import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							int=False  ):
					SCREAMING_SNAKE_CASE    			=       []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"blocks.{i}.norm1.weight", F"deit.encoder.layer.{i}.layernorm_before.weight")  )
										rename_keys.append((F"blocks.{i}.norm1.bias", F"deit.encoder.layer.{i}.layernorm_before.bias")  )
										rename_keys.append((F"blocks.{i}.attn.proj.weight", F"deit.encoder.layer.{i}.attention.output.dense.weight")  )
										rename_keys.append((F"blocks.{i}.attn.proj.bias", F"deit.encoder.layer.{i}.attention.output.dense.bias")  )
										rename_keys.append((F"blocks.{i}.norm2.weight", F"deit.encoder.layer.{i}.layernorm_after.weight")  )
										rename_keys.append((F"blocks.{i}.norm2.bias", F"deit.encoder.layer.{i}.layernorm_after.bias")  )
										rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"deit.encoder.layer.{i}.intermediate.dense.weight")  )
										rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"deit.encoder.layer.{i}.intermediate.dense.bias")  )
										rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"deit.encoder.layer.{i}.output.dense.weight")  )
										rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"deit.encoder.layer.{i}.output.dense.bias")  )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        ("cls_token", "deit.embeddings.cls_token"),
					        ("dist_token", "deit.embeddings.distillation_token"),
					        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
					        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
					        ("pos_embed", "deit.embeddings.position_embeddings"),
					    ]  )

					if base_model:
										# layernorm + pooler
										rename_keys.extend(
										    [
										        ("norm.weight", "layernorm.weight"),
										        ("norm.bias", "layernorm.bias"),
										        ("pre_logits.fc.weight", "pooler.dense.weight"),
										        ("pre_logits.fc.bias", "pooler.dense.bias"),
										    ]  )

										# if just the base model, we should remove "deit" from all keys that start with "deit"
										SCREAMING_SNAKE_CASE    			=       [(pair[0], pair[1][4:]) if pair[1].startswith("deit"  ) else pair for pair in rename_keys]
					else:
										# layernorm + classification heads
										rename_keys.extend(
										    [
										        ("norm.weight", "deit.layernorm.weight"),
										        ("norm.bias", "deit.layernorm.bias"),
										        ("head.weight", "cls_classifier.weight"),
										        ("head.bias", "cls_classifier.bias"),
										        ("head_dist.weight", "distillation_classifier.weight"),
										        ("head_dist.bias", "distillation_classifier.bias"),
										    ]  )

					return rename_keys





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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							int  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(SCREAMING_SNAKE_CASE_  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(SCREAMING_SNAKE_CASE_      , stream=SCREAMING_SNAKE_CASE_  ).raw  )
					return im





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Dict  ):
					SCREAMING_SNAKE_CASE    			=       DeiTConfig()
					# all deit models have fine-tuned heads
					SCREAMING_SNAKE_CASE    			=       False
					# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
					SCREAMING_SNAKE_CASE    			=       1_0_0_0
					SCREAMING_SNAKE_CASE    			=       "huggingface/label-files"
					SCREAMING_SNAKE_CASE    			=       "imagenet-1k-id2label.json"
					SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , repo_type="dataset"  )      , "r"  )  )
					SCREAMING_SNAKE_CASE    			=       {int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE    			=       idalabel
					SCREAMING_SNAKE_CASE    			=       {v: k for k, v in idalabel.items()}
					SCREAMING_SNAKE_CASE    			=       int(deit_name[-6:-4]  )
					SCREAMING_SNAKE_CASE    			=       int(deit_name[-3:]  )
					# size of the architecture
					if deit_name[9:].startswith("tiny"  ):
										SCREAMING_SNAKE_CASE    			=       1_9_2
										SCREAMING_SNAKE_CASE    			=       7_6_8
										SCREAMING_SNAKE_CASE    			=       1_2
										SCREAMING_SNAKE_CASE    			=       3
					elif deit_name[9:].startswith("small"  ):
										SCREAMING_SNAKE_CASE    			=       3_8_4
										SCREAMING_SNAKE_CASE    			=       1_5_3_6
										SCREAMING_SNAKE_CASE    			=       1_2
										SCREAMING_SNAKE_CASE    			=       6
					if deit_name[9:].startswith("base"  ):
										pass
					elif deit_name[4:].startswith("large"  ):
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6

					# load original model from timm
					SCREAMING_SNAKE_CASE    			=       timm.create_model(SCREAMING_SNAKE_CASE_      , pretrained=SCREAMING_SNAKE_CASE_  )
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					SCREAMING_SNAKE_CASE    			=       timm_model.state_dict()
					SCREAMING_SNAKE_CASE    			=       create_rename_keys(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
					for src, dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
					read_in_q_k_v(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )

