




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				IFImgaImgSuperResolutionPipeline
    snake_case__ =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
    snake_case__ =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}   )
    snake_case__ =				PipelineTesterMixin.required_optional_params - {"latents"}


    def        a				(	self					:					Optional[Any]       )				->	Union[str, Any]:
         return self._get_superresolution_dummy_components()


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Dict=0       )				->	List[Any]:
         if str(SCREAMING_SNAKE_CASE__       ).startswith("mps"       ):
              lowerCAmelCase__          =			torch.manual_seed(SCREAMING_SNAKE_CASE__       )
         else:
              lowerCAmelCase__          =			torch.Generator(device=SCREAMING_SNAKE_CASE__       ).manual_seed(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			floats_tensor((1, 3, 32, 32)       ,     rng=random.Random(SCREAMING_SNAKE_CASE__       )       ).to(SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			floats_tensor((1, 3, 16, 16)       ,     rng=random.Random(SCREAMING_SNAKE_CASE__       )       ).to(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			{
             "prompt": "A painting of a squirrel eating a burger",
             "image": image,
             "original_image": original_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        a				(	self					:					Tuple       )				->	List[str]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )


    def        a				(	self					:					int       )				->	int:
         self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != "cuda"       ,     reason="float16 requires CUDA"       )
    def        a				(	self					:					int       )				->	Tuple:
         # 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        a				(	self					:					Any       )				->	Any:
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )


    def        a				(	self					:					Tuple       )				->	int:
         self._test_save_load_local()


    def        a				(	self					:					Tuple       )				->	Optional[int]:
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2       ,     )












from math import pi, sqrt



def 							_A       (						lowerCAmelCase_					: float      ):
     """simple docstring"""

     if num <= 0:
          raise ValueError("math domain error"      )
     if num > 171.5:
          raise OverflowError("math range error"      )
     elif num - int(lowerCAmelCase_      ) not in (0, 0.5):
          raise NotImplementedError("num must be an integer or a half-integer"      )
     elif num == 0.5:
          return sqrt(lowerCAmelCase_      )
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1      )



def 							_A       (						):
     """simple docstring"""

     assert gamma(0.5      ) == sqrt(lowerCAmelCase_      )
     assert gamma(1      ) == 1.0
     assert gamma(2      ) == 1.0


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCamelCase								=					1.0
     while num:
          UpperCamelCase								=					float(input('Gamma of: '))
          print(F"""gamma({num}) = {gamma(num)}""")
          print('\nEnter 0 to exit...')
