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()
SCREAMING_SNAKE_CASE		:     List[Any]         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Optional[Any]  =		SwinConfig(
						    embed_dim=192		, depths=(2, 2, 18, 2)		, num_heads=(6, 12, 24, 48)		, window_size=12		, out_features=["""stage2""", """stage3""", """stage4"""]		, )

						UpperCamelCase_		:							Optional[Any]  =		DetaConfig(
						    backbone_config=_SCREAMING_SNAKE_CASE		, num_queries=900		, encoder_ffn_dim=2048		, decoder_ffn_dim=2048		, num_feature_levels=5		, assign_first_stage=_SCREAMING_SNAKE_CASE		, with_box_refine=_SCREAMING_SNAKE_CASE		, two_stage=_SCREAMING_SNAKE_CASE		, )

						# set labels
						UpperCamelCase_		:							Dict  =		"""huggingface/label-files"""
						if "o365" in model_name:
												UpperCamelCase_		:							List[Any]  =		366
												UpperCamelCase_		:							str  =		"""object365-id2label.json"""
						else:
												UpperCamelCase_		:							Any  =		91
												UpperCamelCase_		:							Optional[int]  =		"""coco-detection-id2label.json"""

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

						return config






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							List[str]  =		[]

						# 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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Dict  =		dct.pop(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							str  =		val






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






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






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Optional[Any]  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase_		:							int  =		Image.open(requests.get(_SCREAMING_SNAKE_CASE		, stream=_SCREAMING_SNAKE_CASE			).raw			)

						return im






@torch.no_grad()
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							Tuple  =		get_deta_config(_SCREAMING_SNAKE_CASE			)

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

						UpperCamelCase_		:							Union[str, Any]  =		torch.load(_SCREAMING_SNAKE_CASE		, map_location="""cpu"""			)["""model"""]

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

						# rename keys
						UpperCamelCase_		:							List[str]  =		create_rename_keys(_SCREAMING_SNAKE_CASE			)
						for src, dest in rename_keys:
												rename_key(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						read_in_swin_q_k_v(_SCREAMING_SNAKE_CASE		, config.backbone_config			)
						read_in_decoder_q_k_v(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		UpperCamelCase_		:							Dict  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Optional[Any]  =		val
												if "input_proj" in key:
																		UpperCamelCase_		:							Tuple  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							int  =		val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		UpperCamelCase_		:							int  =		state_dict.pop(_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							str  =		val

    # finally, create HuggingFace model and load state dict
						UpperCamelCase_		:							List[Any]  =		DetaForObjectDetection(_SCREAMING_SNAKE_CASE			)
						model.load_state_dict(_SCREAMING_SNAKE_CASE			)
						model.eval()

						UpperCamelCase_		:							Dict  =		"""cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(_SCREAMING_SNAKE_CASE			)

						# load image processor
						UpperCamelCase_		:							Optional[Any]  =		DetaImageProcessor(format="""coco_detection"""			)

						# verify our conversion on image
						UpperCamelCase_		:							Optional[int]  =		prepare_img()
						UpperCamelCase_		:							Any  =		processor(images=_SCREAMING_SNAKE_CASE		, return_tensors="""pt"""			)
						UpperCamelCase_		:							int  =		encoding["""pixel_values"""]
						UpperCamelCase_		:							Tuple  =		model(pixel_values.to(_SCREAMING_SNAKE_CASE			)			)

						# verify logits
						print("""Logits:"""		, outputs.logits[0, :3, :3]			)
						print("""Boxes:"""		, outputs.pred_boxes[0, :3, :3]			)
						if model_name == "deta-swin-large":
												UpperCamelCase_		:							Optional[Any]  =		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]]			)
												UpperCamelCase_		:							Optional[Any]  =		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":
												UpperCamelCase_		:							str  =		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]]			)
												UpperCamelCase_		:							Union[str, Any]  =		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(_SCREAMING_SNAKE_CASE			)		, atol=1E-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]		, expected_boxes.to(_SCREAMING_SNAKE_CASE			)		, 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(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
												model.save_pretrained(_SCREAMING_SNAKE_CASE			)
												processor.save_pretrained(_SCREAMING_SNAKE_CASE			)

