




import argparse
import json

import torch

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

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       Optional[int]=1						)					->			Tuple:


      '''simple docstring'''




      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'						)[n_shave_prefix_segments:]						)
      else:
            return ".".join(path.split('.'						)[:n_shave_prefix_segments]						)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       Optional[Any]=0						)					->			Dict:


      '''simple docstring'''




      lowerCamelCase__        =				[]
      for old_item in old_list:
            lowerCamelCase__        =				old_item
            lowerCamelCase__        =				new_item.replace('block.'     ,				'resnets.'						)
            lowerCamelCase__        =				new_item.replace('conv_shorcut'     ,				'conv1'						)
            lowerCamelCase__        =				new_item.replace('in_shortcut'     ,				'conv_shortcut'						)
            lowerCamelCase__        =				new_item.replace('temb_proj'     ,				'time_emb_proj'						)

            lowerCamelCase__        =				shave_segments(lowercase__     ,				n_shave_prefix_segments=lowercase__						)

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

      return mapping

def 			lowerCAmelCase_			(  lowerCamelCase:       List[str]     ,				lowerCamelCase:       Optional[Any]=0     ,				lowerCamelCase:       Dict=False						)					->			Dict:


      '''simple docstring'''




      lowerCamelCase__        =				[]
      for old_item in old_list:
            lowerCamelCase__        =				old_item

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

            lowerCamelCase__        =				new_item.replace('proj_out'     ,				'proj_attn'						)
            lowerCamelCase__        =				new_item.replace('norm'     ,				'group_norm'						)

            lowerCamelCase__        =				shave_segments(lowercase__     ,				n_shave_prefix_segments=lowercase__						)
            mapping.append({'old': old_item, 'new': new_item}						)

      return mapping

def 			lowerCAmelCase_			(  lowerCamelCase:       str     ,				lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       int     ,				lowerCamelCase:       Dict=None     ,				lowerCamelCase:       Union[str, Any]=None     ,				lowerCamelCase:       Optional[Any]=None						)					->			Optional[int]:


      '''simple docstring'''




      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():
                  lowerCamelCase__        =				old_checkpoint[path]
                  lowerCamelCase__        =				old_tensor.shape[0] // 3

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

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

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

                  lowerCamelCase__        =				query.reshape(lowercase__						).squeeze()
                  lowerCamelCase__        =				key.reshape(lowercase__						).squeeze()
                  lowerCamelCase__        =				value.reshape(lowercase__						).squeeze()

      for path in paths:
            lowerCamelCase__        =				path['new']

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            lowerCamelCase__        =				new_path.replace('down.'     ,				'down_blocks.'						)
            lowerCamelCase__        =				new_path.replace('up.'     ,				'up_blocks.'						)

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

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

def 			lowerCAmelCase_			(  lowerCamelCase:       Union[str, Any]     ,				lowerCamelCase:       Tuple						)					->			Dict:


      '''simple docstring'''




      lowerCamelCase__        =				{}

      lowerCamelCase__        =				checkpoint['temb.dense.0.weight']
      lowerCamelCase__        =				checkpoint['temb.dense.0.bias']
      lowerCamelCase__        =				checkpoint['temb.dense.1.weight']
      lowerCamelCase__        =				checkpoint['temb.dense.1.bias']

      lowerCamelCase__        =				checkpoint['norm_out.weight']
      lowerCamelCase__        =				checkpoint['norm_out.bias']

      lowerCamelCase__        =				checkpoint['conv_in.weight']
      lowerCamelCase__        =				checkpoint['conv_in.bias']
      lowerCamelCase__        =				checkpoint['conv_out.weight']
      lowerCamelCase__        =				checkpoint['conv_out.bias']

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'down' in layer}						)
      lowerCamelCase__        =				{
          layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(lowercase__						)
      }

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:2]						) for layer in checkpoint if 'up' in layer}						)
      lowerCamelCase__        =				{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__						):
            lowerCamelCase__        =				(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				checkpoint[
                      f"""down.{i}.downsample.op.weight"""
                  ]
                  lowerCamelCase__        =				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]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      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']						):
                              lowerCamelCase__        =				renew_resnet_paths(blocks[j]						)
                              assign_to_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__						)

            if any('attn' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				2						).split('.'						)[:2]						) for layer in down_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      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']						):
                              lowerCamelCase__        =				renew_attention_paths(attns[j]						)
                              assign_to_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__     ,				config=lowercase__						)

      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_1' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_2' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      lowerCamelCase__        =				renew_resnet_paths(lowercase__						)
      assign_to_checkpoint(
          lowercase__     ,				lowercase__     ,				lowercase__     ,				additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,				)

      lowerCamelCase__        =				renew_resnet_paths(lowercase__						)
      assign_to_checkpoint(
          lowercase__     ,				lowercase__     ,				lowercase__     ,				additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,				)

      lowerCamelCase__        =				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__						):
            lowerCamelCase__        =				num_up_blocks - 1 - i

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

            if any('block' in layer for layer in up_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      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						):
                              lowerCamelCase__        =				{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
                              lowerCamelCase__        =				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]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				2						).split('.'						)[:2]						) for layer in up_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      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						):
                              lowerCamelCase__        =				{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
                              lowerCamelCase__        =				renew_attention_paths(attns[j]						)
                              assign_to_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__     ,				additional_replacements=[replace_indices]						)

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

def 			lowerCAmelCase_			(  lowerCamelCase:       List[Any]     ,				lowerCamelCase:       Optional[int]						)					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				{}

      lowerCamelCase__        =				checkpoint['encoder.norm_out.weight']
      lowerCamelCase__        =				checkpoint['encoder.norm_out.bias']

      lowerCamelCase__        =				checkpoint['encoder.conv_in.weight']
      lowerCamelCase__        =				checkpoint['encoder.conv_in.bias']
      lowerCamelCase__        =				checkpoint['encoder.conv_out.weight']
      lowerCamelCase__        =				checkpoint['encoder.conv_out.bias']

      lowerCamelCase__        =				checkpoint['decoder.norm_out.weight']
      lowerCamelCase__        =				checkpoint['decoder.norm_out.bias']

      lowerCamelCase__        =				checkpoint['decoder.conv_in.weight']
      lowerCamelCase__        =				checkpoint['decoder.conv_in.bias']
      lowerCamelCase__        =				checkpoint['decoder.conv_out.weight']
      lowerCamelCase__        =				checkpoint['decoder.conv_out.bias']

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'down' in layer}						)
      lowerCamelCase__        =				{
          layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(lowercase__						)
      }

      lowerCamelCase__        =				len({'.'.join(layer.split('.'						)[:3]						) for layer in checkpoint if 'up' in layer}						)
      lowerCamelCase__        =				{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__						):
            lowerCamelCase__        =				(i - 1) // (config['layers_per_block'] + 1)

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

            if any('block' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      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']						):
                              lowerCamelCase__        =				renew_resnet_paths(blocks[j]						)
                              assign_to_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__						)

            if any('attn' in layer for layer in down_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				3						).split('.'						)[:3]						) for layer in down_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      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']						):
                              lowerCamelCase__        =				renew_attention_paths(attns[j]						)
                              assign_to_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__     ,				config=lowercase__						)

      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_1' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.block_2' in key]
      lowerCamelCase__        =				[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      lowerCamelCase__        =				renew_resnet_paths(lowercase__						)
      assign_to_checkpoint(
          lowercase__     ,				lowercase__     ,				lowercase__     ,				additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]     ,				)

      lowerCamelCase__        =				renew_resnet_paths(lowercase__						)
      assign_to_checkpoint(
          lowercase__     ,				lowercase__     ,				lowercase__     ,				additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]     ,				)

      lowerCamelCase__        =				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__						):
            lowerCamelCase__        =				num_up_blocks - 1 - i

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

            if any('block' in layer for layer in up_blocks[i]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'block' in layer}						)
                  lowerCamelCase__        =				{
                      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						):
                              lowerCamelCase__        =				{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
                              lowerCamelCase__        =				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]						):
                  lowerCamelCase__        =				len(
                      {'.'.join(shave_segments(lowercase__     ,				3						).split('.'						)[:3]						) for layer in up_blocks[i] if 'attn' in layer}						)
                  lowerCamelCase__        =				{
                      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						):
                              lowerCamelCase__        =				{'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
                              lowerCamelCase__        =				renew_attention_paths(attns[j]						)
                              assign_to_checkpoint(lowercase__     ,				lowercase__     ,				lowercase__     ,				additional_replacements=[replace_indices]						)

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

      return new_checkpoint


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   Dict       =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.""")

