


import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCAmelCase__  :Tuple             =							logging.get_logger(__name__)

class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	None:




									"""simple docstring"""





									warnings.warn(
									    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
									    ' Please use PoolFormerImageProcessor instead.'      ,   _SCREAMING_SNAKE_CASE      ,   )
									super().__init__(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)




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 		__a				(    UpperCAmelCase      ):
		_a     :  Union[List[np.ndarray], torch.FloatTensor]


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
