"""simple docstring"""




from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  torch.FloatTensor






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase=3						,__UpperCAmelCase=3						,__UpperCAmelCase=("DownEncoderBlock2D",)						,__UpperCAmelCase=(64,)						,__UpperCAmelCase=2						,__UpperCAmelCase=32						,__UpperCAmelCase="silu"						,__UpperCAmelCase=True						,)    -> Union[str, Any]:
													super().__init__()
													A__		   =							layers_per_block

													A__		   =							torch.nn.Convad(
													    __UpperCAmelCase						,block_out_channels[0]						,kernel_size=3						,stride=1						,padding=1						,)

													A__		   =							None
													A__		   =							nn.ModuleList([]   )

													# down
													A__		   =							block_out_channels[0]
													for i, down_block_type in enumerate(__UpperCAmelCase   ):
																				A__		   =							output_channel
																				A__		   =							block_out_channels[i]
																				A__		   =							i == len(__UpperCAmelCase   ) - 1

																				A__		   =							get_down_block(
																				    __UpperCAmelCase						,num_layers=self.layers_per_block						,in_channels=__UpperCAmelCase						,out_channels=__UpperCAmelCase						,add_downsample=not is_final_block						,resnet_eps=1e-6						,downsample_padding=0						,resnet_act_fn=__UpperCAmelCase						,resnet_groups=__UpperCAmelCase						,attention_head_dim=__UpperCAmelCase						,temb_channels=__UpperCAmelCase						,)
																				self.down_blocks.append(__UpperCAmelCase   )

													# mid
													A__		   =							UNetMidBlockaD(
													    in_channels=block_out_channels[-1]						,resnet_eps=1e-6						,resnet_act_fn=__UpperCAmelCase						,output_scale_factor=1						,resnet_time_scale_shift='default'						,attention_head_dim=block_out_channels[-1]						,resnet_groups=__UpperCAmelCase						,temb_channels=__UpperCAmelCase						,)

													# out
													A__		   =							nn.GroupNorm(num_channels=block_out_channels[-1]						,num_groups=__UpperCAmelCase						,eps=1e-6   )
													A__		   =							nn.SiLU()

													A__		   =							2 * out_channels if double_z else out_channels
													A__		   =							nn.Convad(block_out_channels[-1]						,__UpperCAmelCase						,3						,padding=1   )

													A__		   =							False





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[int]:
													A__		   =							x
													A__		   =							self.conv_in(__UpperCAmelCase   )

													if self.training and self.gradient_checkpointing:

																				def create_custom_forward(__UpperCAmelCase   ):
																											def custom_forward(*__UpperCAmelCase   ):
																																		return module(*__UpperCAmelCase   )

																											return custom_forward

																				# down
																				if is_torch_version('>='						,'1.11.0'   ):
																											for down_block in self.down_blocks:
																																		A__		   =							torch.utils.checkpoint.checkpoint(
																																		    create_custom_forward(__UpperCAmelCase   )						,__UpperCAmelCase						,use_reentrant=__UpperCAmelCase   )
																											# middle
																											A__		   =							torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block   )						,__UpperCAmelCase						,use_reentrant=__UpperCAmelCase   )
																				else:
																											for down_block in self.down_blocks:
																																		A__		   =							torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase   )						,__UpperCAmelCase   )
																											# middle
																											A__		   =							torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block   )						,__UpperCAmelCase   )

													else:
																				# down
																				for down_block in self.down_blocks:
																											A__		   =							down_block(__UpperCAmelCase   )

																				# middle
																				A__		   =							self.mid_block(__UpperCAmelCase   )

													# post-process
													A__		   =							self.conv_norm_out(__UpperCAmelCase   )
													A__		   =							self.conv_act(__UpperCAmelCase   )
													A__		   =							self.conv_out(__UpperCAmelCase   )

													return sample






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase=3						,__UpperCAmelCase=3						,__UpperCAmelCase=("UpDecoderBlock2D",)						,__UpperCAmelCase=(64,)						,__UpperCAmelCase=2						,__UpperCAmelCase=32						,__UpperCAmelCase="silu"						,__UpperCAmelCase="group"						,)    -> Any:
													super().__init__()
													A__		   =							layers_per_block

													A__		   =							nn.Convad(
													    __UpperCAmelCase						,block_out_channels[-1]						,kernel_size=3						,stride=1						,padding=1						,)

													A__		   =							None
													A__		   =							nn.ModuleList([]   )

													A__		   =							in_channels if norm_type == 'spatial' else None

													# mid
													A__		   =							UNetMidBlockaD(
													    in_channels=block_out_channels[-1]						,resnet_eps=1e-6						,resnet_act_fn=__UpperCAmelCase						,output_scale_factor=1						,resnet_time_scale_shift='default' if norm_type == 'group' else norm_type						,attention_head_dim=block_out_channels[-1]						,resnet_groups=__UpperCAmelCase						,temb_channels=__UpperCAmelCase						,)