						# 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__":
	SCREAMING_SNAKE_CASE		:     str         =	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."
	)
	SCREAMING_SNAKE_CASE		:     int         =	parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
	import torch

if is_tf_available():
	import tensorflow as tf

class        UpperCamelCase						(				__a				):
						a__		:str      =			['''image_processor''']
						a__		:str      =			'''SamImageProcessor'''





						def __init__(self			,     __UpperCamelCase						)				->				int:
												super().__init__(__UpperCamelCase						)
												UpperCamelCase_		:							Any  =		self.image_processor
												UpperCamelCase_		:							int  =		-10
												UpperCamelCase_		:							Dict  =		self.image_processor.size["""longest_edge"""]





						def __call__(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				BatchEncoding:
												UpperCamelCase_		:							int  =		self.image_processor(
												    __UpperCamelCase			,     return_tensors=__UpperCamelCase			,     **__UpperCamelCase			,     )

												# pop arguments that are not used in the foward but used nevertheless
												UpperCamelCase_		:							Optional[int]  =		encoding_image_processor["""original_sizes"""]

												if hasattr(__UpperCamelCase			,     """numpy"""						):  # Checks if Torch or TF tensor
																		UpperCamelCase_		:							Dict  =		original_sizes.numpy()

												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							str  =		self._check_and_preprocess_points(
												    input_points=__UpperCamelCase			,     input_labels=__UpperCamelCase			,     input_boxes=__UpperCamelCase			,     )

												UpperCamelCase_		:							List[str]  =		self._normalize_and_convert(
												    __UpperCamelCase			,     __UpperCamelCase			,     input_points=__UpperCamelCase			,     input_labels=__UpperCamelCase			,     input_boxes=__UpperCamelCase			,     return_tensors=__UpperCamelCase			,     )

												return encoding_image_processor





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase="pt"			,     )				->				Tuple:
												if input_points is not None:
																		if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																								UpperCamelCase_		:							str  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     original_sizes[0]						) for point in input_points
																								]
																		else:
																								UpperCamelCase_		:							List[str]  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     __UpperCamelCase						)
																								    for point, original_size in zip(__UpperCamelCase			,     __UpperCamelCase						)
																								]
																		# check that all arrays have the same shape
																		if not all(point.shape == input_points[0].shape for point in input_points						):
																								if input_labels is not None:
																														UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		self._pad_points_and_labels(__UpperCamelCase			,     __UpperCamelCase						)

																		UpperCamelCase_		:							Dict  =		np.array(__UpperCamelCase						)

												if input_labels is not None:
																		UpperCamelCase_		:							Optional[Any]  =		np.array(__UpperCamelCase						)

												if input_boxes is not None:
																		if len(__UpperCamelCase						) != len(__UpperCamelCase						):
																								UpperCamelCase_		:							Tuple  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     original_sizes[0]			,     is_bounding_box=__UpperCamelCase						)
																								    for box in input_boxes
																								]
																		else:
																								UpperCamelCase_		:							Any  =		[
																								    self._normalize_coordinates(self.target_size			,     __UpperCamelCase			,     __UpperCamelCase			,     is_bounding_box=__UpperCamelCase						)
																								    for box, original_size in zip(__UpperCamelCase			,     __UpperCamelCase						)
																								]
																		UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)

												if input_boxes is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Union[str, Any]  =		torch.from_numpy(__UpperCamelCase						)
																								# boxes batch size of 1 by default
																								UpperCamelCase_		:							Any  =		input_boxes.unsqueeze(1						) if len(input_boxes.shape						) != 3 else input_boxes
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							List[str]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# boxes batch size of 1 by default
																								UpperCamelCase_		:							Optional[int]  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_boxes.shape						) != 3 else input_boxes
																		encoding_image_processor.update({"""input_boxes""": input_boxes}						)
												if input_points is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Optional[Any]  =		torch.from_numpy(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							List[Any]  =		input_points.unsqueeze(1						) if len(input_points.shape						) != 4 else input_points
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							Optional[Any]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Dict  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_points.shape						) != 4 else input_points
																		encoding_image_processor.update({"""input_points""": input_points}						)
												if input_labels is not None:
																		if return_tensors == "pt":
																								UpperCamelCase_		:							Optional[int]  =		torch.from_numpy(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Tuple  =		input_labels.unsqueeze(1						) if len(input_labels.shape						) != 3 else input_labels
																		elif return_tensors == "tf":
																								UpperCamelCase_		:							Optional[Any]  =		tf.convert_to_tensor(__UpperCamelCase						)
																								# point batch size of 1 by default
																								UpperCamelCase_		:							Dict  =		tf.expand_dims(__UpperCamelCase			,     1						) if len(input_labels.shape						) != 3 else input_labels
																		encoding_image_processor.update({"""input_labels""": input_labels}						)

