
"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__        =					logging.get_logger(__name__)
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[int]						)	->   Any:
  _snake_case   	=			SwinConfig(
      embed_dim=1_92					,   depths=(2, 2, 18, 2)					,   num_heads=(6, 12, 24, 48)					,   window_size=12					,   out_features=['''stage2''', '''stage3''', '''stage4''']					,   )

  _snake_case   	=			DetaConfig(
      backbone_config=__lowerCamelCase					,   num_queries=9_00					,   encoder_ffn_dim=20_48					,   decoder_ffn_dim=20_48					,   num_feature_levels=5					,   assign_first_stage=__lowerCamelCase					,   with_box_refine=__lowerCamelCase					,   two_stage=__lowerCamelCase					,   )

  # set labels
  _snake_case   	=			'''huggingface/label-files'''
  if "o365" in model_name:
    _snake_case   	=			3_66
    _snake_case   	=			'''object365-id2label.json'''
  else:
    _snake_case   	=			91
    _snake_case   	=			'''coco-detection-id2label.json'''

  _snake_case   	=			num_labels
  _snake_case   	=			json.load(open(cached_download(hf_hub_url(__lowerCamelCase					,   __lowerCamelCase					,   repo_type='''dataset'''						)						)					,   '''r'''						)						)
  _snake_case   	=			{int(__lowerCamelCase						): v for k, v in idalabel.items()}
  _snake_case   	=			idalabel
  _snake_case   	=			{v: k for k, v in idalabel.items()}

  return config
def    _UpperCAmelCase (					__lowerCamelCase  :			List[Any]						)	->   Dict:
  _snake_case   	=			[]

  # stem
  # fmt: off
  rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')						)
  rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')						)
  rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')						)
  rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')						)
  # stages
  for i in range(len(config.backbone_config.depths						)						):
    for j in range(config.backbone_config.depths[i]						):
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')						)

    if i < 3:
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')						)
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')						)

  rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')						)
  rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')						)
  rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')						)
  rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')						)
  rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')						)
  rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')						)

  # transformer encoder
  for i in range(config.encoder_layers						):
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')						)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')						)

  # transformer decoder
  for i in range(config.decoder_layers						):
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')						)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')						)

  # fmt: on

  return rename_keys
def    _UpperCAmelCase (					__lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			str					,   __lowerCamelCase  :			Optional[Any]						)	->   Any:
  _snake_case   	=			dct.pop(__lowerCamelCase						)
  _snake_case   	=			val
def    _UpperCAmelCase (					__lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			str						)	->   Optional[Any]:
  _snake_case   	=			[int(backbone_config.embed_dim * 2**i						) for i in range(len(backbone_config.depths						)						)]
  for i in range(len(backbone_config.depths						)						):
    _snake_case   	=			num_features[i]
    for j in range(backbone_config.depths[i]						):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      _snake_case   	=			state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''						)
      _snake_case   	=			state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''						)
      # next, add query, keys and values (in that order) to the state dict
      _snake_case   	=			in_proj_weight[:dim, :]
      _snake_case   	=			in_proj_bias[: dim]
      _snake_case   	=			in_proj_weight[
          dim : dim * 2, :
      ]
      _snake_case   	=			in_proj_bias[
          dim : dim * 2
      ]
      _snake_case   	=			in_proj_weight[
          -dim :, :
      ]
      _snake_case   	=			in_proj_bias[-dim :]
      # fmt: on
def    _UpperCAmelCase (					__lowerCamelCase  :			Dict					,   __lowerCamelCase  :			Any						)	->   Tuple:
  _snake_case   	=			config.d_model
  for i in range(config.decoder_layers						):
    # read in weights + bias of input projection layer of self-attention
    _snake_case   	=			state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''						)
    _snake_case   	=			state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''						)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case   	=			in_proj_weight[:hidden_size, :]
    _snake_case   	=			in_proj_bias[:hidden_size]
    _snake_case   	=			in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _snake_case   	=			in_proj_bias[hidden_size : hidden_size * 2]
    _snake_case   	=			in_proj_weight[-hidden_size:, :]
    _snake_case   	=			in_proj_bias[-hidden_size:]
def    _UpperCAmelCase (					)	->   Optional[Any]:
  _snake_case   	=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case   	=			Image.open(requests.get(__lowerCamelCase					,   stream=__lowerCamelCase						).raw						)

  return im
@torch.no_grad()
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			int					,   __lowerCamelCase  :			Any						)	->   Optional[int]:
  _snake_case   	=			get_deta_config(__lowerCamelCase						)

