





import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel

def     a						(      _lowercase   :     Optional[Any] ,						_lowercase   :     int=1       )     ->Dict:
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split("""."""       )[n_shave_prefix_segments:]       )
		else:
				return ".".join(path.split("""."""       )[:n_shave_prefix_segments]       )

def     a						(      _lowercase   :     Any ,						_lowercase   :     Dict=0       )     ->int:
		UpperCamelCase__         =	[]
		for old_item in old_list:
				UpperCamelCase__         =	old_item
				UpperCamelCase__         =	new_item.replace("""block.""" ,						"""resnets."""       )
				UpperCamelCase__         =	new_item.replace("""conv_shorcut""" ,						"""conv1"""       )
				UpperCamelCase__         =	new_item.replace("""in_shortcut""" ,						"""conv_shortcut"""       )
				UpperCamelCase__         =	new_item.replace("""temb_proj""" ,						"""time_emb_proj"""       )

				UpperCamelCase__         =	shave_segments(_lowercase ,						n_shave_prefix_segments=_lowercase       )

				mapping.append({"""old""": old_item, """new""": new_item}       )

		return mapping

def     a						(      _lowercase   :     str ,						_lowercase   :     Dict=0 ,						_lowercase   :     Union[str, Any]=False       )     ->List[str]:
		UpperCamelCase__         =	[]
		for old_item in old_list:
				UpperCamelCase__         =	old_item

				# In `model.mid`, the layer is called `attn`.
				if not in_mid:
						UpperCamelCase__         =	new_item.replace("""attn""" ,						"""attentions"""       )
				UpperCamelCase__         =	new_item.replace(""".k.""" ,						""".key."""       )
				UpperCamelCase__         =	new_item.replace(""".v.""" ,						""".value."""       )
				UpperCamelCase__         =	new_item.replace(""".q.""" ,						""".query."""       )

				UpperCamelCase__         =	new_item.replace("""proj_out""" ,						"""proj_attn"""       )
				UpperCamelCase__         =	new_item.replace("""norm""" ,						"""group_norm"""       )

				UpperCamelCase__         =	shave_segments(_lowercase ,						n_shave_prefix_segments=_lowercase       )
				mapping.append({"""old""": old_item, """new""": new_item}       )

		return mapping

def     a						(      _lowercase   :     Tuple ,						_lowercase   :     Any ,						_lowercase   :     Any ,						_lowercase   :     str=None ,						_lowercase   :     int=None ,						_lowercase   :     Union[str, Any]=None       )     ->Optional[int]:
		assert isinstance(_lowercase ,						_lowercase       ), "Paths should be a list of dicts containing 'old' and 'new' keys."

		if attention_paths_to_split is not None:
				if config is None:
						raise ValueError("""Please specify the config if setting 'attention_paths_to_split' to 'True'."""       )

				for path, path_map in attention_paths_to_split.items():
						UpperCamelCase__         =	old_checkpoint[path]
						UpperCamelCase__         =	old_tensor.shape[0] // 3

						UpperCamelCase__         =	(-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

						UpperCamelCase__         =	old_tensor.shape[0] // config.get("""num_head_channels""" ,						1       ) // 3

						UpperCamelCase__         =	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]       )
						UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__         =	old_tensor.split(channels // num_heads ,						dim=1       )

						UpperCamelCase__         =	query.reshape(_lowercase       ).squeeze()
						UpperCamelCase__         =	key.reshape(_lowercase       ).squeeze()
						UpperCamelCase__         =	value.reshape(_lowercase       ).squeeze()

		for path in paths:
				UpperCamelCase__         =	path["""new"""]

				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				UpperCamelCase__         =	new_path.replace("""down.""" ,						"""down_blocks."""       )
				UpperCamelCase__         =	new_path.replace("""up.""" ,						"""up_blocks."""       )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								UpperCamelCase__         =	new_path.replace(replacement["""old"""] ,						replacement["""new"""]       )

				if "attentions" in new_path:
						UpperCamelCase__         =	old_checkpoint[path["""old"""]].squeeze()
				else:
						UpperCamelCase__         =	old_checkpoint[path["""old"""]]

def     a						(      _lowercase   :     Optional[int] ,						_lowercase   :     Optional[Any]       )     ->List[str]:
		UpperCamelCase__         =	{}

