






"""simple docstring"""


import numpy as np


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = np.array(a__   )
						lowerCAmelCase      :Dict				  = np.array(a__   )

						lowerCAmelCase      :Optional[Any]				  = abs(predict - actual   )
						lowerCAmelCase      :Dict				  = difference.mean()

						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = np.array(a__   )
						lowerCAmelCase      :Optional[Any]				  = np.array(a__   )

						lowerCAmelCase      :List[str]				  = predict - actual
						lowerCAmelCase      :Optional[int]				  = np.square(a__   )

						lowerCAmelCase      :List[Any]				  = square_diff.mean()
						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = np.array(a__   )
						lowerCAmelCase      :List[str]				  = np.array(a__   )

						lowerCAmelCase      :List[Any]				  = predict - actual
						lowerCAmelCase      :Tuple				  = np.square(a__   )
						lowerCAmelCase      :List[str]				  = square_diff.mean()
						lowerCAmelCase      :Union[str, Any]				  = np.sqrt(a__   )
						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = np.array(a__   )
						lowerCAmelCase      :Optional[int]				  = np.array(a__   )

						lowerCAmelCase      :List[str]				  = np.log(predict + 1   )
						lowerCAmelCase      :List[Any]				  = np.log(actual + 1   )

						lowerCAmelCase      :List[str]				  = log_predict - log_actual
						lowerCAmelCase      :str				  = np.square(a__   )
						lowerCAmelCase      :Union[str, Any]				  = square_diff.mean()

						lowerCAmelCase      :int				  = np.sqrt(a__   )

						return score


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = np.array(a__   )
						lowerCAmelCase      :Any				  = np.array(a__   )

						lowerCAmelCase      :Any				  = predict - actual
						lowerCAmelCase      :Optional[Any]				  = np.sum(a__   ) / len(a__   )
						lowerCAmelCase      :Optional[int]				  = np.sum(a__   ) / len(a__   )
						# print(numerator, denumerator)
						lowerCAmelCase      :Optional[Any]				  = float(a__   ) / denumerator * 1_00

						return score







def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






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







"""simple docstring"""


import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = 'runwayml/stable-diffusion-v1-5'
											lowerCAmelCase      :Tuple				  = TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase    ,   torch_dtype=torch.floataa      ).to('cuda'      )
											lowerCAmelCase      :Union[str, Any]				  = DDIMScheduler.from_config(pipe.scheduler.config      )
											lowerCAmelCase      :str				  = torch.Generator(device='cuda'      ).manual_seed(0      )

											lowerCAmelCase      :Any				  = 'A bear is playing a guitar on Times Square'
											lowerCAmelCase      :Optional[Any]				  = pipe(prompt=UpperCAmelCase    ,   generator=UpperCAmelCase      ).images

											lowerCAmelCase      :List[str]				  = load_pt(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'      )

											assert_mean_pixel_difference(UpperCAmelCase    ,   UpperCAmelCase      )