code: 
  
  
  
  
  
  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)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  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       )
  
  
  
  
  
  