






import argparse
import json

import torch

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




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=1				)  ->      List[str]:





 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   (							lowerCamelCase__ ,			lowerCamelCase__=0				)  ->      Optional[Any]:
 lowerCamelCase_							:    Tuple							=     []
 for old_item in old_list:
  lowerCamelCase_							:    Optional[Any]							=     old_item
  lowerCamelCase_							:    Tuple							=     new_item.replace('block.' ,			'resnets.'				)
  lowerCamelCase_							:    Optional[Any]							=     new_item.replace('conv_shorcut' ,			'conv1'				)
  lowerCamelCase_							:    List[Any]							=     new_item.replace('in_shortcut' ,			'conv_shortcut'				)
  lowerCamelCase_							:    Tuple							=     new_item.replace('temb_proj' ,			'time_emb_proj'				)

  lowerCamelCase_							:    List[Any]							=     shave_segments(lowerCamelCase_ ,			n_shave_prefix_segments=lowerCamelCase_				)

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

 return mapping




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__=0 ,			lowerCamelCase__=False				)  ->      Optional[Any]:
 lowerCamelCase_							:    Optional[int]							=     []
 for old_item in old_list:
  lowerCamelCase_							:    Dict							=     old_item

  # In `model.mid`, the layer is called `attn`.
  if not in_mid:
   lowerCamelCase_							:    Union[str, Any]							=     new_item.replace('attn' ,			'attentions'				)
  lowerCamelCase_							:    Optional[int]							=     new_item.replace('.k.' ,			'.key.'				)
  lowerCamelCase_							:    Tuple							=     new_item.replace('.v.' ,			'.value.'				)
  lowerCamelCase_							:    Optional[Any]							=     new_item.replace('.q.' ,			'.query.'				)

  lowerCamelCase_							:    str							=     new_item.replace('proj_out' ,			'proj_attn'				)
  lowerCamelCase_							:    Optional[int]							=     new_item.replace('norm' ,			'group_norm'				)

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

 return mapping




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__=None ,			lowerCamelCase__=None ,			lowerCamelCase__=None				)  ->      Any:
 assert isinstance(lowerCamelCase_ ,			lowerCamelCase_				), "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_							:    Any							=     old_checkpoint[path]
   lowerCamelCase_							:    Tuple							=     old_tensor.shape[0] // 3

   lowerCamelCase_							:    List[Any]							=     (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

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

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

   lowerCamelCase_							:    Union[str, Any]							=     query.reshape(lowerCamelCase_				).squeeze()
   lowerCamelCase_							:    List[Any]							=     key.reshape(lowerCamelCase_				).squeeze()
   lowerCamelCase_							:    List[str]							=     value.reshape(lowerCamelCase_				).squeeze()

 for path in paths:
  lowerCamelCase_							:    List[str]							=     path['new']

  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  lowerCamelCase_							:    Optional[int]							=     new_path.replace('down.' ,			'down_blocks.'				)
  lowerCamelCase_							:    Any							=     new_path.replace('up.' ,			'up_blocks.'				)

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

  if "attentions" in new_path:
   lowerCamelCase_							:    int							=     old_checkpoint[path['old']].squeeze()
  else:
   lowerCamelCase_							:    List[Any]							=     old_checkpoint[path['old']]




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      Optional[int]:
 lowerCamelCase_							:    str							=     {}

 lowerCamelCase_							:    str							=     checkpoint['temb.dense.0.weight']
 lowerCamelCase_							:    int							=     checkpoint['temb.dense.0.bias']
 lowerCamelCase_							:    int							=     checkpoint['temb.dense.1.weight']
 lowerCamelCase_							:    Dict							=     checkpoint['temb.dense.1.bias']

 lowerCamelCase_							:    Optional[Any]							=     checkpoint['norm_out.weight']
 lowerCamelCase_							:    Any							=     checkpoint['norm_out.bias']

 lowerCamelCase_							:    str							=     checkpoint['conv_in.weight']
 lowerCamelCase_							:    Optional[Any]							=     checkpoint['conv_in.bias']
 lowerCamelCase_							:    Tuple							=     checkpoint['conv_out.weight']
 lowerCamelCase_							:    Optional[int]							=     checkpoint['conv_out.bias']

 lowerCamelCase_							:    Tuple							=     len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'down' in layer}				)
 lowerCamelCase_							:    Any							=     {
     layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)
 }

 lowerCamelCase_							:    int							=     len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'up' in layer}				)
 lowerCamelCase_							:    Union[str, Any]							=     {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)}

 for i in range(lowerCamelCase_				):
  lowerCamelCase_							:    Optional[int]							=     (i - 1) // (config['layers_per_block'] + 1)

