





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(	__snake_case ,   __snake_case ,   unittest.TestCase  ):
     '''simple docstring'''


     __magic_name__        = IFImgaImgPipeline
     __magic_name__        = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     __magic_name__        = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __magic_name__        = PipelineTesterMixin.required_optional_params - {'latents'}




     def 					lowerCAmelCase__		(						self       ):
         return self._get_dummy_components()




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_=0       ):
         if str(snake_case_       ).startswith('mps'       ):
             _A    =       torch.manual_seed(snake_case_       )
         else:
             _A    =       torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )

         _A    =       floats_tensor((1, 3, 32, 32)							,    rng=random.Random(snake_case_       )       ).to(snake_case_       )

         _A    =       {
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




     def 					lowerCAmelCase__		(						self       ):
         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 					lowerCAmelCase__		(						self       ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )




     @unittest.skipIf(torch_device != 'cuda'							,    reason='float16 requires CUDA'       )
     def 					lowerCAmelCase__		(						self       ):
         # 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 					lowerCAmelCase__		(						self       ):
         super().test_floataa_inference(expected_max_diff=1E-1       )




     def 					lowerCAmelCase__		(						self       ):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )




     def 					lowerCAmelCase__		(						self       ):
         self._test_save_load_local()




     def 					lowerCAmelCase__		(						self       ):
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2							,    )












import numpy as np





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       abs(predict - actual							)
    _A    =       difference.mean()

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)

    _A    =       square_diff.mean()
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()
    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)
    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       np.log(predict + 1							)
    _A    =       np.log(actual + 1							)

    _A    =       log_predict - log_actual
    _A    =       np.square(_SCREAMING_SNAKE_CASE							)
    _A    =       square_diff.mean()

    _A    =       np.sqrt(_SCREAMING_SNAKE_CASE							)

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    _A    =       np.array(_SCREAMING_SNAKE_CASE							)
    _A    =       np.array(_SCREAMING_SNAKE_CASE							)

    _A    =       predict - actual
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    _A    =       np.sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)
    # print(numerator, denumerator)
    _A    =       float(_SCREAMING_SNAKE_CASE							) / denumerator * 100

    return score





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Dict:




    """simple docstring"""




    return np.mean(np.array(_SCREAMING_SNAKE_CASE							) == np.array(_SCREAMING_SNAKE_CASE							)							)





