


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 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				torch.FloatTensor

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    int       ,      lowerCamelCase			:    List[Any]=3       ,      lowerCamelCase			:    str=3       ,      lowerCamelCase			:    Optional[int]=("DownEncoderBlock2D",)       ,      lowerCamelCase			:    str=(64,)       ,      lowerCamelCase			:    Optional[Any]=2       ,      lowerCamelCase			:    Any=32       ,      lowerCamelCase			:    Any="silu"       ,      lowerCamelCase			:    List[str]=True       ,      )      ->       str:
														super().__init__()
														__snake_case						:     Union[str, Any]									=  layers_per_block

														__snake_case						:     str									=  torch.nn.Convad(
														    lowerCamelCase       ,      block_out_channels[0]       ,      kernel_size=3       ,      stride=1       ,      padding=1       ,      )

														__snake_case						:     Optional[int]									=  None
														__snake_case						:     Any									=  nn.ModuleList([]							)

														# down
														__snake_case						:     Any									=  block_out_channels[0]
														for i, down_block_type in enumerate(lowerCamelCase							):
																					__snake_case						:     List[str]									=  output_channel
																					__snake_case						:     Any									=  block_out_channels[i]
																					__snake_case						:     Tuple									=  i == len(lowerCamelCase							) - 1

																					__snake_case						:     Dict									=  get_down_block(
																					    lowerCamelCase       ,      num_layers=self.layers_per_block       ,      in_channels=lowerCamelCase       ,      out_channels=lowerCamelCase       ,      add_downsample=not is_final_block       ,      resnet_eps=1E-6       ,      downsample_padding=0       ,      resnet_act_fn=lowerCamelCase       ,      resnet_groups=lowerCamelCase       ,      attention_head_dim=lowerCamelCase       ,      temb_channels=lowerCamelCase       ,      )
																					self.down_blocks.append(lowerCamelCase							)

														# mid
														__snake_case						:     str									=  UNetMidBlockaD(
														    in_channels=block_out_channels[-1]       ,      resnet_eps=1E-6       ,      resnet_act_fn=lowerCamelCase       ,      output_scale_factor=1       ,      resnet_time_scale_shift="default"       ,      attention_head_dim=block_out_channels[-1]       ,      resnet_groups=lowerCamelCase       ,      temb_channels=lowerCamelCase       ,      )

														# out
														__snake_case						:     Optional[int]									=  nn.GroupNorm(num_channels=block_out_channels[-1]       ,      num_groups=lowerCamelCase       ,      eps=1E-6							)
														__snake_case						:     int									=  nn.SiLU()

														__snake_case						:     Tuple									=  2 * out_channels if double_z else out_channels
														__snake_case						:     str									=  nn.Convad(block_out_channels[-1]       ,      lowerCamelCase       ,      3       ,      padding=1							)

														__snake_case						:     Any									=  False






							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    int							)      ->       int:
														__snake_case						:     Any									=  x
														__snake_case						:     Union[str, Any]									=  self.conv_in(lowerCamelCase							)

														if self.training and self.gradient_checkpointing:

																					def create_custom_forward(lowerCamelCase			:    Dict							):
																												def custom_forward(*lowerCamelCase			:    str							):
																																			return module(*lowerCamelCase							)

																												return custom_forward

																					# down
																					if is_torch_version(">="       ,      "1.11.0"							):
																												for down_block in self.down_blocks:
																																			__snake_case						:     str									=  torch.utils.checkpoint.checkpoint(
																																			    create_custom_forward(lowerCamelCase							)       ,      lowerCamelCase       ,      use_reentrant=lowerCamelCase							)
																												# middle
																												__snake_case						:     Optional[Any]									=  torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block							)       ,      lowerCamelCase       ,      use_reentrant=lowerCamelCase							)
																					else:
																												for down_block in self.down_blocks:
																																			__snake_case						:     Union[str, Any]									=  torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCamelCase							)       ,      lowerCamelCase							)
																												# middle
																												__snake_case						:     List[str]									=  torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block							)       ,      lowerCamelCase							)

														else:
																					# down
																					for down_block in self.down_blocks:
																												__snake_case						:     Union[str, Any]									=  down_block(lowerCamelCase							)