		UpperCamelCase__         =	checkpoint["""temb.dense.0.weight"""]
		UpperCamelCase__         =	checkpoint["""temb.dense.0.bias"""]
		UpperCamelCase__         =	checkpoint["""temb.dense.1.weight"""]
		UpperCamelCase__         =	checkpoint["""temb.dense.1.bias"""]

		UpperCamelCase__         =	checkpoint["""norm_out.weight"""]
		UpperCamelCase__         =	checkpoint["""norm_out.bias"""]

		UpperCamelCase__         =	checkpoint["""conv_in.weight"""]
		UpperCamelCase__         =	checkpoint["""conv_in.bias"""]
		UpperCamelCase__         =	checkpoint["""conv_out.weight"""]
		UpperCamelCase__         =	checkpoint["""conv_out.bias"""]

		UpperCamelCase__         =	len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """down""" in layer}       )
		UpperCamelCase__         =	{
		    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase       )
		}

		UpperCamelCase__         =	len({""".""".join(layer.split("""."""       )[:2]       ) for layer in checkpoint if """up""" in layer}       )
		UpperCamelCase__         =	{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowercase       )}

		for i in range(_lowercase       ):
				UpperCamelCase__         =	(i - 1) // (config["""layers_per_block"""] + 1)

				if any("""downsample""" in layer for layer in down_blocks[i]       ):
						UpperCamelCase__         =	checkpoint[
						    F"""down.{i}.downsample.op.weight"""
						]
						UpperCamelCase__         =	checkpoint[F"""down.{i}.downsample.op.bias"""]
				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
				#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

				if any("""block""" in layer for layer in down_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						2       ).split("""."""       )[:2]       ) for layer in down_blocks[i] if """block""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
						    for layer_id in range(_lowercase       )
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""]       ):
										UpperCamelCase__         =	renew_resnet_paths(blocks[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase       )

				if any("""attn""" in layer for layer in down_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						2       ).split("""."""       )[:2]       ) for layer in down_blocks[i] if """attn""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
						    for layer_id in range(_lowercase       )
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""]       ):
										UpperCamelCase__         =	renew_attention_paths(attns[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase ,						config=_lowercase       )

		UpperCamelCase__         =	[key for key in checkpoint if """mid.block_1""" in key]
		UpperCamelCase__         =	[key for key in checkpoint if """mid.block_2""" in key]
		UpperCamelCase__         =	[key for key in checkpoint if """mid.attn_1""" in key]

		# Mid new 2
		UpperCamelCase__         =	renew_resnet_paths(_lowercase       )
		assign_to_checkpoint(
		    _lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}] ,						)

		UpperCamelCase__         =	renew_resnet_paths(_lowercase       )
		assign_to_checkpoint(
		    _lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}] ,						)

		UpperCamelCase__         =	renew_attention_paths(_lowercase ,						in_mid=_lowercase       )
		assign_to_checkpoint(
		    _lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}] ,						)

		for i in range(_lowercase       ):
				UpperCamelCase__         =	num_up_blocks - 1 - i

				if any("""upsample""" in layer for layer in up_blocks[i]       ):
						UpperCamelCase__         =	checkpoint[
						    F"""up.{i}.upsample.conv.weight"""
						]
						UpperCamelCase__         =	checkpoint[F"""up.{i}.upsample.conv.bias"""]

				if any("""block""" in layer for layer in up_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						2       ).split("""."""       )[:2]       ) for layer in up_blocks[i] if """block""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_lowercase       )
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""] + 1       ):
										UpperCamelCase__         =	{"""old""": F"""up_blocks.{i}""", """new""": F"""up_blocks.{block_id}"""}
										UpperCamelCase__         =	renew_resnet_paths(blocks[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[replace_indices]       )

				if any("""attn""" in layer for layer in up_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						2       ).split("""."""       )[:2]       ) for layer in up_blocks[i] if """attn""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_lowercase       )
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""] + 1       ):
										UpperCamelCase__         =	{"""old""": F"""up_blocks.{i}""", """new""": F"""up_blocks.{block_id}"""}
										UpperCamelCase__         =	renew_attention_paths(attns[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[replace_indices]       )

		UpperCamelCase__         =	{k.replace("""mid_new_2""" ,						"""mid_block"""       ): v for k, v in new_checkpoint.items()}
		return new_checkpoint

def     a						(      _lowercase   :     Tuple ,						_lowercase   :     str       )     ->List[str]:
		UpperCamelCase__         =	{}