													# up
													A__		   =							list(reversed(__UpperCAmelCase   )   )
													A__		   =							reversed_block_out_channels[0]
													for i, up_block_type in enumerate(__UpperCAmelCase   ):
																				A__		   =							output_channel
																				A__		   =							reversed_block_out_channels[i]

																				A__		   =							i == len(__UpperCAmelCase   ) - 1

																				A__		   =							get_up_block(
																				    __UpperCAmelCase						,num_layers=self.layers_per_block + 1						,in_channels=__UpperCAmelCase						,out_channels=__UpperCAmelCase						,prev_output_channel=__UpperCAmelCase						,add_upsample=not is_final_block						,resnet_eps=1e-6						,resnet_act_fn=__UpperCAmelCase						,resnet_groups=__UpperCAmelCase						,attention_head_dim=__UpperCAmelCase						,temb_channels=__UpperCAmelCase						,resnet_time_scale_shift=__UpperCAmelCase						,)
																				self.up_blocks.append(__UpperCAmelCase   )
																				A__		   =							output_channel

													# out
													if norm_type == "spatial":
																				A__		   =							SpatialNorm(block_out_channels[0]						,__UpperCAmelCase   )
													else:
																				A__		   =							nn.GroupNorm(num_channels=block_out_channels[0]						,num_groups=__UpperCAmelCase						,eps=1e-6   )
													A__		   =							nn.SiLU()
													A__		   =							nn.Convad(block_out_channels[0]						,__UpperCAmelCase						,3						,padding=1   )

													A__		   =							False





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=None   )    -> Dict:
													A__		   =							z
													A__		   =							self.conv_in(__UpperCAmelCase   )

													A__		   =							next(iter(self.up_blocks.parameters()   )   ).dtype
													if self.training and self.gradient_checkpointing:

																				def create_custom_forward(__UpperCAmelCase   ):
																											def custom_forward(*__UpperCAmelCase   ):
																																		return module(*__UpperCAmelCase   )

																											return custom_forward

																				if is_torch_version('>='						,'1.11.0'   ):
																											# middle
																											A__		   =							torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block   )						,__UpperCAmelCase						,__UpperCAmelCase						,use_reentrant=__UpperCAmelCase   )
																											A__		   =							sample.to(__UpperCAmelCase   )

																											# up
																											for up_block in self.up_blocks:
																																		A__		   =							torch.utils.checkpoint.checkpoint(
																																		    create_custom_forward(__UpperCAmelCase   )						,__UpperCAmelCase						,__UpperCAmelCase						,use_reentrant=__UpperCAmelCase   )
																				else:
																											# middle
																											A__		   =							torch.utils.checkpoint.checkpoint(
																											    create_custom_forward(self.mid_block   )						,__UpperCAmelCase						,__UpperCAmelCase   )
																											A__		   =							sample.to(__UpperCAmelCase   )

																											# up
																											for up_block in self.up_blocks:
																																		A__		   =							torch.utils.checkpoint.checkpoint(create_custom_forward(__UpperCAmelCase   )						,__UpperCAmelCase						,__UpperCAmelCase   )
													else:
																				# middle
																				A__		   =							self.mid_block(__UpperCAmelCase						,__UpperCAmelCase   )
																				A__		   =							sample.to(__UpperCAmelCase   )

																				# up
																				for up_block in self.up_blocks:
																											A__		   =							up_block(__UpperCAmelCase						,__UpperCAmelCase   )

        # post-process
													if latent_embeds is None:
																				A__		   =							self.conv_norm_out(__UpperCAmelCase   )
													else:
																				A__		   =							self.conv_norm_out(__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							self.conv_act(__UpperCAmelCase   )
													A__		   =							self.conv_out(__UpperCAmelCase   )

													return sample






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,__UpperCAmelCase="random"						,__UpperCAmelCase=False						,__UpperCAmelCase=True   )    -> Tuple:
													super().__init__()
													A__		   =							n_e
													A__		   =							vq_embed_dim
													A__		   =							beta
													A__		   =							legacy

													A__		   =							nn.Embedding(self.n_e						,self.vq_embed_dim   )
													self.embedding.weight.data.uniform_(-1.0 / self.n_e						,1.0 / self.n_e   )

													A__		   =							remap
													if self.remap is not None:
																				self.register_buffer('used'						,torch.tensor(np.load(self.remap   )   )   )
																				A__		   =							self.used.shape[0]
																				A__		   =							unknown_index  # "random" or "extra" or integer
																				if self.unknown_index == "extra":
																											A__		   =							self.re_embed
																											A__		   =							self.re_embed + 1
																				print(
																				    f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
																				    f'''Using {self.unknown_index} for unknown indices.'''   )
													else:
																				A__		   =							n_e