																					# middle
																					__snake_case						:     Dict									=  self.mid_block(lowerCamelCase							)

														# post-process
														__snake_case						:     Union[str, Any]									=  self.conv_norm_out(lowerCamelCase							)
														__snake_case						:     int									=  self.conv_act(lowerCamelCase							)
														__snake_case						:     List[Any]									=  self.conv_out(lowerCamelCase							)

														return sample

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    Optional[int]       ,      lowerCamelCase			:    int=3       ,      lowerCamelCase			:    int=3       ,      lowerCamelCase			:    Any=("UpDecoderBlock2D",)       ,      lowerCamelCase			:    List[str]=(64,)       ,      lowerCamelCase			:    List[str]=2       ,      lowerCamelCase			:    Any=32       ,      lowerCamelCase			:    Union[str, Any]="silu"       ,      lowerCamelCase			:    Union[str, Any]="group"       ,      )      ->       str:
														super().__init__()
														__snake_case						:     Tuple									=  layers_per_block

														__snake_case						:     Optional[int]									=  nn.Convad(
														    lowerCamelCase       ,      block_out_channels[-1]       ,      kernel_size=3       ,      stride=1       ,      padding=1       ,      )

														__snake_case						:     Dict									=  None
														__snake_case						:     Optional[int]									=  nn.ModuleList([]							)

														__snake_case						:     List[Any]									=  in_channels if norm_type == "spatial" else None

														# mid
														__snake_case						:     int									=  UNetMidBlockaD(
														    in_channels=block_out_channels[-1]       ,      resnet_eps=1E-6       ,      resnet_act_fn=lowerCamelCase       ,      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=lowerCamelCase       ,      temb_channels=lowerCamelCase       ,      )

														# up
														__snake_case						:     Dict									=  list(reversed(lowerCamelCase							)							)
														__snake_case						:     List[str]									=  reversed_block_out_channels[0]
														for i, up_block_type in enumerate(lowerCamelCase							):
																					__snake_case						:     Any									=  output_channel
																					__snake_case						:     Tuple									=  reversed_block_out_channels[i]

																					__snake_case						:     int									=  i == len(lowerCamelCase							) - 1

																					__snake_case						:     List[str]									=  get_up_block(
																					    lowerCamelCase       ,      num_layers=self.layers_per_block + 1       ,      in_channels=lowerCamelCase       ,      out_channels=lowerCamelCase       ,      prev_output_channel=lowerCamelCase       ,      add_upsample=not is_final_block       ,      resnet_eps=1E-6       ,      resnet_act_fn=lowerCamelCase       ,      resnet_groups=lowerCamelCase       ,      attention_head_dim=lowerCamelCase       ,      temb_channels=lowerCamelCase       ,      resnet_time_scale_shift=lowerCamelCase       ,      )
																					self.up_blocks.append(lowerCamelCase							)
																					__snake_case						:     int									=  output_channel

														# out
														if norm_type == "spatial":
																					__snake_case						:     List[Any]									=  SpatialNorm(block_out_channels[0]       ,      lowerCamelCase							)
														else:
																					__snake_case						:     List[str]									=  nn.GroupNorm(num_channels=block_out_channels[0]       ,      num_groups=lowerCamelCase       ,      eps=1E-6							)
														__snake_case						:     Any									=  nn.SiLU()
														__snake_case						:     Optional[Any]									=  nn.Convad(block_out_channels[0]       ,      lowerCamelCase       ,      3       ,      padding=1							)

														__snake_case						:     Any									=  False






							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    Dict=None							)      ->       int:
														__snake_case						:     Union[str, Any]									=  z
														__snake_case						:     int									=  self.conv_in(lowerCamelCase							)

														__snake_case						:     Optional[int]									=  next(iter(self.up_blocks.parameters()							)							).dtype
														if self.training and self.gradient_checkpointing:

																					def create_custom_forward(lowerCamelCase			:    str							):
																												def custom_forward(*lowerCamelCase			:    Optional[Any]							):
																																			return module(*lowerCamelCase							)

																												return custom_forward

																					if is_torch_version(">="       ,      "1.11.0"							):
																												# middle
																												__snake_case						:     str									=  torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block							)       ,      lowerCamelCase       ,      lowerCamelCase       ,      use_reentrant=lowerCamelCase							)
																												__snake_case						:     Dict									=  sample.to(lowerCamelCase							)

																												# up
																												for up_block in self.up_blocks:
																																			__snake_case						:     str									=  torch.utils.checkpoint.checkpoint(
																																			    create_custom_forward(lowerCamelCase							)       ,      lowerCamelCase       ,      lowerCamelCase       ,      use_reentrant=lowerCamelCase							)
																					else:
																												# middle
																												__snake_case						:     Union[str, Any]									=  torch.utils.checkpoint.checkpoint(
																												    create_custom_forward(self.mid_block							)       ,      lowerCamelCase       ,      lowerCamelCase							)
																												__snake_case						:     List[Any]									=  sample.to(lowerCamelCase							)