		UpperCamelCase__         =	checkpoint["""encoder.norm_out.weight"""]
		UpperCamelCase__         =	checkpoint["""encoder.norm_out.bias"""]

		UpperCamelCase__         =	checkpoint["""encoder.conv_in.weight"""]
		UpperCamelCase__         =	checkpoint["""encoder.conv_in.bias"""]
		UpperCamelCase__         =	checkpoint["""encoder.conv_out.weight"""]
		UpperCamelCase__         =	checkpoint["""encoder.conv_out.bias"""]

		UpperCamelCase__         =	checkpoint["""decoder.norm_out.weight"""]
		UpperCamelCase__         =	checkpoint["""decoder.norm_out.bias"""]

		UpperCamelCase__         =	checkpoint["""decoder.conv_in.weight"""]
		UpperCamelCase__         =	checkpoint["""decoder.conv_in.bias"""]
		UpperCamelCase__         =	checkpoint["""decoder.conv_out.weight"""]
		UpperCamelCase__         =	checkpoint["""decoder.conv_out.bias"""]

		UpperCamelCase__         =	len({""".""".join(layer.split("""."""       )[:3]       ) for layer in checkpoint if """down""" in layer}       )
		UpperCamelCase__         =	{
		    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(_lowercase       )
		}

		UpperCamelCase__         =	len({""".""".join(layer.split("""."""       )[:3]       ) for layer in checkpoint if """up""" in layer}       )
		UpperCamelCase__         =	{layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(_lowercase       )}

		for i in range(_lowercase       ):
				UpperCamelCase__         =	(i - 1) // (config["""layers_per_block"""] + 1)

				if any("""downsample""" in layer for layer in down_blocks[i]       ):
						UpperCamelCase__         =	checkpoint[
						    F"""encoder.down.{i}.downsample.conv.weight"""
						]
						UpperCamelCase__         =	checkpoint[
						    F"""encoder.down.{i}.downsample.conv.bias"""
						]

				if any("""block""" in layer for layer in down_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						3       ).split("""."""       )[:3]       ) for layer in down_blocks[i] if """block""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
						    for layer_id in range(_lowercase       )
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""]       ):
										UpperCamelCase__         =	renew_resnet_paths(blocks[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase       )

				if any("""attn""" in layer for layer in down_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						3       ).split("""."""       )[:3]       ) for layer in down_blocks[i] if """attn""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
						    for layer_id in range(_lowercase       )
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""]       ):
										UpperCamelCase__         =	renew_attention_paths(attns[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase ,						config=_lowercase       )

		UpperCamelCase__         =	[key for key in checkpoint if """mid.block_1""" in key]
		UpperCamelCase__         =	[key for key in checkpoint if """mid.block_2""" in key]
		UpperCamelCase__         =	[key for key in checkpoint if """mid.attn_1""" in key]

		# Mid new 2
		UpperCamelCase__         =	renew_resnet_paths(_lowercase       )
		assign_to_checkpoint(
		    _lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}] ,						)

		UpperCamelCase__         =	renew_resnet_paths(_lowercase       )
		assign_to_checkpoint(
		    _lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}] ,						)

		UpperCamelCase__         =	renew_attention_paths(_lowercase ,						in_mid=_lowercase       )
		assign_to_checkpoint(
		    _lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}] ,						)

		for i in range(_lowercase       ):
				UpperCamelCase__         =	num_up_blocks - 1 - i

				if any("""upsample""" in layer for layer in up_blocks[i]       ):
						UpperCamelCase__         =	checkpoint[
						    F"""decoder.up.{i}.upsample.conv.weight"""
						]
						UpperCamelCase__         =	checkpoint[
						    F"""decoder.up.{i}.upsample.conv.bias"""
						]

				if any("""block""" in layer for layer in up_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						3       ).split("""."""       )[:3]       ) for layer in up_blocks[i] if """block""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(_lowercase       )
						}

						if num_blocks > 0:
								for j in range(config["""layers_per_block"""] + 1       ):
										UpperCamelCase__         =	{"""old""": F"""up_blocks.{i}""", """new""": F"""up_blocks.{block_id}"""}
										UpperCamelCase__         =	renew_resnet_paths(blocks[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[replace_indices]       )

