCamelCase  ).startswith('mps'  ):
               _UpperCamelCase     :       str  						=  torch.manual_seed(lowerCamelCase  )
        else:
               _UpperCamelCase     :       Any  						=  torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )

        _UpperCamelCase     :       Tuple  						=  floats_tensor((1, 3, 3_2, 3_2)       ,			rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )

        _UpperCamelCase     :       List[str]  						=  {
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }

        return inputs

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Dict:
        self._test_save_load_optional_components()

 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()       ,			reason='XFormers attention is only available with CUDA and `xformers` installed'       ,			)
 def 					__snake_case					(			self       :		Any  ) ->							Union[str, Any]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )

 @unittest.skipIf(torch_device != 'cuda'       ,			reason='float16 requires CUDA'  )
 def 					__snake_case					(			self       :		int  ) ->							Dict:
        # 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  )

 @unittest.skipIf(torch_device != 'cuda'       ,			reason='float16 requires CUDA'  )
 def 					__snake_case					(			self       :		str  ) ->							List[Any]:
        super().test_floataa_inference(expected_max_diff=1e-1  )

 def 					__snake_case					(			self       :		Any  ) ->							int:
        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )

 def 					__snake_case					(			self       :		List[str]  ) ->							Any:
        self._test_save_load_local()

 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Optional[Any]:
        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2       ,			)




UpperCAmelCase					=				[chr(i) for i in range(32, 1_26)]
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(reversed(range(len(alphabets))))
UpperCAmelCase					=				[]
UpperCAmelCase					=				UpperCAmelCase					=				UpperCAmelCase					=				0


def SCREAMING_SNAKE_CASE				(     ):
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       _UpperCamelCase     :       Any  						=  gear_one[0]
       gear_one.append(lowercase_				)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(lowercase_				)				) == 0:
              _UpperCamelCase     :       str  						=  gear_two[0]
              gear_two.append(lowercase_				)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(lowercase_				)				) == 0:
                     _UpperCamelCase     :       int  						=  gear_three[0]
                     gear_three.append(lowercase_				)
                     del gear_three[0]
                     gear_three_pos += 1


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
       _UpperCamelCase     :       Dict  						=  alphabets.index(lowercase_				)
       _UpperCamelCase     :       Union[str, Any]  						=  gear_one[target]
       _UpperCamelCase     :       Any  						=  gear_two[target]
       _UpperCamelCase     :       Tuple  						=  gear_three[target]
       _UpperCamelCase     :       Tuple  						=  reflector[target]
       _UpperCamelCase     :       Any  						=  gear_three.index(lowercase_				)
       _UpperCamelCase     :       Any  						=  gear_two.index(lowercase_				)
       _UpperCamelCase     :       str  						=  gear_one.index(lowercase_				)
       code.append(alphabets[target]				)
       rotator()


if __name__ == "__main__":
 UpperCAmelCase					=				list(input('''Type your message:\n'''))
 while True:
  try:
   UpperCAmelCase					=				int(input('''Please set token:(must be only digits)\n'''))
   break
  except Exception as error:
   print(error)
 for _ in range(token):
  rotator()
 for j in decode:
  engine(j)
 print('''\n''' + ''''''.join(code))
 print(
     f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
     '''this message again you should input same digits as token!'''
 )