																												# up
																												for up_block in self.up_blocks:
																																			__snake_case						:     Any									=  torch.utils.checkpoint.checkpoint(create_custom_forward(lowerCamelCase							)       ,      lowerCamelCase       ,      lowerCamelCase							)
														else:
																					# middle
																					__snake_case						:     Tuple									=  self.mid_block(lowerCamelCase       ,      lowerCamelCase							)
																					__snake_case						:     Union[str, Any]									=  sample.to(lowerCamelCase							)

																					# up
																					for up_block in self.up_blocks:
																												__snake_case						:     int									=  up_block(lowerCamelCase       ,      lowerCamelCase							)

        # post-process
														if latent_embeds is None:
																					__snake_case						:     List[str]									=  self.conv_norm_out(lowerCamelCase							)
														else:
																					__snake_case						:     List[Any]									=  self.conv_norm_out(lowerCamelCase       ,      lowerCamelCase							)
														__snake_case						:     Optional[Any]									=  self.conv_act(lowerCamelCase							)
														__snake_case						:     List[Any]									=  self.conv_out(lowerCamelCase							)

														return sample

class 							a						(nn.Module ):




							"""simple docstring"""


							def __init__(				self			:    Optional[Any]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Tuple=None       ,      lowerCamelCase			:    Dict="random"       ,      lowerCamelCase			:    List[str]=False       ,      lowerCamelCase			:    int=True							)      ->       List[str]:
														super().__init__()
														__snake_case						:     Any									=  n_e
														__snake_case						:     Optional[int]									=  vq_embed_dim
														__snake_case						:     List[str]									=  beta
														__snake_case						:     List[Any]									=  legacy

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

														__snake_case						:     Union[str, Any]									=  remap
														if self.remap is not None:
																					self.register_buffer("used"       ,      torch.tensor(np.load(self.remap							)							)							)
																					__snake_case						:     List[str]									=  self.used.shape[0]
																					__snake_case						:     Union[str, Any]									=  unknown_index  # "random" or "extra" or integer
																					if self.unknown_index == "extra":
																												__snake_case						:     List[str]									=  self.re_embed
																												__snake_case						:     str									=  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:
																					__snake_case						:     List[Any]									=  n_e

														__snake_case						:     Union[str, Any]									=  sane_index_shape

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

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    int							)      ->       List[Any]:
														__snake_case						:     str									=  inds.shape
														assert len(lowerCamelCase							) > 1
														__snake_case						:     Union[str, Any]									=  inds.reshape(ishape[0]       ,      -1							)
														__snake_case						:     Any									=  self.used.to(lowerCamelCase							)
														if self.re_embed > self.used.shape[0]:  # extra token
																					__snake_case						:     List[str]									=  0  # simply set to zero
														__snake_case						:     List[Any]									=  torch.gather(used[None, :][inds.shape[0] * [0], :]       ,      1       ,      lowerCamelCase							)
														return back.reshape(lowerCamelCase							)

							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    Tuple							)      ->       Any:
														# reshape z -> (batch, height, width, channel) and flatten
														__snake_case						:     Any									=  z.permute(0       ,      2       ,      3       ,      1							).contiguous()
														__snake_case						:     Optional[Any]									=  z.view(-1       ,      self.vq_embed_dim							)

														# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
														__snake_case						:     Optional[int]									=  torch.argmin(torch.cdist(lowerCamelCase       ,      self.embedding.weight							)       ,      dim=1							)

														__snake_case						:     Union[str, Any]									=  self.embedding(lowerCamelCase							).view(z.shape							)
														__snake_case						:     Any									=  None
														__snake_case						:     Optional[Any]									=  None

														# compute loss for embedding
														if not self.legacy:
																					__snake_case						:     Union[str, Any]									=  self.beta * torch.mean((z_q.detach() - z) ** 2							) + torch.mean((z_q - z.detach()) ** 2							)
														else:
																					__snake_case						:     int									=  torch.mean((z_q.detach() - z) ** 2							) + self.beta * torch.mean((z_q - z.detach()) ** 2							)

