
'''simple docstring'''





a__						:				Optional[Any]           =    {
    "0000": "0",
    "0001": "1",
    "0010": "2",
    "0011": "3",
    "0100": "4",
    "0101": "5",
    "0110": "6",
    "0111": "7",
    "1000": "8",
    "1001": "9",
    "1010": "a",
    "1011": "b",
    "1100": "c",
    "1101": "d",
    "1110": "e",
    "1111": "f",
}





def     snake_case							(						UpperCAmelCase		)->     str:







      """simple docstring"""
      __A    		=     str(UpperCAmelCase		).strip()

      # Exceptions
      if not binary_str:
            raise ValueError('Empty string was passed to the function'		)
      __A    		=     binary_str[0] == '-'
      __A    		=     binary_str[1:] if is_negative else binary_str
      if not all(char in '01' for char in binary_str		):
            raise ValueError('Non-binary value was passed to the function'		)

      __A    		=     (
          '0' * (4 * (divmod(len(UpperCAmelCase		) ,					4		)[0] + 1) - len(UpperCAmelCase		)) + binary_str
      )

      __A    		=     []
      for x in range(0 ,					len(UpperCAmelCase		) ,					4		):
            hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]		)
      __A    		=     '0x' + ''.join(UpperCAmelCase		)

      return "-" + hexadecimal_str if is_negative else hexadecimal_str


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

      testmod()








'''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 		lowercase_  (     self					:Optional[int]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     'runwayml/stable-diffusion-v1-5'
       __A    		=     TextToVideoZeroPipeline.from_pretrained(_A		,			torch_dtype=torch.floataa     ).to('cuda'     )
       __A    		=     DDIMScheduler.from_config(pipe.scheduler.config     )
       __A    		=     torch.Generator(device='cuda'     ).manual_seed(0     )

       __A    		=     'A bear is playing a guitar on Times Square'
       __A    		=     pipe(prompt=_A		,			generator=_A     ).images

       __A    		=     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(_A		,			_A     )






