




"""simple docstring"""






from __future__ import annotations

lowercase__  		=		10







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					1
				_lowerCamelCase  :    Optional[int]					=					max(lowercase__	)
				while placement <= max_digit:
								# declare and initialize empty buckets
								_lowerCamelCase  :    list[list]					=					[[] for _ in range(lowercase__	)]
								# split list_of_ints between the buckets
								for i in list_of_ints:
												_lowerCamelCase  :    Dict					=					int((i / placement) % RADIX	)
												buckets[tmp].append(lowercase__	)
								# put each buckets' contents into list_of_ints
								_lowerCamelCase  :    str					=					0
								for b in range(lowercase__	):
												for i in buckets[b]:
																_lowerCamelCase  :    str					=					i
																a += 1
        # move to next
								placement *= RADIX
				return list_of_ints


if __name__ == "__main__":
			import doctest

			doctest.testmod()





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    42


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
			from .pipeline_text_to_video_synth import TextToVideoSDPipeline
			from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
			from .pipeline_text_to_video_zero import TextToVideoZeroPipeline