				if any("""attn""" in layer for layer in up_blocks[i]       ):
						UpperCamelCase__         =	len(
						    {""".""".join(shave_segments(_lowercase ,						3       ).split("""."""       )[:3]       ) for layer in up_blocks[i] if """attn""" in layer}       )
						UpperCamelCase__         =	{
						    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(_lowercase       )
						}

						if num_attn > 0:
								for j in range(config["""layers_per_block"""] + 1       ):
										UpperCamelCase__         =	{"""old""": F"""up_blocks.{i}""", """new""": F"""up_blocks.{block_id}"""}
										UpperCamelCase__         =	renew_attention_paths(attns[j]       )
										assign_to_checkpoint(_lowercase ,						_lowercase ,						_lowercase ,						additional_replacements=[replace_indices]       )

		UpperCamelCase__         =	{k.replace("""mid_new_2""" ,						"""mid_block"""       ): v for k, v in new_checkpoint.items()}
		UpperCamelCase__         =	checkpoint["""quant_conv.weight"""]
		UpperCamelCase__         =	checkpoint["""quant_conv.bias"""]
		if "quantize.embedding.weight" in checkpoint:
				UpperCamelCase__         =	checkpoint["""quantize.embedding.weight"""]
		UpperCamelCase__         =	checkpoint["""post_quant_conv.weight"""]
		UpperCamelCase__         =	checkpoint["""post_quant_conv.bias"""]

		return new_checkpoint


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

				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
				)

				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the architecture.",
				)

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

				__lowerCAmelCase     =			parser.parse_args()
				__lowerCAmelCase     =			torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								__lowerCAmelCase     =			json.loads(f.read())

				# unet case
				__lowerCAmelCase     =			{key.split(".")[0] for key in checkpoint.keys()}
				if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
								__lowerCAmelCase     =			convert_vq_autoenc_checkpoint(checkpoint, config)
				else:
								__lowerCAmelCase     =			convert_ddpm_checkpoint(checkpoint, config)

				if "ddpm" in config:
								del config["ddpm"]

				if config["_class_name"] == "VQModel":
								__lowerCAmelCase     =			VQModel(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				elif config["_class_name"] == "AutoencoderKL":
								__lowerCAmelCase     =			AutoencoderKL(**config)
								model.load_state_dict(converted_checkpoint)
								model.save_pretrained(args.dump_path)
				else:
								__lowerCAmelCase     =			UNetaDModel(**config)
								model.load_state_dict(converted_checkpoint)

								__lowerCAmelCase     =			DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								__lowerCAmelCase     =			DDPMPipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)












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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase     =			logging.get_logger(__name__)


if is_vision_available():
				import PIL




class   _UpperCAmelCase	(   _lowercase					):
					__A          =   ["pixel_values"]




					def __init__(						self    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = PILImageResampling.BILINEAR    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = 1 / 255    ,      __lowerCAmelCase = True    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  None:
							super().__init__(**__lowerCAmelCase       )

							UpperCamelCase__         =	size if size is not None else {"""height""": 2_560, """width""": 1_920}
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# The previous feature extractor size parameter was in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )

							UpperCamelCase__         =	do_resize
							UpperCamelCase__         =	size
							UpperCamelCase__         =	resample
							UpperCamelCase__         =	do_thumbnail
							UpperCamelCase__         =	do_align_long_axis
							UpperCamelCase__         =	do_pad
							UpperCamelCase__         =	do_rescale
							UpperCamelCase__         =	rescale_factor
							UpperCamelCase__         =	do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							UpperCamelCase__         =	image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None       )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							if (output_width < output_height and input_width > input_height) or (
							    output_width > output_height and input_width < input_height
							):
									UpperCamelCase__         =	np.rotaa(__lowerCAmelCase    ,      3       )

							if data_format is not None:
									UpperCamelCase__         =	to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       )

							return image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )

							UpperCamelCase__         =	output_width - input_width
							UpperCamelCase__         =	output_height - input_height

							if random_padding:
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_height + 1       )
									UpperCamelCase__         =	np.random.randint(low=0    ,      high=delta_width + 1       )
							else:
									UpperCamelCase__         =	delta_height // 2
									UpperCamelCase__         =	delta_width // 2