					# load HuggingFace model
					SCREAMING_SNAKE_CASE    			=       DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_  ).eval()
					model.load_state_dict(SCREAMING_SNAKE_CASE_  )

					# Check outputs on an image, prepared by DeiTImageProcessor
					SCREAMING_SNAKE_CASE    			=       int(
					    (2_5_6 / 2_2_4) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
					SCREAMING_SNAKE_CASE    			=       DeiTImageProcessor(size=SCREAMING_SNAKE_CASE_      , crop_size=config.image_size  )
					SCREAMING_SNAKE_CASE    			=       image_processor(images=prepare_img()      , return_tensors="pt"  )
					SCREAMING_SNAKE_CASE    			=       encoding["pixel_values"]
					SCREAMING_SNAKE_CASE    			=       model(SCREAMING_SNAKE_CASE_  )

					SCREAMING_SNAKE_CASE    			=       timm_model(SCREAMING_SNAKE_CASE_  )
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(SCREAMING_SNAKE_CASE_      , outputs.logits      , atol=1e-3  )

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


if __name__ == "__main__":
							_lowerCamelCase					:   Any							=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--deit_name''',
							    default='''vit_deit_base_distilled_patch16_224''',
							    type=str,
							    help='''Name of the DeiT timm model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
							)

							_lowerCamelCase					:   List[str]							=		parser.parse_args()
							convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



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 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=7	,							_UpperCamelCase   :					Union[str, Any]=3	,							_UpperCamelCase   :					Optional[int]=30	,							_UpperCamelCase   :					List[Any]=400	,							_UpperCamelCase   :					Dict=True	,							_UpperCamelCase   :					Union[str, Any]=None	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					List[Any]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=True	,							_UpperCamelCase   :					List[Any]=1 / 255	,							_UpperCamelCase   :					Optional[Any]=True	,							)					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std
												SCREAMING_SNAKE_CASE    			=       do_rescale
												SCREAMING_SNAKE_CASE    			=       rescale_factor
												SCREAMING_SNAKE_CASE    			=       do_pad




							def 	__snake_case(						self   :					List[str]      )					->				Union[str, Any]:



												'''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 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]=False      )					->				List[Any]:



												'''simple docstring'''

												if not batched:
																	SCREAMING_SNAKE_CASE    			=       image_inputs[0]
																	if isinstance(_UpperCamelCase	,							Image.Image      ):
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       image.size
																	else:
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       image.shape[1], image.shape[2]
																	if w < h:
																						SCREAMING_SNAKE_CASE    			=       int(self.size["shortest_edge"] * h / w      )
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
																	elif w > h:
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
																						SCREAMING_SNAKE_CASE    			=       int(self.size["shortest_edge"] * w / h      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]

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

												return expected_height, expected_width



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							DetaImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DetaImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_mean"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"image_std"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_normalize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_resize"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_rescale"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_pad"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class.from_dict(self.image_processor_dict      )
												self.assertEqual(image_processor.size	,							{"shortest_edge": 18, "longest_edge": 1_333}      )
												self.assertEqual(image_processor.do_pad	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

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

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							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 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							numpify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							np.ndarray      )

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

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




							def 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase	,							torchify=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							torch.Tensor      )

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase      )

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

												# Test batched
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

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




							@slow
							def 	__snake_case(						self   :					Dict      )					->				str:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       {"image_id": 39_769, "annotations": target}

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor()
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							return_tensors="pt"      )

												# verify pixel values
												SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
												self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
												self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )

												# verify area
												SCREAMING_SNAKE_CASE    			=       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,							_UpperCamelCase	,							atol=1e-3      )      )
												# verify image_id
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([75, 75, 63, 65, 17, 17]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )





							@slow
							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE    			=       {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

												SCREAMING_SNAKE_CASE    			=       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor(format="coco_panoptic"      )
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							masks_path=_UpperCamelCase	,							return_tensors="pt"      )

												# verify pixel values
												SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
												self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]      )
												self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,							_UpperCamelCase	,							atol=1e-4      )      )

												# verify area
												SCREAMING_SNAKE_CASE    			=       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,							_UpperCamelCase	,							atol=1e-3      )      )
												# verify image_id
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([17, 17, 63, 75, 75, 93]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify masks
												SCREAMING_SNAKE_CASE    			=       822_873
												self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,							_UpperCamelCase      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )




