
'''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()
snake_case									=    logging.get_logger(__name__)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	SwinConfig(
     embed_dim=1_9_2      ,			depths=(2, 2, 1_8, 2)      ,			num_heads=(6, 1_2, 2_4, 4_8)      ,			window_size=1_2      ,			out_features=["stage2", "stage3", "stage4"]      ,			)

 lowerCAmelCase__     :   Optional[Any]     		=	DetaConfig(
     backbone_config=lowerCamelCase_      ,			num_queries=9_0_0      ,			encoder_ffn_dim=2_0_4_8      ,			decoder_ffn_dim=2_0_4_8      ,			num_feature_levels=5      ,			assign_first_stage=lowerCamelCase_      ,			with_box_refine=lowerCamelCase_      ,			two_stage=lowerCamelCase_      ,			)

 # set labels
 lowerCAmelCase__     :   List[Any]     		=	"huggingface/label-files"
 if "o365" in model_name:
  lowerCAmelCase__     :   str     		=	3_6_6
  lowerCAmelCase__     :   Tuple     		=	"object365-id2label.json"
 else:
  lowerCAmelCase__     :   Tuple     		=	9_1
  lowerCAmelCase__     :   Optional[int]     		=	"coco-detection-id2label.json"

 lowerCAmelCase__     :   Optional[Any]     		=	num_labels
 lowerCAmelCase__     :   List[Any]     		=	json.load(open(cached_download(hf_hub_url(lowerCamelCase_      ,			lowerCamelCase_      ,			repo_type="dataset"			)			)      ,			"r"			)			)
 lowerCAmelCase__     :   List[Any]     		=	{int(lowerCamelCase_			): v for k, v in idalabel.items()}
 lowerCAmelCase__     :   Any     		=	idalabel
 lowerCAmelCase__     :   List[Any]     		=	{v: k for k, v in idalabel.items()}

 return config


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Any     		=	[]

 # 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_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   int     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
 for i in range(len(backbone_config.depths			)			):
  lowerCAmelCase__     :   str     		=	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)
   lowerCAmelCase__     :   str     		=	state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
   lowerCAmelCase__     :   Union[str, Any]     		=	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
   lowerCAmelCase__     :   Union[str, Any]     		=	in_proj_weight[:dim, :]
   lowerCAmelCase__     :   Union[str, Any]     		=	in_proj_bias[: dim]
   lowerCAmelCase__     :   List[str]     		=	in_proj_weight[
       dim : dim * 2, :
   ]
   lowerCAmelCase__     :   str     		=	in_proj_bias[
       dim : dim * 2
   ]
   lowerCAmelCase__     :   List[str]     		=	in_proj_weight[
       -dim :, :
   ]
   lowerCAmelCase__     :   Optional[Any]     		=	in_proj_bias[-dim :]
   # fmt: on


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	config.d_model
 for i in range(config.decoder_layers			):
  # read in weights + bias of input projection layer of self-attention
  lowerCAmelCase__     :   Any     		=	state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''			)
  lowerCAmelCase__     :   Union[str, Any]     		=	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
  lowerCAmelCase__     :   Any     		=	in_proj_weight[:hidden_size, :]
  lowerCAmelCase__     :   Tuple     		=	in_proj_bias[:hidden_size]
  lowerCAmelCase__     :   Optional[Any]     		=	in_proj_weight[
      hidden_size : hidden_size * 2, :
  ]
  lowerCAmelCase__     :   List[Any]     		=	in_proj_bias[hidden_size : hidden_size * 2]
  lowerCAmelCase__     :   Dict     		=	in_proj_weight[-hidden_size:, :]
  lowerCAmelCase__     :   List[str]     		=	in_proj_bias[-hidden_size:]


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCAmelCase__     :   str     		=	Image.open(requests.get(lowerCamelCase_      ,			stream=lowerCamelCase_			).raw			)

 return im


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   int     		=	get_deta_config(lowerCamelCase_			)

 # load original state dict
 if model_name == "deta-swin-large":
  lowerCAmelCase__     :   Optional[int]     		=	hf_hub_download(repo_id="nielsr/deta-checkpoints"      ,			filename="adet_swin_ft.pth"			)
 elif model_name == "deta-swin-large-o365":
  lowerCAmelCase__     :   Tuple     		=	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'''			)

 lowerCAmelCase__     :   str     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)["model"]

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

 # rename keys
 lowerCAmelCase__     :   Optional[int]     		=	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:
   lowerCAmelCase__     :   Optional[Any]     		=	state_dict.pop(lowerCamelCase_			)
   lowerCAmelCase__     :   Optional[int]     		=	val
  if "input_proj" in key:
   lowerCAmelCase__     :   Optional[int]     		=	state_dict.pop(lowerCamelCase_			)
   lowerCAmelCase__     :   str     		=	val
  if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
   lowerCAmelCase__     :   List[str]     		=	state_dict.pop(lowerCamelCase_			)
   lowerCAmelCase__     :   Optional[Any]     		=	val

    # finally, create HuggingFace model and load state dict
 lowerCAmelCase__     :   Tuple     		=	DetaForObjectDetection(lowerCamelCase_			)
 model.load_state_dict(lowerCamelCase_			)
 model.eval()

 lowerCAmelCase__     :   List[str]     		=	"cuda" if torch.cuda.is_available() else "cpu"
 model.to(lowerCamelCase_			)

 # load image processor
 lowerCAmelCase__     :   Dict     		=	DetaImageProcessor(format="coco_detection"			)

 # verify our conversion on image
 lowerCAmelCase__     :   Union[str, Any]     		=	prepare_img()
 lowerCAmelCase__     :   Optional[int]     		=	processor(images=lowerCamelCase_      ,			return_tensors="pt"			)
 lowerCAmelCase__     :   Dict     		=	encoding["pixel_values"]
 lowerCAmelCase__     :   Optional[Any]     		=	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":
  lowerCAmelCase__     :   Any     		=	torch.tensor(
      [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
  lowerCAmelCase__     :   str     		=	torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
 elif model_name == "deta-swin-large-o365":
  lowerCAmelCase__     :   Optional[int]     		=	torch.tensor(
      [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
  lowerCAmelCase__     :   Dict     		=	torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
 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__":
     snake_case									=    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."""
     )
     snake_case									=    parser.parse_args()
     convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


_snake_case : Tuple  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    str				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''shortest_edge''': 2_56}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							resample
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Tuple   )     ->				np.ndarray:


														"""simple docstring"""


														return rescale(lowerCAmelCase_				,     scale=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							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_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale 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.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

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

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

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

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

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Any:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation

