
import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__lowerCamelCase								=					'pcuenq/k-upscaler'




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	*, _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      List[Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		{
							    # norm1
							    F"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[F"""{resnet_prefix}.main.0.mapper.weight"""],
							    F"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[F"""{resnet_prefix}.main.0.mapper.bias"""],
							    # conv1
							    F"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[F"""{resnet_prefix}.main.2.weight"""],
							    F"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[F"""{resnet_prefix}.main.2.bias"""],
							    # norm2
							    F"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[F"""{resnet_prefix}.main.4.mapper.weight"""],
							    F"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[F"""{resnet_prefix}.main.4.mapper.bias"""],
							    # conv2
							    F"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[F"""{resnet_prefix}.main.6.weight"""],
							    F"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[F"""{resnet_prefix}.main.6.bias"""],
							}

							if resnet.conv_shortcut is not None:
														rv.update(
														    {
														        F"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[F"""{resnet_prefix}.skip.weight"""],
														    }  )

							return rv




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	*, _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Tuple:

							"""simple docstring"""






							_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			Dict	       =		checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3      ,	dim=0  )
							_UpperCAmelCase       ,_UpperCAmelCase       ,_UpperCAmelCase  :			Optional[Any]	       =		checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3      ,	dim=0  )
							_UpperCAmelCase  :			List[Any]	       =		{
							    # norm
							    F"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[F"""{attention_prefix}.norm_in.mapper.weight"""],
							    F"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[F"""{attention_prefix}.norm_in.mapper.bias"""],
							    # to_q
							    F"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1  ).squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
							    # to_k
							    F"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
							    # to_v
							    F"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
							    # to_out
							    F"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[F"""{attention_prefix}.out_proj.weight"""]
							    .squeeze(-1  )
							    .squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[F"""{attention_prefix}.out_proj.bias"""],
							}

							return rv




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	*, _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      Tuple:

							"""simple docstring"""






							_UpperCAmelCase       ,_UpperCAmelCase  :			Union[str, Any]	       =		checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2      ,	dim=0  )
							_UpperCAmelCase       ,_UpperCAmelCase  :			Any	       =		checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2      ,	dim=0  )

							_UpperCAmelCase  :			str	       =		{
							    # norm2 (ada groupnorm)
							    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
							        F"""{attention_prefix}.norm_dec.mapper.weight"""
							    ],
							    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
							        F"""{attention_prefix}.norm_dec.mapper.bias"""
							    ],
							    # layernorm on encoder_hidden_state
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
							        F"""{attention_prefix}.norm_enc.weight"""
							    ],
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
							        F"""{attention_prefix}.norm_enc.bias"""
							    ],
							    # to_q
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
							        F"""{attention_prefix}.q_proj.weight"""
							    ]
							    .squeeze(-1  )
							    .squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
							        F"""{attention_prefix}.q_proj.bias"""
							    ],
							    # to_k
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
							    # to_v
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
							    # to_out
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
							        F"""{attention_prefix}.out_proj.weight"""
							    ]
							    .squeeze(-1  )
							    .squeeze(-1  ),
							    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
							        F"""{attention_prefix}.out_proj.bias"""
							    ],
							}

							return rv




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      List[str]:

							"""simple docstring"""






							_UpperCAmelCase  :			Dict	       =		"inner_model.u_net.u_blocks" if block_type == "up" else "inner_model.u_net.d_blocks"
							_UpperCAmelCase  :			str	       =		F"""{block_prefix}.{block_idx}"""

							_UpperCAmelCase  :			Optional[Any]	       =		{}

							if not hasattr(_SCREAMING_SNAKE_CASE      ,	"attentions"  ):
														_UpperCAmelCase  :			Tuple	       =		1  # resnet only
							elif not block.attentions[0].add_self_attention:
														_UpperCAmelCase  :			Tuple	       =		2  # resnet -> cross-attention
							else:
														_UpperCAmelCase  :			str	       =		3  # resnet -> self-attention -> cross-attention)

							for resnet_idx, resnet in enumerate(block.resnets  ):
														# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
														_UpperCAmelCase  :			str	       =		F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
														_UpperCAmelCase  :			List[Any]	       =		n * resnet_idx if block_type == "up" else n * resnet_idx + 1
														_UpperCAmelCase  :			Union[str, Any]	       =		F"""{block_prefix}.{idx}""" if block_type == "up" else F"""{block_prefix}.{idx}"""

														diffusers_checkpoint.update(
														    resnet_to_diffusers_checkpoint(
														        _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	diffusers_resnet_prefix=_SCREAMING_SNAKE_CASE      ,	resnet_prefix=_SCREAMING_SNAKE_CASE  )  )

							if hasattr(_SCREAMING_SNAKE_CASE      ,	"attentions"  ):
														for attention_idx, attention in enumerate(block.attentions  ):
																					_UpperCAmelCase  :			Any	       =		F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
																					_UpperCAmelCase  :			Dict	       =		n * attention_idx + 1 if block_type == "up" else n * attention_idx + 2
																					_UpperCAmelCase  :			Dict	       =		F"""{block_prefix}.{idx}"""
																					_UpperCAmelCase  :			Optional[Any]	       =		F"""{block_prefix}.{idx }"""
																					_UpperCAmelCase  :			Optional[int]	       =		1 if not attention.add_self_attention else 2
																					_UpperCAmelCase  :			Any	       =		(
																					    n * attention_idx + cross_attention_index
																					    if block_type == "up"
																					    else n * attention_idx + cross_attention_index + 1
																					)
																					_UpperCAmelCase  :			Tuple	       =		F"""{block_prefix}.{idx }"""

																					diffusers_checkpoint.update(
																					    cross_attn_to_diffusers_checkpoint(
																					        _SCREAMING_SNAKE_CASE      ,	diffusers_attention_prefix=_SCREAMING_SNAKE_CASE      ,	diffusers_attention_index=2      ,	attention_prefix=_SCREAMING_SNAKE_CASE      ,	)  )

																					if attention.add_self_attention is True:
																												diffusers_checkpoint.update(
																												    self_attn_to_diffusers_checkpoint(
																												        _SCREAMING_SNAKE_CASE      ,	diffusers_attention_prefix=_SCREAMING_SNAKE_CASE      ,	attention_prefix=_SCREAMING_SNAKE_CASE      ,	)  )

							return diffusers_checkpoint




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )   ->      List[str]:

							"""simple docstring"""






							_UpperCAmelCase  :			Optional[int]	       =		{}

							# pre-processing
							diffusers_checkpoint.update(
							    {
							        "conv_in.weight": checkpoint["inner_model.proj_in.weight"],
							        "conv_in.bias": checkpoint["inner_model.proj_in.bias"],
							    }  )

							# timestep and class embedding
							diffusers_checkpoint.update(
							    {
							        "time_proj.weight": checkpoint["inner_model.timestep_embed.weight"].squeeze(-1  ),
							        "time_embedding.linear_1.weight": checkpoint["inner_model.mapping.0.weight"],
							        "time_embedding.linear_1.bias": checkpoint["inner_model.mapping.0.bias"],
							        "time_embedding.linear_2.weight": checkpoint["inner_model.mapping.2.weight"],
							        "time_embedding.linear_2.bias": checkpoint["inner_model.mapping.2.bias"],
							        "time_embedding.cond_proj.weight": checkpoint["inner_model.mapping_cond.weight"],
							    }  )

							# down_blocks
							for down_block_idx, down_block in enumerate(model.down_blocks  ):
														diffusers_checkpoint.update(block_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	"down"  )  )

							# up_blocks
							for up_block_idx, up_block in enumerate(model.up_blocks  ):
														diffusers_checkpoint.update(block_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	"up"  )  )

							# post-processing
							diffusers_checkpoint.update(
							    {
							        "conv_out.weight": checkpoint["inner_model.proj_out.weight"],
							        "conv_out.bias": checkpoint["inner_model.proj_out.bias"],
							    }  )

							return diffusers_checkpoint




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Optional[int]:

							"""simple docstring"""






							_UpperCAmelCase  :			Optional[Any]	       =		original_config["input_channels"] + original_config["unet_cond_dim"]
							_UpperCAmelCase  :			Any	       =		original_config["input_channels"] + (1 if original_config["has_variance"] else 0)

							_UpperCAmelCase  :			str	       =		original_config["channels"]

							assert (
							    len(set(original_config["depths"]  )  ) == 1
							), "UNet2DConditionModel currently do not support blocks with different number of layers"
							_UpperCAmelCase  :			List[str]	       =		original_config["depths"][0]

							_UpperCAmelCase  :			Any	       =		original_config["mapping_cond_dim"]
							_UpperCAmelCase  :			Dict	       =		original_config["cross_cond_dim"]