													A__		   =							sane_index_shape





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> List[str]:
													A__		   =							inds.shape
													assert len(__UpperCAmelCase   ) > 1
													A__		   =							inds.reshape(ishape[0]						,-1   )
													A__		   =							self.used.to(__UpperCAmelCase   )
													A__		   =							(inds[:, :, None] == used[None, None, ...]).long()
													A__		   =							match.argmax(-1   )
													A__		   =							match.sum(2   ) < 1
													if self.unknown_index == "random":
																				A__		   =							torch.randint(0						,self.re_embed						,size=new[unknown].shape   ).to(device=new.device   )
													else:
																				A__		   =							self.unknown_index
													return new.reshape(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							inds.shape
													assert len(__UpperCAmelCase   ) > 1
													A__		   =							inds.reshape(ishape[0]						,-1   )
													A__		   =							self.used.to(__UpperCAmelCase   )
													if self.re_embed > self.used.shape[0]:  # extra token
																				A__		   =							0  # simply set to zero
													A__		   =							torch.gather(used[None, :][inds.shape[0] * [0], :]						,1						,__UpperCAmelCase   )
													return back.reshape(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Dict:
													# reshape z -> (batch, height, width, channel) and flatten
													A__		   =							z.permute(0						,2						,3						,1   ).contiguous()
													A__		   =							z.view(-1						,self.vq_embed_dim   )

													# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
													A__		   =							torch.argmin(torch.cdist(__UpperCAmelCase						,self.embedding.weight   )						,dim=1   )

													A__		   =							self.embedding(__UpperCAmelCase   ).view(z.shape   )
													A__		   =							None
													A__		   =							None

													# compute loss for embedding
													if not self.legacy:
																				A__		   =							self.beta * torch.mean((z_q.detach() - z) ** 2   ) + torch.mean((z_q - z.detach()) ** 2   )
													else:
																				A__		   =							torch.mean((z_q.detach() - z) ** 2   ) + self.beta * torch.mean((z_q - z.detach()) ** 2   )

													# preserve gradients
													A__		   =							z + (z_q - z).detach()

													# reshape back to match original input shape
													A__		   =							z_q.permute(0						,3						,1						,2   ).contiguous()

													if self.remap is not None:
																				A__		   =							min_encoding_indices.reshape(z.shape[0]						,-1   )  # add batch axis
																				A__		   =							self.remap_to_used(__UpperCAmelCase   )
																				A__		   =							min_encoding_indices.reshape(-1						,1   )  # flatten

													if self.sane_index_shape:
																				A__		   =							min_encoding_indices.reshape(z_q.shape[0]						,z_q.shape[2]						,z_q.shape[3]   )

													return z_q, loss, (perplexity, min_encodings, min_encoding_indices)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													# shape specifying (batch, height, width, channel)
													if self.remap is not None:
																				A__		   =							indices.reshape(shape[0]						,-1   )  # add batch axis
																				A__		   =							self.unmap_to_all(__UpperCAmelCase   )
																				A__		   =							indices.reshape(-1   )  # flatten again

													# get quantized latent vectors
													A__		   =							self.embedding(__UpperCAmelCase   )

													if shape is not None:
																				A__		   =							z_q.view(__UpperCAmelCase   )
																				# reshape back to match original input shape
																				A__		   =							z_q.permute(0						,3						,1						,2   ).contiguous()

													return z_q






class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Any:
													A__		   =							parameters
													A__   ,     A__		   =							torch.chunk(__UpperCAmelCase						,2						,dim=1   )
													A__		   =							torch.clamp(self.logvar						,-3_0.0						,2_0.0   )
													A__		   =							deterministic
													A__		   =							torch.exp(0.5 * self.logvar   )
													A__		   =							torch.exp(self.logvar   )
													if self.deterministic:
																				A__		   =							A__		   =							torch.zeros_like(
																				    self.mean						,device=self.parameters.device						,dtype=self.parameters.dtype   )





						def  snake_case__		(				self						,__UpperCAmelCase = None   )    -> torch.FloatTensor:
													# make sure sample is on the same device as the parameters and has same dtype
													A__		   =							randn_tensor(
													    self.mean.shape						,generator=__UpperCAmelCase						,device=self.parameters.device						,dtype=self.parameters.dtype   )
													A__		   =							self.mean + self.std * sample
													return x





						def  snake_case__		(				self						,__UpperCAmelCase=None   )    -> int:
													if self.deterministic:
																				return torch.Tensor([0.0]   )
													else:
																				if other is None:
																											return 0.5 * torch.sum(torch.pow(self.mean						,2   ) + self.var - 1.0 - self.logvar						,dim=[1, 2, 3]   )
																				else:
																											return 0.5 * torch.sum(
																											    torch.pow(self.mean - other.mean						,2   ) / other.var
																											    + self.var / other.var
																											    - 1.0
																											    - self.logvar
																											    + other.logvar						,dim=[1, 2, 3]						,)