												return encoding_image_processor





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				Tuple:
												UpperCamelCase_		:							Union[str, Any]  =		max([point.shape[0] for point in input_points]						)
												UpperCamelCase_		:							Any  =		[]
												for i, point in enumerate(__UpperCamelCase						):
																		if point.shape[0] != expected_nb_points:
																								UpperCamelCase_		:							Optional[int]  =		np.concatenate(
																								    [point, np.zeros((expected_nb_points - point.shape[0], 2)						) + self.point_pad_value]			,     axis=0						)
																								UpperCamelCase_		:							Dict  =		np.append(input_labels[i]			,     [self.point_pad_value]						)
																		processed_input_points.append(__UpperCamelCase						)
												UpperCamelCase_		:							List[Any]  =		processed_input_points
												return input_points, input_labels





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=False						)				->				np.ndarray:
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		original_size
												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		self.image_processor._get_preprocess_shape(__UpperCamelCase			,     longest_edge=__UpperCamelCase						)
												UpperCamelCase_		:							str  =		deepcopy(__UpperCamelCase						).astype(__UpperCamelCase						)

												if is_bounding_box:
																		UpperCamelCase_		:							int  =		coords.reshape(-1			,     2			,     2						)

												UpperCamelCase_		:							Tuple  =		coords[..., 0] * (new_w / old_w)
												UpperCamelCase_		:							Optional[int]  =		coords[..., 1] * (new_h / old_h)

												if is_bounding_box:
																		UpperCamelCase_		:							Optional[Any]  =		coords.reshape(-1			,     4						)

												return coords





						def        A_							(self			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     )				->				List[str]:
												if input_points is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):  # Checks for TF or Torch tensor
																								UpperCamelCase_		:							int  =		input_points.numpy().tolist()

																		if not isinstance(__UpperCamelCase			,     __UpperCamelCase						) or not isinstance(input_points[0]			,     __UpperCamelCase						):
																								raise ValueError("""Input points must be a list of list of floating points."""						)
																		UpperCamelCase_		:							List[Any]  =		[np.array(__UpperCamelCase						) for input_point in input_points]
												else:
																		UpperCamelCase_		:							str  =		None

												if input_labels is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):
																								UpperCamelCase_		:							Any  =		input_labels.numpy().tolist()

																		if not isinstance(__UpperCamelCase			,     __UpperCamelCase						) or not isinstance(input_labels[0]			,     __UpperCamelCase						):
																								raise ValueError("""Input labels must be a list of list integers."""						)
																		UpperCamelCase_		:							List[str]  =		[np.array(__UpperCamelCase						) for label in input_labels]
												else:
																		UpperCamelCase_		:							List[str]  =		None

												if input_boxes is not None:
																		if hasattr(__UpperCamelCase			,     """numpy"""						):
																								UpperCamelCase_		:							Dict  =		input_boxes.numpy().tolist()

																		if (
																		    not isinstance(__UpperCamelCase			,     __UpperCamelCase						)
																		    or not isinstance(input_boxes[0]			,     __UpperCamelCase						)
																		    or not isinstance(input_boxes[0][0]			,     __UpperCamelCase						)
																		):
																								raise ValueError("""Input boxes must be a list of list of list of floating points."""						)
																		UpperCamelCase_		:							Tuple  =		[np.array(__UpperCamelCase						).astype(np.floataa						) for box in input_boxes]
												else:
																		UpperCamelCase_		:							List[str]  =		None

												return input_points, input_labels, input_boxes





						@property
						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							str  =		self.image_processor.model_input_names
												return list(dict.fromkeys(__UpperCamelCase						)						)





						def        A_							(self			,     *__UpperCamelCase			,     **__UpperCamelCase						)				->				Optional[Any]:
												return self.image_processor.post_process_masks(*__UpperCamelCase			,     **__UpperCamelCase						)