							_UpperCAmelCase  :			Union[str, Any]	       =		[]
							_UpperCAmelCase  :			Dict	       =		[]
							for s, c in zip(original_config["self_attn_depths"]      ,	original_config["cross_attn_depths"]  ):
														if s:
																					_UpperCAmelCase  :			List[str]	       =		"self"
																					_UpperCAmelCase  :			Dict	       =		"cross" if c else None
														elif c:
																					_UpperCAmelCase  :			Union[str, Any]	       =		"cross"
																					_UpperCAmelCase  :			Optional[int]	       =		None
														else:
																					_UpperCAmelCase  :			int	       =		None
																					_UpperCAmelCase  :			List[str]	       =		None
														attna_types.append(_SCREAMING_SNAKE_CASE  )
														attna_types.append(_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			Union[str, Any]	       =		UNetaDConditionModel(
							    in_channels=_SCREAMING_SNAKE_CASE      ,	out_channels=_SCREAMING_SNAKE_CASE      ,	down_block_types=("KDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D", "KCrossAttnDownBlock2D")      ,	mid_block_type=_SCREAMING_SNAKE_CASE      ,	up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")      ,	block_out_channels=_SCREAMING_SNAKE_CASE      ,	layers_per_block=_SCREAMING_SNAKE_CASE      ,	act_fn="gelu"      ,	norm_num_groups=_SCREAMING_SNAKE_CASE      ,	cross_attention_dim=_SCREAMING_SNAKE_CASE      ,	attention_head_dim=6_4      ,	time_cond_proj_dim=_SCREAMING_SNAKE_CASE      ,	resnet_time_scale_shift="scale_shift"      ,	time_embedding_type="fourier"      ,	timestep_post_act="gelu"      ,	conv_in_kernel=1      ,	conv_out_kernel=1      ,	)

							return unet




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE  )   ->      Union[str, Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			Optional[Any]	       =		torch.device("cuda" if torch.cuda.is_available() else "cpu"  )

							_UpperCAmelCase  :			Optional[int]	       =		huggingface_hub.hf_hub_download(_SCREAMING_SNAKE_CASE      ,	"config_laion_text_cond_latent_upscaler_2.json"  )
							_UpperCAmelCase  :			Tuple	       =		huggingface_hub.hf_hub_download(
							    _SCREAMING_SNAKE_CASE      ,	"laion_text_cond_latent_upscaler_2_1_00470000_slim.pth"  )
							print(F"""loading original model configuration from {orig_config_path}"""  )
							print(F"""loading original model checkpoint from {orig_weights_path}"""  )

							print("converting to diffusers unet"  )
							_UpperCAmelCase  :			List[Any]	       =		K.config.load_config(open(_SCREAMING_SNAKE_CASE  )  )["model"]
							_UpperCAmelCase  :			Dict	       =		unet_model_from_original_config(_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			Optional[int]	       =		torch.load(_SCREAMING_SNAKE_CASE      ,	map_location=_SCREAMING_SNAKE_CASE  )["model_ema"]
							_UpperCAmelCase  :			int	       =		unet_to_diffusers_checkpoint(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )

							model.load_state_dict(_SCREAMING_SNAKE_CASE      ,	strict=_SCREAMING_SNAKE_CASE  )
							model.save_pretrained(args.dump_path  )
							print(F"""saving converted unet model in {args.dump_path}"""  )


if __name__ == "__main__":
				__lowerCamelCase								=					argparse.ArgumentParser()

				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				__lowerCamelCase								=					parser.parse_args()

				main(args)






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


__lowerCamelCase								=					logging.get_logger(__name__)
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Union[str, Any]				   =      ["""pixel_values"""]

						def __init__(					self		:			str	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			PILImageResampling = PIL.Image.NEAREST	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			Union[int, float] = 1 / 2_55	,						_lowerCamelCase		:			bool = False	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			bool = True	,						**_lowerCamelCase		:			Tuple	,						):
													super().__init__(**_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		size if size is not None else {"height": 3_46, "width": 3_46}
													_UpperCAmelCase  :			Dict	       =		get_size_dict(_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
													_UpperCAmelCase  :			int	       =		get_size_dict(_lowerCamelCase	,						param_name="crop_size"			)

													_UpperCAmelCase  :			Dict	       =		do_resize
													_UpperCAmelCase  :			Tuple	       =		size
													_UpperCAmelCase  :			Union[str, Any]	       =		resample
													_UpperCAmelCase  :			List[Any]	       =		do_center_crop
													_UpperCAmelCase  :			List[str]	       =		crop_size
													_UpperCAmelCase  :			int	       =		do_rescale
													_UpperCAmelCase  :			str	       =		rescale_factor
													_UpperCAmelCase  :			int	       =		rescale_offset
													_UpperCAmelCase  :			Any	       =		do_normalize
													_UpperCAmelCase  :			Optional[int]	       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													_UpperCAmelCase  :			List[str]	       =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													_UpperCAmelCase  :			Dict	       =		include_top