														# preserve gradients
														__snake_case						:     Dict									=  z + (z_q - z).detach()

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

														if self.remap is not None:
																					__snake_case						:     int									=  min_encoding_indices.reshape(z.shape[0]       ,      -1							)  # add batch axis
																					__snake_case						:     Union[str, Any]									=  self.remap_to_used(lowerCamelCase							)
																					__snake_case						:     Union[str, Any]									=  min_encoding_indices.reshape(-1       ,      1							)  # flatten

														if self.sane_index_shape:
																					__snake_case						:     List[Any]									=  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			:    Optional[Any]       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    Dict							)      ->       List[str]:
														# shape specifying (batch, height, width, channel)
														if self.remap is not None:
																					__snake_case						:     List[Any]									=  indices.reshape(shape[0]       ,      -1							)  # add batch axis
																					__snake_case						:     Dict									=  self.unmap_to_all(lowerCamelCase							)
																					__snake_case						:     List[Any]									=  indices.reshape(-1							)  # flatten again

														# get quantized latent vectors
														__snake_case						:     Optional[Any]									=  self.embedding(lowerCamelCase							)

														if shape is not None:
																					__snake_case						:     List[Any]									=  z_q.view(lowerCamelCase							)
																					# reshape back to match original input shape
																					__snake_case						:     Optional[int]									=  z_q.permute(0       ,      3       ,      1       ,      2							).contiguous()

														return z_q

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def __init__(				self			:    List[Any]       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    List[Any]=False							)      ->       Optional[Any]:
														__snake_case						:     List[str]									=  parameters
														__snake_case					,     __snake_case						:     List[Any]									=  torch.chunk(lowerCamelCase       ,      2       ,      dim=1							)
														__snake_case						:     int									=  torch.clamp(self.logvar       ,      -30.0       ,      20.0							)
														__snake_case						:     List[Any]									=  deterministic
														__snake_case						:     Tuple									=  torch.exp(0.5 * self.logvar							)
														__snake_case						:     Tuple									=  torch.exp(self.logvar							)
														if self.deterministic:
																					__snake_case						:     str									=  torch.zeros_like(
																					    self.mean       ,      device=self.parameters.device       ,      dtype=self.parameters.dtype							)

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Optional[torch.Generator] = None							)      ->       torch.FloatTensor:
														# make sure sample is on the same device as the parameters and has same dtype
														__snake_case						:     Tuple									=  randn_tensor(
														    self.mean.shape       ,      generator=lowerCamelCase       ,      device=self.parameters.device       ,      dtype=self.parameters.dtype							)
														__snake_case						:     Optional[int]									=  self.mean + self.std * sample
														return x

							def        __snake_case   (				self			:    List[Any]       ,      lowerCamelCase			:    Union[str, Any]=None							)      ->       Dict:
														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			:    str       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Optional[Any]=[1, 2, 3]							)      ->       List[str]:
														if self.deterministic:
																					return torch.Tensor([0.0]							)
														__snake_case						:     List[str]									=  np.log(2.0 * np.pi							)
														return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean       ,      2							) / self.var       ,      dim=lowerCamelCase							)






							def        __snake_case   (				self			:    int							)      ->       Union[str, Any]:
														return self.mean








from typing import Dict, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						["""pixel_values"""]
 def __init__( self    : Any     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = 0.9     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : Union[int, float] = 1 / 255     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : bool = True     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      None:
     super().__init__(**UpperCAmelCase      )
     lowerCamelCase__       : int   					=						size if size is not None else {'shortest_edge': 224}
     lowerCamelCase__       : Tuple   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : Any   					=						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowerCamelCase__       : str   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Tuple   					=						do_resize
     lowerCamelCase__       : str   					=						size
     lowerCamelCase__       : List[str]   					=						crop_pct
     lowerCamelCase__       : Any   					=						resample
     lowerCamelCase__       : Tuple   					=						do_center_crop
     lowerCamelCase__       : Any   					=						crop_size
     lowerCamelCase__       : Optional[int]   					=						do_rescale
     lowerCamelCase__       : Optional[Any]   					=						rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     lowerCamelCase__       : Optional[Any]   					=						image_std if image_std is not None else IMAGENET_DEFAULT_STD
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[float] = None     ,	UpperCAmelCase    : PILImageResampling = PILImageResampling.BICUBIC     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : int     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Any   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""      )
     if crop_pct is not None:
         if "shortest_edge" in size:
             lowerCamelCase__       : List[Any]   					=						int(size['shortest_edge'] / crop_pct      )
         elif "height" in size and "width" in size:
             if size["height"] == size["width"]:
                 lowerCamelCase__       : int   					=						int(size['height'] / crop_pct      )
             else:
                 lowerCamelCase__       : Any   					=						(int(size['height'] / crop_pct      ), int(size['width'] / crop_pct      ))
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