  if any('downsample' in layer for layer in down_blocks[i]				):
   lowerCamelCase_							:    Optional[Any]							=     checkpoint[
       F'down.{i}.downsample.op.weight'
   ]
   lowerCamelCase_							:    Any							=     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_							:    str							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'block' in layer}				)
   lowerCamelCase_							:    Dict							=     {
       layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
       for layer_id in range(lowerCamelCase_				)
   }

   if num_blocks > 0:
    for j in range(config['layers_per_block']				):
     lowerCamelCase_							:    Optional[Any]							=     renew_resnet_paths(blocks[j]				)
     assign_to_checkpoint(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_				)

  if any('attn' in layer for layer in down_blocks[i]				):
   lowerCamelCase_							:    Tuple							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'attn' in layer}				)
   lowerCamelCase_							:    str							=     {
       layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
       for layer_id in range(lowerCamelCase_				)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block']				):
     lowerCamelCase_							:    str							=     renew_attention_paths(attns[j]				)
     assign_to_checkpoint(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_ ,			config=lowerCamelCase_				)

 lowerCamelCase_							:    Any							=     [key for key in checkpoint if 'mid.block_1' in key]
 lowerCamelCase_							:    str							=     [key for key in checkpoint if 'mid.block_2' in key]
 lowerCamelCase_							:    Dict							=     [key for key in checkpoint if 'mid.attn_1' in key]

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

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

 lowerCamelCase_							:    int							=     renew_attention_paths(lowerCamelCase_ ,			in_mid=lowerCamelCase_				)
 assign_to_checkpoint(
     lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_ ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}] ,			)

 for i in range(lowerCamelCase_				):
  lowerCamelCase_							:    int							=     num_up_blocks - 1 - i

  if any('upsample' in layer for layer in up_blocks[i]				):
   lowerCamelCase_							:    str							=     checkpoint[
       F'up.{i}.upsample.conv.weight'
   ]
   lowerCamelCase_							:    Tuple							=     checkpoint[F'up.{i}.upsample.conv.bias']

  if any('block' in layer for layer in up_blocks[i]				):
   lowerCamelCase_							:    Optional[Any]							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'block' in layer}				)
   lowerCamelCase_							:    Dict							=     {
       layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)
   }

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

  if any('attn' in layer for layer in up_blocks[i]				):
   lowerCamelCase_							:    List[Any]							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'attn' in layer}				)
   lowerCamelCase_							:    Dict							=     {
       layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)
   }

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

 lowerCamelCase_							:    List[str]							=     {k.replace('mid_new_2' ,			'mid_block'				): v for k, v in new_checkpoint.items()}
 return new_checkpoint




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 lowerCamelCase_							:    Any							=     {}

 lowerCamelCase_							:    int							=     checkpoint['encoder.norm_out.weight']
 lowerCamelCase_							:    List[Any]							=     checkpoint['encoder.norm_out.bias']

 lowerCamelCase_							:    Tuple							=     checkpoint['encoder.conv_in.weight']
 lowerCamelCase_							:    Optional[int]							=     checkpoint['encoder.conv_in.bias']
 lowerCamelCase_							:    Union[str, Any]							=     checkpoint['encoder.conv_out.weight']
 lowerCamelCase_							:    Union[str, Any]							=     checkpoint['encoder.conv_out.bias']

 lowerCamelCase_							:    Any							=     checkpoint['decoder.norm_out.weight']
 lowerCamelCase_							:    List[str]							=     checkpoint['decoder.norm_out.bias']

 lowerCamelCase_							:    Union[str, Any]							=     checkpoint['decoder.conv_in.weight']
 lowerCamelCase_							:    Dict							=     checkpoint['decoder.conv_in.bias']
 lowerCamelCase_							:    Optional[int]							=     checkpoint['decoder.conv_out.weight']
 lowerCamelCase_							:    Optional[int]							=     checkpoint['decoder.conv_out.bias']

 lowerCamelCase_							:    str							=     len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'down' in layer}				)
 lowerCamelCase_							:    str							=     {
     layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)
 }

 lowerCamelCase_							:    Dict							=     len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'up' in layer}				)
 lowerCamelCase_							:    Optional[int]							=     {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)}

 for i in range(lowerCamelCase_				):
  lowerCamelCase_							:    List[Any]							=     (i - 1) // (config['layers_per_block'] + 1)