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=[1, 2, 3]   )    -> List[Any]:
													if self.deterministic:
																				return torch.Tensor([0.0]   )
													A__		   =							np.log(2.0 * np.pi   )
													return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,2   ) / self.var						,dim=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													return self.mean






"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							if isinstance(UpperCamelCase__				,			(list, tuple) ) and isinstance(videos[0]				,			(list, tuple) ) and is_valid_image(videos[0][0] ):
														return videos

							elif isinstance(UpperCamelCase__				,			(list, tuple) ) and is_valid_image(videos[0] ):
														return [videos]

							elif is_valid_image(UpperCamelCase__ ):
														return [[videos]]

							raise ValueError(F'''Could not make batched video from {videos}''' )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = 1 / 2_55						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'shortest_edge': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_center_crop
													A__		   =							crop_size
													A__		   =							resample
													A__		   =							do_rescale
													A__		   =							rescale_factor
													A__		   =							offset
													A__		   =							do_normalize
													A__		   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A__		   =							image_std if image_std is not None else IMAGENET_STANDARD_STD





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													if "shortest_edge" in size:
																				A__		   =							get_resize_output_image_size(__UpperCAmelCase						,size['shortest_edge']						,default_to_square=__UpperCAmelCase   )
													elif "height" in size and "width" in size:
																				A__		   =							(size['height'], size['width'])
													else:
																				raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''   )
													return resize(__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''   )
													return center_crop(__UpperCAmelCase						,size=(size['height'], size['width'])						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> Optional[Any]:
													A__		   =							image.astype(np.floataa   )
													if offset:
																				A__		   =							image - (scale / 2)
													return rescale(__UpperCAmelCase						,scale=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													return normalize(__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,)    -> np.ndarray:
													if do_resize and size is None or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_center_crop and crop_size is None:
																				raise ValueError('Crop size must be specified if do_center_crop is True.'   )

													if do_rescale and rescale_factor is None:
																				raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

													if do_normalize and (image_mean is None or image_std is None):
																				raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

													if offset and not do_rescale:
																				raise ValueError('For offset, do_rescale must also be set to True.'   )

													# All transformations expect numpy arrays.
													A__		   =							to_numpy_array(__UpperCAmelCase   )

													if do_resize:
																				A__		   =							self.resize(image=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase   )

													if do_center_crop:
																				A__		   =							self.center_crop(__UpperCAmelCase						,size=__UpperCAmelCase   )

													if do_rescale:
																				A__		   =							self.rescale(image=__UpperCAmelCase						,scale=__UpperCAmelCase						,offset=__UpperCAmelCase   )

													if do_normalize:
																				A__		   =							self.normalize(image=__UpperCAmelCase						,mean=__UpperCAmelCase						,std=__UpperCAmelCase   )

													A__		   =							to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   )
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_center_crop if do_center_crop is not None else self.do_center_crop
													A__		   =							do_rescale if do_rescale is not None else self.do_rescale
													A__		   =							rescale_factor if rescale_factor is not None else self.rescale_factor
													A__		   =							offset if offset is not None else self.offset
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							image_mean if image_mean is not None else self.image_mean
													A__		   =							image_std if image_std is not None else self.image_std

													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase						,default_to_square=__UpperCAmelCase   )
													A__		   =							crop_size if crop_size is not None else self.crop_size
													A__		   =							get_size_dict(__UpperCAmelCase						,param_name='crop_size'   )

													if not valid_images(__UpperCAmelCase   ):
																				raise ValueError(
																				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																				    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

													A__		   =							make_batched(__UpperCAmelCase   )

													A__		   =							[
													    [
													        self._preprocess_image(
													            image=__UpperCAmelCase						,do_resize=__UpperCAmelCase						,size=__UpperCAmelCase						,resample=__UpperCAmelCase						,do_center_crop=__UpperCAmelCase						,crop_size=__UpperCAmelCase						,do_rescale=__UpperCAmelCase						,rescale_factor=__UpperCAmelCase						,offset=__UpperCAmelCase						,do_normalize=__UpperCAmelCase						,image_mean=__UpperCAmelCase						,image_std=__UpperCAmelCase						,data_format=__UpperCAmelCase						,)
													        for img in video
													    ]
													    for video in videos
													]

													A__		   =							{'pixel_values': videos}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





