


import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->Dict:







      """simple docstring"""

      lowercase	:      List[str]          =						old_name

      if "patch_embed" in old_name:
            lowercase	:      Optional[int]          =						old_name.split('''.'''   )

            if layer == "0":
                  lowercase	:      str          =						old_name.replace('''0''',			'''convolution1'''   )
            elif layer == "1":
                  lowercase	:      List[Any]          =						old_name.replace('''1''',			'''batchnorm_before'''   )
            elif layer == "3":
                  lowercase	:      Optional[Any]          =						old_name.replace('''3''',			'''convolution2'''   )
            else:
                  lowercase	:      Optional[int]          =						old_name.replace('''4''',			'''batchnorm_after'''   )

      if "network" in old_name and re.search(R'''\d\.\d''',			A_   ):
            lowercase	:      int          =						r'''\b\d{2}\b'''
            if bool(re.search(A_,			A_   )   ):
                  lowercase	:      Union[str, Any]          =						re.search(R'''\d\.\d\d.''',			A_   ).group()
            else:
                  lowercase	:      Tuple          =						re.search(R'''\d\.\d.''',			A_   ).group()
            if int(match[0]   ) < 6:
                  lowercase	:      Tuple          =						old_name.replace(A_,			''''''   )
                  lowercase	:      int          =						trimmed_name.replace('''network''',			match[0] + '''.meta4D_layers.blocks.''' + match[2:-1]   )
                  lowercase	:      str          =						'''intermediate_stages.''' + trimmed_name
            else:
                  lowercase	:      Dict          =						old_name.replace(A_,			''''''   )
                  if int(match[2]   ) < num_meta4D_last_stage:
                        lowercase	:      str          =						trimmed_name.replace('''network''',			'''meta4D_layers.blocks.''' + match[2]   )
                  else:
                        lowercase	:      List[Any]          =						str(int(match[2]   ) - num_meta4D_last_stage   )
                        lowercase	:      Tuple          =						trimmed_name.replace('''network''',			'''meta3D_layers.blocks.''' + layer_index   )
                        if "norm1" in old_name:
                              lowercase	:      Any          =						trimmed_name.replace('''norm1''',			'''layernorm1'''   )
                        elif "norm2" in old_name:
                              lowercase	:      str          =						trimmed_name.replace('''norm2''',			'''layernorm2'''   )
                        elif "fc1" in old_name:
                              lowercase	:      int          =						trimmed_name.replace('''fc1''',			'''linear_in'''   )
                        elif "fc2" in old_name:
                              lowercase	:      int          =						trimmed_name.replace('''fc2''',			'''linear_out'''   )

                  lowercase	:      Any          =						'''last_stage.''' + trimmed_name

      elif "network" in old_name and re.search(R'''.\d.''',			A_   ):
            lowercase	:      Tuple          =						old_name.replace('''network''',			'''intermediate_stages'''   )

      if "fc" in new_name:
            lowercase	:      Union[str, Any]          =						new_name.replace('''fc''',			'''convolution'''   )
      elif ("norm1" in new_name) and ("layernorm1" not in new_name):
            lowercase	:      int          =						new_name.replace('''norm1''',			'''batchnorm_before'''   )
      elif ("norm2" in new_name) and ("layernorm2" not in new_name):
            lowercase	:      str          =						new_name.replace('''norm2''',			'''batchnorm_after'''   )
      if "proj" in new_name:
            lowercase	:      Tuple          =						new_name.replace('''proj''',			'''projection'''   )
      if "dist_head" in new_name:
            lowercase	:      Any          =						new_name.replace('''dist_head''',			'''distillation_classifier'''   )
      elif "head" in new_name:
            lowercase	:      Tuple          =						new_name.replace('''head''',			'''classifier'''   )
      elif "patch_embed" in new_name:
            lowercase	:      Optional[int]          =						'''efficientformer.''' + new_name
      elif new_name == "norm.weight" or new_name == "norm.bias":
            lowercase	:      Optional[int]          =						new_name.replace('''norm''',			'''layernorm'''   )
            lowercase	:      Tuple          =						'''efficientformer.''' + new_name
      else:
            lowercase	:      Dict          =						'''efficientformer.encoder.''' + new_name

      return new_name




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->Union[str, Any]:







      """simple docstring"""

      for key in checkpoint.copy().keys():
            lowercase	:      List[Any]          =						checkpoint.pop(A_   )
            lowercase	:      List[str]          =						val

      return checkpoint




def 	__lowercase		(  )			->Optional[int]:







      """simple docstring"""

      lowercase	:      Optional[int]          =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowercase	:      Union[str, Any]          =						Image.open(requests.get(A_,			stream=A_   ).raw   )

      return image




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase,			_UpperCamelCase   )			->str:







      """simple docstring"""

      lowercase	:      Tuple          =						torch.load(A_,			map_location='''cpu'''   )['''model''']
      lowercase	:      int          =						EfficientFormerConfig.from_json_file(A_   )
      lowercase	:      Tuple          =						EfficientFormerForImageClassificationWithTeacher(A_   )
      lowercase	:      Dict          =						'''_'''.join(checkpoint_path.split('''/'''   )[-1].split('''.'''   )[0].split('''_'''   )[:-1]   )

      lowercase	:      Dict          =						config.depths[-1] - config.num_metaad_blocks + 1
      lowercase	:      Any          =						convert_torch_checkpoint(A_,			A_   )

      model.load_state_dict(A_   )
      model.eval()

      lowercase	:      List[str]          =						{
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