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

  if any('block' in layer for layer in down_blocks[i]				):
   lowerCamelCase_							:    int							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'block' in layer}				)
   lowerCamelCase_							:    Any							=     {
       layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
       for layer_id in range(lowerCamelCase_				)
   }

   if num_blocks > 0:
    for j in range(config['layers_per_block']				):
     lowerCamelCase_							:    Optional[Any]							=     renew_resnet_paths(blocks[j]				)
     assign_to_checkpoint(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_				)

  if any('attn' in layer for layer in down_blocks[i]				):
   lowerCamelCase_							:    str							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'attn' in layer}				)
   lowerCamelCase_							:    int							=     {
       layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
       for layer_id in range(lowerCamelCase_				)
   }

   if num_attn > 0:
    for j in range(config['layers_per_block']				):
     lowerCamelCase_							:    int							=     renew_attention_paths(attns[j]				)
     assign_to_checkpoint(lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_ ,			config=lowerCamelCase_				)

 lowerCamelCase_							:    Any							=     [key for key in checkpoint if 'mid.block_1' in key]
 lowerCamelCase_							:    Any							=     [key for key in checkpoint if 'mid.block_2' in key]
 lowerCamelCase_							:    List[str]							=     [key for key in checkpoint if 'mid.attn_1' in key]

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

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

 lowerCamelCase_							:    Any							=     renew_attention_paths(lowerCamelCase_ ,			in_mid=lowerCamelCase_				)
 assign_to_checkpoint(
     lowerCamelCase_ ,			lowerCamelCase_ ,			lowerCamelCase_ ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}] ,			)

 for i in range(lowerCamelCase_				):
  lowerCamelCase_							:    List[Any]							=     num_up_blocks - 1 - i

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

  if any('block' in layer for layer in up_blocks[i]				):
   lowerCamelCase_							:    List[str]							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'block' in layer}				)
   lowerCamelCase_							:    List[Any]							=     {
       layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)
   }

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

  if any('attn' in layer for layer in up_blocks[i]				):
   lowerCamelCase_							:    int							=     len(
       {'.'.join(shave_segments(lowerCamelCase_ ,			3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'attn' in layer}				)
   lowerCamelCase_							:    Optional[Any]							=     {
       layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(lowerCamelCase_				)
   }

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

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

 return new_checkpoint


if __name__ == "__main__":
       UpperCamelCase											=      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.''')

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

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

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

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

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

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

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

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViltImageProcessor

class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def __init__(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=7	,       __SCREAMING_SNAKE_CASE     :					Dict=3	,       __SCREAMING_SNAKE_CASE     :					Any=18	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=30	,       __SCREAMING_SNAKE_CASE     :					int=400	,       __SCREAMING_SNAKE_CASE     :					int=True	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=None	,       __SCREAMING_SNAKE_CASE     :					Any=2	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					int=[0.5, 0.5, 0.5]	,       __SCREAMING_SNAKE_CASE     :					List[str]=[0.5, 0.5, 0.5]	,       ):






					"""simple docstring"""
					_snake_case														=   size if size is not None else {"shortest_edge": 30}
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   num_channels
					_snake_case														=   image_size
					_snake_case														=   min_resolution
					_snake_case														=   max_resolution
					_snake_case														=   do_resize
					_snake_case														=   size
					_snake_case														=   size_divisor
					_snake_case														=   do_normalize
					_snake_case														=   image_mean
					_snake_case														=   image_std



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}







		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






					"""simple docstring"""
					if not batched:
								_snake_case														=   self.size["shortest_edge"]
								_snake_case														=   image_inputs[0]
								if isinstance(__SCREAMING_SNAKE_CASE	,       Image.Image ):
											_snake_case							,			_snake_case														=   image.size
								else:
											_snake_case							,			_snake_case														=   image.shape[1], image.shape[2]
								_snake_case														=   size / min(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
								if h < w:
											_snake_case							,			_snake_case														=   size, scale * w
								else:
											_snake_case							,			_snake_case														=   scale * h, size

								_snake_case														=   int((1_333 / 800) * size )
								if max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) > max_size:
											_snake_case														=   max_size / max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   newh * scale
											_snake_case														=   neww * scale

								_snake_case							,			_snake_case														=   int(newh + 0.5 ), int(neww + 0.5 )
								_snake_case							,			_snake_case														=   (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								_snake_case														=   []
								for image in image_inputs:
											_snake_case							,			_snake_case														=   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   ViltImageProcessor if is_vision_available() else None



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   ViltImageProcessingTester(self )



		@property
		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_mean" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_std" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_normalize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_resize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size_divisor" ) )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 30} )

					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict	,       size=42 )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 42} )



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					pass



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       Image.Image )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       numpify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       np.ndarray )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )







		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       torchify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       torch.Tensor )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )

