
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							lowercase						(   lowercase__						):
							lowercase_			:			Union[str, Any]			   =42
							lowercase_			:			str			   =42




							def __init__(							self  ,A__  ,A__):
												super().__init__()

												lowercase		      =		PNDMScheduler.from_config(scheduler.config)

												self.register_modules(unet=A__  ,scheduler=A__)





							@torch.no_grad()
							def __call__(							self  ,A__ = 1  ,A__ = 5_0  ,A__ = None  ,A__ = "pil"  ,A__ = True  ,**A__  ,):
												lowercase		      =		randn_tensor(
												    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  ,generator=A__  ,device=self.device  ,)

												self.scheduler.set_timesteps(A__)
												for t in self.progress_bar(self.scheduler.timesteps):
																	lowercase		      =		self.unet(A__  ,A__).sample

																	lowercase		      =		self.scheduler.step(A__  ,A__  ,A__).prev_sample

												lowercase		      =		(image / 2 + 0.5).clamp(0  ,1)
												lowercase		      =		image.cpu().permute(0  ,2  ,3  ,1).numpy()
												if output_type == "pil":
																	lowercase		      =		self.numpy_to_pil(A__)

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=A__)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase								=      logging.get_logger(__name__)

__lowerCamelCase								=      {
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}


class 	snake_case_			(lowercase__	):




       """simple docstring"""






       _lowerCamelCase           =					"""videomae"""



       def __init__(  self  ,lowercase=224  ,lowercase=16  ,lowercase=3  ,lowercase=16  ,lowercase=2  ,lowercase=768  ,lowercase=12  ,lowercase=12  ,lowercase=3072  ,lowercase="gelu"  ,lowercase=0.0  ,lowercase=0.0  ,lowercase=0.02  ,lowercase=1E-12  ,lowercase=True  ,lowercase=True  ,lowercase=6  ,lowercase=384  ,lowercase=4  ,lowercase=1536  ,lowercase=True  ,**lowercase  ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_	:						Optional[int]					   =		image_size
             UpperCAmelCase_	:						Optional[Any]					   =		patch_size
             UpperCAmelCase_	:						Tuple					   =		num_channels
             UpperCAmelCase_	:						Optional[int]					   =		num_frames
             UpperCAmelCase_	:						str					   =		tubelet_size

             UpperCAmelCase_	:						Any					   =		hidden_size
             UpperCAmelCase_	:						List[str]					   =		num_hidden_layers
             UpperCAmelCase_	:						List[str]					   =		num_attention_heads
             UpperCAmelCase_	:						Union[str, Any]					   =		intermediate_size
             UpperCAmelCase_	:						List[str]					   =		hidden_act
             UpperCAmelCase_	:						Tuple					   =		hidden_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		attention_probs_dropout_prob
             UpperCAmelCase_	:						Optional[Any]					   =		initializer_range
             UpperCAmelCase_	:						Dict					   =		layer_norm_eps
             UpperCAmelCase_	:						Tuple					   =		qkv_bias
             UpperCAmelCase_	:						int					   =		use_mean_pooling

             UpperCAmelCase_	:						List[Any]					   =		decoder_num_attention_heads
             UpperCAmelCase_	:						Optional[int]					   =		decoder_hidden_size
             UpperCAmelCase_	:						Any					   =		decoder_num_hidden_layers
             UpperCAmelCase_	:						Dict					   =		decoder_intermediate_size
             UpperCAmelCase_	:						str					   =		norm_pix_loss
