




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_		:				List[str]									=     logging.get_logger(__name__)




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								=YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								__magic_name__	:	Union[str, Any]								=192
								__magic_name__	:	List[Any]								=768
								__magic_name__	:	str								=12
								__magic_name__	:	Any								=3
								__magic_name__	:	Dict								=[800, 1333]
								__magic_name__	:	int								=False
				elif yolos_name == "yolos_s_dWr":
								__magic_name__	:	int								=330
								__magic_name__	:	int								=14
								__magic_name__	:	Union[str, Any]								=6
								__magic_name__	:	Optional[Any]								=1320
				elif "yolos_s" in yolos_name:
								__magic_name__	:	Optional[Any]								=384
								__magic_name__	:	int								=1536
								__magic_name__	:	Any								=12
								__magic_name__	:	Tuple								=6
				elif "yolos_b" in yolos_name:
								__magic_name__	:	Any								=[800, 1344]

				__magic_name__	:	Any								=91
				__magic_name__	:	int								="""huggingface/label-files"""
				__magic_name__	:	int								="""coco-detection-id2label.json"""
				__magic_name__	:	Optional[int]								=json.load(open(hf_hub_download(lowerCamelCase	,     lowerCamelCase	,     repo_type="""dataset"""   )	,     """r"""   )   )
				__magic_name__	:	Optional[int]								={int(lowerCamelCase   ): v for k, v in idalabel.items()}
				__magic_name__	:	Any								=idalabel
				__magic_name__	:	Tuple								={v: k for k, v in idalabel.items()}

				return config




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = False   ):
				for i in range(config.num_hidden_layers   ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								__magic_name__	:	List[Any]								=state_dict.pop(F"blocks.{i}.attn.qkv.weight"   )
								__magic_name__	:	Tuple								=state_dict.pop(F"blocks.{i}.attn.qkv.bias"   )
								# next, add query, keys and values (in that order) to the state dict
								__magic_name__	:	List[Any]								=in_proj_weight[: config.hidden_size, :]
								__magic_name__	:	Optional[Any]								=in_proj_bias[: config.hidden_size]
								__magic_name__	:	Optional[Any]								=in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__magic_name__	:	Any								=in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								__magic_name__	:	Any								=in_proj_weight[-config.hidden_size :, :]
								__magic_name__	:	List[Any]								=in_proj_bias[-config.hidden_size :]




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				if "backbone" in name:
								__magic_name__	:	Union[str, Any]								=name.replace("""backbone"""	,     """vit"""   )
				if "cls_token" in name:
								__magic_name__	:	List[Any]								=name.replace("""cls_token"""	,     """embeddings.cls_token"""   )
				if "det_token" in name:
								__magic_name__	:	Any								=name.replace("""det_token"""	,     """embeddings.detection_tokens"""   )
				if "mid_pos_embed" in name:
								__magic_name__	:	str								=name.replace("""mid_pos_embed"""	,     """encoder.mid_position_embeddings"""   )
				if "pos_embed" in name:
								__magic_name__	:	Optional[int]								=name.replace("""pos_embed"""	,     """embeddings.position_embeddings"""   )
				if "patch_embed.proj" in name:
								__magic_name__	:	Dict								=name.replace("""patch_embed.proj"""	,     """embeddings.patch_embeddings.projection"""   )
				if "blocks" in name:
								__magic_name__	:	List[str]								=name.replace("""blocks"""	,     """encoder.layer"""   )
				if "attn.proj" in name:
								__magic_name__	:	int								=name.replace("""attn.proj"""	,     """attention.output.dense"""   )
				if "attn" in name:
								__magic_name__	:	str								=name.replace("""attn"""	,     """attention.self"""   )
				if "norm1" in name:
								__magic_name__	:	Optional[int]								=name.replace("""norm1"""	,     """layernorm_before"""   )
				if "norm2" in name:
								__magic_name__	:	Optional[int]								=name.replace("""norm2"""	,     """layernorm_after"""   )
				if "mlp.fc1" in name:
								__magic_name__	:	Optional[Any]								=name.replace("""mlp.fc1"""	,     """intermediate.dense"""   )
				if "mlp.fc2" in name:
								__magic_name__	:	Tuple								=name.replace("""mlp.fc2"""	,     """output.dense"""   )
				if "class_embed" in name:
								__magic_name__	:	Union[str, Any]								=name.replace("""class_embed"""	,     """class_labels_classifier"""   )
				if "bbox_embed" in name:
								__magic_name__	:	Any								=name.replace("""bbox_embed"""	,     """bbox_predictor"""   )
				if "vit.norm" in name:
								__magic_name__	:	Optional[Any]								=name.replace("""vit.norm"""	,     """vit.layernorm"""   )