         lowerCamelCase__       : Union[str, Any]   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     else:
         if "shortest_edge" in size:
             lowerCamelCase__       : int   					=						get_resize_output_image_size(UpperCAmelCase     ,	size=size['shortest_edge']     ,	default_to_square=UpperCAmelCase      )
         elif "height" in size and "width" in size:
             lowerCamelCase__       : List[Any]   					=						(size['height'], size['width'])
         else:
             raise ValueError('Invalid size for resize: {}'.format(UpperCAmelCase      )      )

     return resize(UpperCAmelCase     ,	size=UpperCAmelCase     ,	resample=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Dict[str, int]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : str     ,	)     ->      np.ndarray:
     lowerCamelCase__       : Union[str, Any]   					=						get_size_dict(UpperCAmelCase      )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""      )
     return center_crop(UpperCAmelCase     ,	size=(size['height'], size['width'])     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Union[str, Any]     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[int, float]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : List[Any]     ,	)     ->      int:
     return rescale(UpperCAmelCase     ,	scale=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : np.ndarray     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Union[float, List[float]]     ,	UpperCAmelCase    : Optional[Union[str, ChannelDimension]] = None     ,	**UpperCAmelCase    : Dict     ,	)     ->      np.ndarray:
     return normalize(UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase     ,	data_format=UpperCAmelCase     ,	**UpperCAmelCase      )







 def 	A_      ( self    : str     ,	UpperCAmelCase    : ImageInput     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : int = None     ,	UpperCAmelCase    : PILImageResampling = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Dict[str, int] = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : float = None     ,	UpperCAmelCase    : bool = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[float, List[float]]] = None     ,	UpperCAmelCase    : Optional[Union[str, TensorType]] = None     ,	UpperCAmelCase    : ChannelDimension = ChannelDimension.FIRST     ,	**UpperCAmelCase    : Any     ,	)     ->      PIL.Image.Image:
     lowerCamelCase__       : Optional[Any]   					=						do_resize if do_resize is not None else self.do_resize
     lowerCamelCase__       : Any   					=						crop_pct if crop_pct is not None else self.crop_pct
     lowerCamelCase__       : Any   					=						resample if resample is not None else self.resample
     lowerCamelCase__       : Any   					=						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCamelCase__       : Optional[int]   					=						do_rescale if do_rescale is not None else self.do_rescale
     lowerCamelCase__       : List[Any]   					=						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCamelCase__       : Union[str, Any]   					=						do_normalize if do_normalize is not None else self.do_normalize
     lowerCamelCase__       : Any   					=						image_mean if image_mean is not None else self.image_mean
     lowerCamelCase__       : str   					=						image_std if image_std is not None else self.image_std

     lowerCamelCase__       : Optional[Any]   					=						size if size is not None else self.size
     lowerCamelCase__       : Optional[Any]   					=						get_size_dict(UpperCAmelCase     ,	default_to_square=UpperCAmelCase      )
     lowerCamelCase__       : List[str]   					=						crop_size if crop_size is not None else self.crop_size
     lowerCamelCase__       : int   					=						get_size_dict(UpperCAmelCase     ,	param_name='crop_size'      )

     lowerCamelCase__       : Any   					=						make_list_of_images(UpperCAmelCase      )

     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.'      )

     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_pct is None:
         raise ValueError('Crop_pct 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.'      )

     # All transformations expect numpy arrays.
     lowerCamelCase__       : Dict   					=						[to_numpy_array(UpperCAmelCase      ) for image in images]

     if do_resize:
         lowerCamelCase__       : Any   					=						[self.resize(image=UpperCAmelCase     ,	size=UpperCAmelCase     ,	crop_pct=UpperCAmelCase     ,	resample=UpperCAmelCase      ) for image in images]

     if do_center_crop:
         lowerCamelCase__       : Optional[Any]   					=						[self.center_crop(image=UpperCAmelCase     ,	size=UpperCAmelCase      ) for image in images]

     if do_rescale:
         lowerCamelCase__       : int   					=						[self.rescale(image=UpperCAmelCase     ,	scale=UpperCAmelCase      ) for image in images]

     if do_normalize:
         lowerCamelCase__       : List[Any]   					=						[self.normalize(image=UpperCAmelCase     ,	mean=UpperCAmelCase     ,	std=UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						[to_channel_dimension_format(UpperCAmelCase     ,	UpperCAmelCase      ) for image in images]

     lowerCamelCase__       : Dict   					=						{'pixel_values': images}
     return BatchFeature(data=UpperCAmelCase     ,	tensor_type=UpperCAmelCase      )