							UpperCamelCase__         =	delta_height - pad_top
							UpperCamelCase__         =	delta_width - pad_left

							UpperCamelCase__         =	((pad_top, pad_bottom), (pad_left, pad_right))
							return pad(__lowerCAmelCase    ,      __lowerCAmelCase    ,      data_format=__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      *__lowerCAmelCase    ,      **__lowerCAmelCase       )						->  List[Any]:
							logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""       )
							return self.pad_image(*__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__  ,			UpperCamelCase__         =	get_image_size(__lowerCAmelCase       )
							UpperCamelCase__  ,			UpperCamelCase__         =	size["""height"""], size["""width"""]

							# We always resize to the smallest of either the input or output size.
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	min(__lowerCAmelCase    ,      __lowerCAmelCase       )

							if height == input_height and width == input_width:
									return image

							if input_height > input_width:
									UpperCamelCase__         =	int(input_width * height / input_height       )
							elif input_width > input_height:
									UpperCamelCase__         =	int(input_height * width / input_width       )

							return resize(
							    __lowerCAmelCase    ,      size=(height, width)    ,      resample=__lowerCAmelCase    ,      reducing_gap=2.0    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = PILImageResampling.BICUBIC    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  np.ndarray:
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	min(size["""height"""]    ,      size["""width"""]       )
							UpperCamelCase__         =	get_resize_output_image_size(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      default_to_square=__lowerCAmelCase       )
							UpperCamelCase__         =	resize(__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )
							return resized_image




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      **__lowerCAmelCase    ,      )						->  List[str]:
							return rescale(__lowerCAmelCase    ,      scale=__lowerCAmelCase    ,      data_format=__lowerCAmelCase    ,      **__lowerCAmelCase       )




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




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = False    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = None    ,      __lowerCAmelCase = ChannelDimension.FIRST    ,      **__lowerCAmelCase    ,      )						->  PIL.Image.Image:
							UpperCamelCase__         =	do_resize if do_resize is not None else self.do_resize
							UpperCamelCase__         =	size if size is not None else self.size
							if isinstance(__lowerCAmelCase    ,      (tuple, list)       ):
									# Previous feature extractor had size in (width, height) format
									UpperCamelCase__         =	size[::-1]
							UpperCamelCase__         =	get_size_dict(__lowerCAmelCase       )
							UpperCamelCase__         =	resample if resample is not None else self.resample
							UpperCamelCase__         =	do_thumbnail if do_thumbnail is not None else self.do_thumbnail
							UpperCamelCase__         =	do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
							UpperCamelCase__         =	do_pad if do_pad is not None else self.do_pad
							UpperCamelCase__         =	do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase__         =	rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase__         =	do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase__         =	image_mean if image_mean is not None else self.image_mean
							UpperCamelCase__         =	image_std if image_std is not None else self.image_std

							UpperCamelCase__         =	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:
									raise ValueError("""Size must be specified if do_resize is True."""       )

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

							if do_pad and size is None:
									raise ValueError("""Size must be specified if do_pad 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__         =	[to_numpy_array(__lowerCAmelCase       ) for image in images]

							if do_align_long_axis:
									UpperCamelCase__         =	[self.align_long_axis(__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_resize:
									UpperCamelCase__         =	[self.resize(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      resample=__lowerCAmelCase       ) for image in images]

							if do_thumbnail:
									UpperCamelCase__         =	[self.thumbnail(image=__lowerCAmelCase    ,      size=__lowerCAmelCase       ) for image in images]

							if do_pad:
									UpperCamelCase__         =	[self.pad_image(image=__lowerCAmelCase    ,      size=__lowerCAmelCase    ,      random_padding=__lowerCAmelCase       ) for image in images]

							if do_rescale:
									UpperCamelCase__         =	[self.rescale(image=__lowerCAmelCase    ,      scale=__lowerCAmelCase       ) for image in images]

							if do_normalize:
									UpperCamelCase__         =	[self.normalize(image=__lowerCAmelCase    ,      mean=__lowerCAmelCase    ,      std=__lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	[to_channel_dimension_format(__lowerCAmelCase    ,      __lowerCAmelCase       ) for image in images]

							UpperCamelCase__         =	{"""pixel_values""": images}
							return BatchFeature(data=__lowerCAmelCase    ,      tensor_type=__lowerCAmelCase       )