				return name




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				for key in orig_state_dict.copy().keys():
								__magic_name__	:	List[str]								=orig_state_dict.pop(lowerCamelCase   )

								if "qkv" in key:
												__magic_name__	:	str								=key.split("""."""   )
												__magic_name__	:	List[str]								=int(key_split[2]   )
												__magic_name__	:	Optional[Any]								=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
												if "weight" in key:
																__magic_name__	:	Union[str, Any]								=val[:dim, :]
																__magic_name__	:	List[Any]								=val[
																    dim : dim * 2, :
																]
																__magic_name__	:	int								=val[-dim:, :]
												else:
																__magic_name__	:	int								=val[:dim]
																__magic_name__	:	Union[str, Any]								=val[dim : dim * 2]
																__magic_name__	:	Optional[Any]								=val[-dim:]
								else:
												__magic_name__	:	str								=val

				return orig_state_dict




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Optional[Any]								="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__magic_name__	:	Union[str, Any]								=Image.open(requests.get(lowerCamelCase	,     stream=lowerCamelCase   ).raw   )
				return im




@torch.no_grad()
def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = False   ):
				__magic_name__	:	List[str]								=get_yolos_config(lowerCamelCase   )

				# load original state_dict
				__magic_name__	:	Union[str, Any]								=torch.load(lowerCamelCase	,     map_location="""cpu"""   )["""model"""]

				# load 🤗 model
				__magic_name__	:	int								=YolosForObjectDetection(lowerCamelCase   )
				model.eval()
				__magic_name__	:	int								=convert_state_dict(lowerCamelCase	,     lowerCamelCase   )
				model.load_state_dict(lowerCamelCase   )

				# Check outputs on an image, prepared by YolosImageProcessor
				__magic_name__	:	Union[str, Any]								=800 if yolos_name != """yolos_ti""" else 512
				__magic_name__	:	Union[str, Any]								=YolosImageProcessor(format="""coco_detection"""	,     size=lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=image_processor(images=prepare_img()	,     return_tensors="""pt"""   )
				__magic_name__	:	Tuple								=model(**lowerCamelCase   )
				__magic_name__				, __magic_name__	:	str								=outputs.logits, outputs.pred_boxes

				__magic_name__				, __magic_name__	:	Union[str, Any]								=None, None
				if yolos_name == "yolos_ti":
								__magic_name__	:	Any								=torch.tensor(
								    [[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]]   )
								__magic_name__	:	str								=torch.tensor(
								    [[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]]   )
				elif yolos_name == "yolos_s_200_pre":
								__magic_name__	:	Tuple								=torch.tensor(
								    [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]   )
								__magic_name__	:	List[Any]								=torch.tensor(
								    [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]   )
				elif yolos_name == "yolos_s_300_pre":
								__magic_name__	:	Tuple								=torch.tensor(
								    [[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]]   )
								__magic_name__	:	Optional[int]								=torch.tensor(
								    [[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]]   )
				elif yolos_name == "yolos_s_dWr":
								__magic_name__	:	Union[str, Any]								=torch.tensor(
								    [[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]]   )
								__magic_name__	:	Optional[int]								=torch.tensor(
								    [[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]]   )
				elif yolos_name == "yolos_base":
								__magic_name__	:	List[str]								=torch.tensor(
								    [[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]]   )
								__magic_name__	:	List[str]								=torch.tensor(
								    [[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]]   )
				else:
								raise ValueError(F"Unknown yolos_name: {yolos_name}"   )

				assert torch.allclose(logits[0, :3, :3]	,     lowerCamelCase	,     atol=1E-4   )
				assert torch.allclose(pred_boxes[0, :3, :3]	,     lowerCamelCase	,     atol=1E-4   )

				Path(lowerCamelCase   ).mkdir(exist_ok=lowerCamelCase   )
				print(F"Saving model {yolos_name} to {pytorch_dump_folder_path}"   )
				model.save_pretrained(lowerCamelCase   )
				print(F"Saving image processor to {pytorch_dump_folder_path}"   )
				image_processor.save_pretrained(lowerCamelCase   )

				if push_to_hub:
								__magic_name__	:	str								={
								    """yolos_ti""": """yolos-tiny""",
								    """yolos_s_200_pre""": """yolos-small""",
								    """yolos_s_300_pre""": """yolos-small-300""",
								    """yolos_s_dWr""": """yolos-small-dwr""",
								    """yolos_base""": """yolos-base""",
								}

								print("""Pushing to the hub..."""   )
								__magic_name__	:	Any								=model_mapping[yolos_name]
								image_processor.push_to_hub(lowerCamelCase	,     organization="""hustvl"""   )
								model.push_to_hub(lowerCamelCase	,     organization="""hustvl"""   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Tuple									=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--yolos_name",
							    default="yolos_s_200_pre",
							    type=str,
							    help=(
							        "Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
							        " 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
							    ),
							)
							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)

							UpperCAmelCase_		:				List[Any]									=     parser.parse_args()
							convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor




class 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[str]    , __snake_case   :str    , __snake_case   :List[Any]=7    , __snake_case   :List[str]=3    , __snake_case   :int=30    , __snake_case   :int=4_00    , __snake_case   :List[str]=True    , __snake_case   :List[str]=None    , __snake_case   :Tuple=True    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Union[str, Any]=True    , __snake_case   :str=1 / 2_55    , __snake_case   :int=True    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_rescale": self.do_rescale,
										    "rescale_factor": self.rescale_factor,
										    "do_pad": self.do_pad,
										}


						def     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

										if not batched:
														__magic_name__	:	Any								=image_inputs[0]
														if isinstance(__snake_case    , Image.Image ):
																		__magic_name__				, __magic_name__	:	Dict								=image.size
														else:
																		__magic_name__				, __magic_name__	:	Dict								=image.shape[1], image.shape[2]
														if w < h:
																		__magic_name__	:	Any								=int(self.size["""shortest_edge"""] * h / w )
																		__magic_name__	:	Dict								=self.size["""shortest_edge"""]
														elif w > h:
																		__magic_name__	:	Union[str, Any]								=self.size["""shortest_edge"""]
																		__magic_name__	:	Optional[int]								=int(self.size["""shortest_edge"""] * w / h )
														else:
																		__magic_name__	:	Any								=self.size["""shortest_edge"""]
																		__magic_name__	:	List[str]								=self.size["""shortest_edge"""]

										else:
														__magic_name__	:	List[str]								=[]
														for image in image_inputs:
																		__magic_name__				, __magic_name__	:	Tuple								=self.get_expected_values([image] )
																		expected_values.append((expected_height, expected_width) )
														__magic_name__	:	Optional[int]								=max(__snake_case    , key=lambda __snake_case : item[0] )[0]
														__magic_name__	:	Tuple								=max(__snake_case    , key=lambda __snake_case : item[1] )[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size    , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
										self.assertEqual(image_processor.do_pad    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										__magic_name__	:	Tuple								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , Image.Image )

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

										__magic_name__				, __magic_name__	:	str								=self.image_processor_tester.get_expected_values(__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

										# Test batched
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values
										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[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	Optional[Any]								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , numpify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , np.ndarray )

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

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

										# Test batched
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__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   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Dict								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

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

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

										# Test batched
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										self.assertEqual(
										    encoded_images.shape    , (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        expected_height,
										        expected_width,
										    )    , )

						@slow
						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	Optional[int]								=json.loads(f.read() )

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Tuple								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	Optional[int]								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	str								=json.loads(f.read() )

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