      __SCREAMING_SNAKE_CASE    :   int       =parser.parse_args()
      __SCREAMING_SNAKE_CASE    :   List[str]       =torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            __SCREAMING_SNAKE_CASE    :   Union[str, Any]       =json.loads(f.read())

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

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

      if config["_class_name"] == "VQModel":
            __SCREAMING_SNAKE_CASE    :   Union[str, Any]       =VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            __SCREAMING_SNAKE_CASE    :   List[Any]       =AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            __SCREAMING_SNAKE_CASE    :   List[Any]       =UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

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

            __SCREAMING_SNAKE_CASE    :   str       =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


__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["pixel_values"]




     def __init__(    self  :   Optional[Any]	,  A__  :   bool = True	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = PILImageResampling.BILINEAR	,  A__  :   bool = True	,  A__  :   bool = False	,  A__  :   bool = True	,  A__  :   bool = True	,  A__  :   Union[int, float] = 1 / 2_5_5	,  A__  :   bool = True	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  **A__  :   List[str]	,  )				->       None:
           super().__init__(**A__			)

           lowerCamelCase__        =				size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
           if isinstance(A__	,  (tuple, list)			):
                 # The previous feature extractor size parameter was in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)

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




     def  __snake_case   (    self  :   str	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   Optional[Union[str, ChannelDimension]] = None			)				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

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

           if data_format is not None:
                 lowerCamelCase__        =				to_channel_dimension_format(A__	,  A__			)

           return image




     def  __snake_case   (    self  :   Tuple	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   bool = False	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)

           lowerCamelCase__        =				output_width - input_width
           lowerCamelCase__        =				output_height - input_height

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

           lowerCamelCase__        =				delta_height - pad_top
           lowerCamelCase__        =				delta_width - pad_left

           lowerCamelCase__        =				((pad_top, pad_bottom), (pad_left, pad_right))
           return pad(A__	,  A__	,  data_format=A__			)




     def  __snake_case   (    self  :   int	,  *A__  :   Tuple	,  **A__  :   Dict			)				->       List[Any]:
           logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'			)
           return self.pad_image(*A__	,  **A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__  ,		lowerCamelCase__        =				get_image_size(A__			)
           lowerCamelCase__  ,		lowerCamelCase__        =				size['height'], size['width']

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

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

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

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




     def  __snake_case   (    self  :   List[str]	,  A__  :   np.ndarray	,  A__  :   Dict[str, int]	,  A__  :   PILImageResampling = PILImageResampling.BICUBIC	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Any	,  )				->       np.ndarray:
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				min(size['height']	,  size['width']			)
           lowerCamelCase__        =				get_resize_output_image_size(A__	,  size=A__	,  default_to_square=A__			)
           lowerCamelCase__        =				resize(A__	,  size=A__	,  resample=A__	,  data_format=A__	,  **A__			)
           return resized_image




     def  __snake_case   (    self  :   Optional[int]	,  A__  :   np.ndarray	,  A__  :   Union[int, float]	,  A__  :   Optional[Union[str, ChannelDimension]] = None	,  **A__  :   Optional[int]	,  )				->       Tuple:
           return rescale(A__	,  scale=A__	,  data_format=A__	,  **A__			)




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




     def  __snake_case   (    self  :   Union[str, Any]	,  A__  :   ImageInput	,  A__  :   bool = None	,  A__  :   Dict[str, int] = None	,  A__  :   PILImageResampling = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = None	,  A__  :   bool = False	,  A__  :   bool = None	,  A__  :   float = None	,  A__  :   bool = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[float, List[float]]] = None	,  A__  :   Optional[Union[str, TensorType]] = None	,  A__  :   Optional[ChannelDimension] = ChannelDimension.FIRST	,  **A__  :   Any	,  )				->       PIL.Image.Image:
           lowerCamelCase__        =				do_resize if do_resize is not None else self.do_resize
           lowerCamelCase__        =				size if size is not None else self.size
           if isinstance(A__	,  (tuple, list)			):
                 # Previous feature extractor had size in (width, height) format
                 lowerCamelCase__        =				size[::-1]
           lowerCamelCase__        =				get_size_dict(A__			)
           lowerCamelCase__        =				resample if resample is not None else self.resample
           lowerCamelCase__        =				do_thumbnail if do_thumbnail is not None else self.do_thumbnail
           lowerCamelCase__        =				do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
           lowerCamelCase__        =				do_pad if do_pad is not None else self.do_pad
           lowerCamelCase__        =				do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase__        =				rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase__        =				do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase__        =				image_mean if image_mean is not None else self.image_mean
           lowerCamelCase__        =				image_std if image_std is not None else self.image_std

           lowerCamelCase__        =				make_list_of_images(A__			)

           if not valid_images(A__			):
                 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.
           lowerCamelCase__        =				[to_numpy_array(A__			) for image in images]

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

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

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

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

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

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

           lowerCamelCase__        =				[to_channel_dimension_format(A__	,  A__			) for image in images]

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