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Dict[str, int]	,						_lowerCamelCase		:			PILImageResampling = PIL.Image.NEAREST	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Tuple	,						):
													_UpperCAmelCase  :			Optional[int]	       =		get_size_dict(_lowerCamelCase			)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
													return resize(
													    _lowerCamelCase	,						size=(size["height"], size["width"])	,						resample=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Dict[str, int]	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			List[str]	,						):
													_UpperCAmelCase  :			Optional[Any]	       =		get_size_dict(_lowerCamelCase			)
													if "height" not in size or "width" not in size:
																				raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
													return center_crop(_lowerCamelCase	,						size=(size["height"], size["width"])	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)

						def 						a__ (					self		:			Optional[Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Union[int, float]	,						_lowerCamelCase		:			bool = True	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Tuple	,						):
													if offset:
																				_UpperCAmelCase  :			Any	       =		(image - 1_27.5) * scale
																				if data_format is not None:
																											_UpperCAmelCase  :			Union[str, Any]	       =		to_channel_dimension_format(_lowerCamelCase	,						_lowerCamelCase			)
																				_UpperCAmelCase  :			Optional[Any]	       =		rescaled_image.astype(np.floataa			)
													else:
																				_UpperCAmelCase  :			List[Any]	       =		rescale(_lowerCamelCase	,						scale=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)
													return rescaled_image

						def 						a__ (					self		:			Union[str, Any]	,						_lowerCamelCase		:			np.ndarray	,						_lowerCamelCase		:			Union[float, List[float]]	,						_lowerCamelCase		:			Union[float, List[float]]	,						_lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None	,						**_lowerCamelCase		:			Union[str, Any]	,						):
													return normalize(_lowerCamelCase	,						mean=_lowerCamelCase	,						std=_lowerCamelCase	,						data_format=_lowerCamelCase	,						**_lowerCamelCase			)






						def 						a__ (					self		:			str	,						_lowerCamelCase		:			ImageInput	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			List[Any]=None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Dict[str, int] = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			float = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			Optional[Union[float, List[float]]] = None	,						_lowerCamelCase		:			bool = None	,						_lowerCamelCase		:			Optional[Union[str, TensorType]] = None	,						_lowerCamelCase		:			ChannelDimension = ChannelDimension.FIRST	,						**_lowerCamelCase		:			str	,						):
													_UpperCAmelCase  :			Optional[Any]	       =		do_resize if do_resize is not None else self.do_resize
													_UpperCAmelCase  :			Optional[int]	       =		resample if resample is not None else self.resample
													_UpperCAmelCase  :			Tuple	       =		do_center_crop if do_center_crop is not None else self.do_center_crop
													_UpperCAmelCase  :			str	       =		do_rescale if do_rescale is not None else self.do_rescale
													_UpperCAmelCase  :			str	       =		rescale_factor if rescale_factor is not None else self.rescale_factor
													_UpperCAmelCase  :			Tuple	       =		rescale_offset if rescale_offset is not None else self.rescale_offset
													_UpperCAmelCase  :			str	       =		do_normalize if do_normalize is not None else self.do_normalize
													_UpperCAmelCase  :			Dict	       =		image_mean if image_mean is not None else self.image_mean
													_UpperCAmelCase  :			Tuple	       =		image_std if image_std is not None else self.image_std
													_UpperCAmelCase  :			Tuple	       =		include_top if include_top is not None else self.include_top

													_UpperCAmelCase  :			Optional[int]	       =		size if size is not None else self.size
													_UpperCAmelCase  :			Union[str, Any]	       =		get_size_dict(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		crop_size if crop_size is not None else self.crop_size
													_UpperCAmelCase  :			int	       =		get_size_dict(_lowerCamelCase	,						param_name="crop_size"			)

													_UpperCAmelCase  :			Union[str, Any]	       =		make_list_of_images(_lowerCamelCase			)

													if not valid_images(_lowerCamelCase			):
																				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_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."			)

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

													if do_resize:
																				_UpperCAmelCase  :			Any	       =		[self.resize(image=_lowerCamelCase	,						size=_lowerCamelCase	,						resample=_lowerCamelCase			) for image in images]

													if do_center_crop:
																				_UpperCAmelCase  :			int	       =		[self.center_crop(image=_lowerCamelCase	,						size=_lowerCamelCase			) for image in images]

													if do_rescale:
																				_UpperCAmelCase  :			int	       =		[self.rescale(image=_lowerCamelCase	,						scale=_lowerCamelCase	,						offset=_lowerCamelCase			) for image in images]

													if do_normalize:
																				_UpperCAmelCase  :			str	       =		[self.normalize(image=_lowerCamelCase	,						mean=_lowerCamelCase	,						std=_lowerCamelCase			) for image in images]

													if include_top:
																				_UpperCAmelCase  :			int	       =		[self.normalize(image=_lowerCamelCase	,						mean=[0, 0, 0]	,						std=_lowerCamelCase			) for image in images]

													_UpperCAmelCase  :			str	       =		[to_channel_dimension_format(_lowerCamelCase	,						_lowerCamelCase			) for image in images]

													_UpperCAmelCase  :			Dict	       =		{"pixel_values": images}
													return BatchFeature(data=_lowerCamelCase	,						tensor_type=_lowerCamelCase			)