  # load original state dict
  if model_name == "deta-swin-large":
    _snake_case   	=			hf_hub_download(repo_id='''nielsr/deta-checkpoints'''					,   filename='''adet_swin_ft.pth'''						)
  elif model_name == "deta-swin-large-o365":
    _snake_case   	=			hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''					,   filename='''deta_swin_pt_o365.pth'''						)
  else:
    raise ValueError(f'''Model name {model_name} not supported'''						)

  _snake_case   	=			torch.load(__lowerCamelCase					,   map_location='''cpu'''						)['''model''']

  # original state dict
  for name, param in state_dict.items():
    print(__lowerCamelCase					,   param.shape						)

  # rename keys
  _snake_case   	=			create_rename_keys(__lowerCamelCase						)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase						)
  read_in_swin_q_k_v(__lowerCamelCase					,   config.backbone_config						)
  read_in_decoder_q_k_v(__lowerCamelCase					,   __lowerCamelCase						)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      _snake_case   	=			state_dict.pop(__lowerCamelCase						)
      _snake_case   	=			val
    if "input_proj" in key:
      _snake_case   	=			state_dict.pop(__lowerCamelCase						)
      _snake_case   	=			val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _snake_case   	=			state_dict.pop(__lowerCamelCase						)
      _snake_case   	=			val

       # finally, create HuggingFace model and load state dict
  _snake_case   	=			DetaForObjectDetection(__lowerCamelCase						)
  model.load_state_dict(__lowerCamelCase						)
  model.eval()

  _snake_case   	=			'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(__lowerCamelCase						)

  # load image processor
  _snake_case   	=			DetaImageProcessor(format='''coco_detection'''						)

  # verify our conversion on image
  _snake_case   	=			prepare_img()
  _snake_case   	=			processor(images=__lowerCamelCase					,   return_tensors='''pt'''						)
  _snake_case   	=			encoding['''pixel_values''']
  _snake_case   	=			model(pixel_values.to(__lowerCamelCase						)						)

  # verify logits
  print('''Logits:'''					,   outputs.logits[0, :3, :3]						)
  print('''Boxes:'''					,   outputs.pred_boxes[0, :3, :3]						)
  if model_name == "deta-swin-large":
    _snake_case   	=			torch.tensor(
        [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]						)
    _snake_case   	=			torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]						)
  elif model_name == "deta-swin-large-o365":
    _snake_case   	=			torch.tensor(
        [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]						)
    _snake_case   	=			torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]						)
  assert torch.allclose(outputs.logits[0, :3, :3]					,   expected_logits.to(__lowerCamelCase						)					,   atol=1E-4						)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,   expected_boxes.to(__lowerCamelCase						)					,   atol=1E-4						)
  print('''Everything ok!'''						)

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''						)
    Path(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)
    model.save_pretrained(__lowerCamelCase						)
    processor.save_pretrained(__lowerCamelCase						)

  # Push to hub
  if push_to_hub:
    print('''Pushing model and processor to hub...'''						)
    model.push_to_hub(f'''jozhang97/{model_name}'''						)
    processor.push_to_hub(f'''jozhang97/{model_name}'''						)


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

 parser.add_argument(
     '--model_name',
     type=str,
     default='deta-swin-large',
     choices=['deta-swin-large', 'deta-swin-large-o365'],
     help='Name of the model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     help='Path to the folder to output PyTorch model.',
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )
 UpperCAmelCase__        =					parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_           =				logging.get_logger(__name__)




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Union[str, Any]		)     ->					Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			b.T
 _UpperCamelCase:  Optional[Any]    						=			np.sum(np.square(lowercase		)	,       axis=1		)
 _UpperCamelCase:  List[Any]    						=			np.sum(np.square(lowercase		)	,       axis=0		)
 _UpperCamelCase:  Tuple    						=			np.matmul(lowercase	,       lowercase		)
 _UpperCamelCase:  Tuple    						=			aa[:, None] - 2 * ab + ba[None, :]
 return d




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







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			x.reshape(-1	,       3		)
 _UpperCamelCase:  Dict    						=			squared_euclidean_distance(lowercase	,       lowercase		)
 return np.argmin(lowercase	,       axis=1		)





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = ['''pixel_values''']



   def __init__(						self       :	List[str]		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	bool = True		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	bool = True		,	_lowercase       :	bool = True		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  Tuple    						=			size if size is not None else {'''height''': 256, '''width''': 256}
    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Dict    						=			np.array(_lowercase				) if clusters is not None else None
    _UpperCamelCase:  Optional[Any]    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			size
    _UpperCamelCase:  Optional[int]    						=			resample
    _UpperCamelCase:  Any    						=			do_normalize
    _UpperCamelCase:  int    						=			do_color_quantize



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Dict[str, int]		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""				)
    return resize(
        _lowercase		,	size=(size['''height'''], size['''width'''])		,	resample=_lowercase		,	data_format=_lowercase		,	**_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			rescale(image=_lowercase		,	scale=1 / 127.5		,	data_format=_lowercase				)
    _UpperCamelCase:  List[Any]    						=			image - 1
    return image



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	ImageInput		,	_lowercase       :	bool = None		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = None		,	_lowercase       :	bool = None		,	_lowercase       :	Optional[bool] = None		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	Optional[Union[str, TensorType]] = None		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST		,	**_lowercase       :	List[str]		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase:  Optional[Any]    						=			size if size is not None else self.size
    _UpperCamelCase:  Optional[Any]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			resample if resample is not None else self.resample
    _UpperCamelCase:  Union[str, Any]    						=			do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:  Any    						=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    _UpperCamelCase:  Dict    						=			clusters if clusters is not None else self.clusters
    _UpperCamelCase:  Optional[Any]    						=			np.array(_lowercase				)

    _UpperCamelCase:  List[str]    						=			make_list_of_images(_lowercase				)

    if not valid_images(_lowercase				):
     raise ValueError(
         '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
         '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

    if do_resize and size is None or resample is None:
     raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

    if do_color_quantize and clusters is None:
     raise ValueError('''Clusters must be specified if do_color_quantize is True.'''				)

    # All transformations expect numpy arrays.
    _UpperCamelCase:  Optional[int]    						=			[to_numpy_array(_lowercase				) for image in images]

    if do_resize:
     _UpperCamelCase:  int    						=			[self.resize(image=_lowercase		,	size=_lowercase		,	resample=_lowercase				) for image in images]

    if do_normalize:
     _UpperCamelCase:  Any    						=			[self.normalize(image=_lowercase				) for image in images]

    if do_color_quantize:
     _UpperCamelCase:  Tuple    						=			[to_channel_dimension_format(_lowercase		,	ChannelDimension.LAST				) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     _UpperCamelCase:  Tuple    						=			np.array(_lowercase				)
     _UpperCamelCase:  List[Any]    						=			color_quantize(_lowercase		,	_lowercase				).reshape(images.shape[:-1]				)

     # flatten to (batch_size, height*width)
     _UpperCamelCase:  Any    						=			images.shape[0]
     _UpperCamelCase:  Optional[int]    						=			images.reshape(_lowercase		,	-1				)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     _UpperCamelCase:  Tuple    						=			list(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			[to_channel_dimension_format(_lowercase		,	_lowercase				) for image in images]

    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': images}
    return BatchFeature(data=_lowercase		,	tensor_type=_lowercase				)