      # prepare image
      lowercase	:      Union[str, Any]          =						prepare_img()
      lowercase	:      Union[str, Any]          =						256
      lowercase	:      List[str]          =						224
      lowercase	:      int          =						EfficientFormerImageProcessor(
          size={'''shortest_edge''': image_size},			crop_size={'''height''': crop_size, '''width''': crop_size},			resample=pillow_resamplings['''bicubic'''],			)
      lowercase	:      Optional[int]          =						processor(images=A_,			return_tensors='''pt'''   ).pixel_values

      # original processing pipeline
      lowercase	:      Optional[int]          =						Compose(
          [
              Resize(A_,			interpolation=pillow_resamplings['''bicubic''']   ),
              CenterCrop(A_   ),
              ToTensor(),
              Normalize(A_,			A_   ),
          ]   )
      lowercase	:      List[str]          =						image_transforms(A_   ).unsqueeze(0   )

      assert torch.allclose(A_,			A_   )

      lowercase	:      Dict          =						model(A_   )
      lowercase	:      int          =						outputs.logits

      lowercase	:      Tuple          =						(1, 1000)

      if "l1" in model_name:
            lowercase	:      Optional[Any]          =						torch.Tensor(
                [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]   )
            assert torch.allclose(logits[0, :10],			A_,			atol=1e-3   )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            lowercase	:      Optional[Any]          =						torch.Tensor(
                [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]   )
            assert torch.allclose(logits[0, :10],			A_,			atol=1e-3   )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            lowercase	:      Dict          =						torch.Tensor(
                [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]   )
            assert logits.shape == expected_shape
      else:
            raise ValueError(
                f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""   )

      # Save Checkpoints
      Path(A_   ).mkdir(exist_ok=A_   )
      model.save_pretrained(A_   )
      print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
      processor.save_pretrained(A_   )
      print(f"""Processor successfuly saved at {pytorch_dump_path}"""   )

      if push_to_hub:
            print('''Pushing model to the hub...'''   )

            model.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}""",			commit_message='''Add model''',			use_temp_dir=A_,			)
            processor.push_to_hub(
                repo_id=f"""Bearnardd/{pytorch_dump_path}""",			commit_message='''Add image processor''',			use_temp_dir=A_,			)


if __name__ == "__main__":
  __a       =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--pytorch_model_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to EfficientFormer pytorch checkpoint.''',
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The json file for EfficientFormer model config.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )

  parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
  parser.add_argument(
      '''--no-push_to_hub''',
      dest='''push_to_hub''',
      action='''store_false''',
      help='''Do not push model and image processor to the hub''',
  )
  parser.set_defaults(push_to_hub=True)

  __a       =					parser.parse_args()
  convert_efficientformer_checkpoint(
      checkpoint_path=args.pytorch_model_path,
      efficientformer_config_file=args.config_file,
      pytorch_dump_path=args.pytorch_dump_path,
      push_to_hub=args.push_to_hub,
  )




"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def __init__(       self			:				List[str]   ,lowercase_			:				Dict   ,lowercase_			:				Dict=7   ,lowercase_			:				Optional[int]=3   ,lowercase_			:				int=3_0   ,lowercase_			:				Optional[Any]=4_0_0   ,lowercase_			:				Any=True   ,lowercase_			:				List[str]=None   ,lowercase_			:				str=True   ,lowercase_			:				List[Any]=[0.5, 0.5, 0.5]   ,lowercase_			:				List[str]=[0.5, 0.5, 0.5]   ,lowercase_			:				Any=True   ,lowercase_			:				Union[str, Any]=1 / 2_5_5   ,lowercase_			:				str=True   ,):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						lowerCAmelCase__	:						str  				=	size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Tuple  				=	batch_size
						lowerCAmelCase__	:						List[str]  				=	num_channels
						lowerCAmelCase__	:						Optional[Any]  				=	min_resolution
						lowerCAmelCase__	:						Union[str, Any]  				=	max_resolution
						lowerCAmelCase__	:						Optional[int]  				=	do_resize
						lowerCAmelCase__	:						str  				=	size
						lowerCAmelCase__	:						Union[str, Any]  				=	do_normalize
						lowerCAmelCase__	:						List[str]  				=	image_mean
						lowerCAmelCase__	:						str  				=	image_std
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale
						lowerCAmelCase__	:						Union[str, Any]  				=	rescale_factor
						lowerCAmelCase__	:						Optional[Any]  				=	do_pad







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						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        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				int=False		):
						if not batched:
							lowerCAmelCase__	:						Tuple  				=	image_inputs[0]
							if isinstance(lowercase_   ,Image.Image		):
								lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	image.size
							else:
								lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	image.shape[1], image.shape[2]
							if w < h:
								lowerCAmelCase__	:						Any  				=	int(self.size['''shortest_edge'''] * h / w		)
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']
							elif w > h:
								lowerCAmelCase__	:						Union[str, Any]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						Dict  				=	int(self.size['''shortest_edge'''] * w / h		)
							else:
								lowerCAmelCase__	:						List[str]  				=	self.size['''shortest_edge''']
								lowerCAmelCase__	:						str  				=	self.size['''shortest_edge''']

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

						return expected_height, expected_width







@require_torch
@require_vision
class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       DetaImageProcessor if is_vision_available() else None







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessingTester(self		)







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return self.image_processor_tester.prepare_image_processor_dict()







					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(lowercase_   ,'''image_mean'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''image_std'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_normalize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_resize'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_rescale'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''do_pad'''		)		)
						self.assertTrue(hasattr(lowercase_   ,'''size'''		)		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Dict  				=	self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size   ,{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
						self.assertEqual(image_processor.do_pad   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				List[str]		):
						pass







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						str  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						lowerCAmelCase__	:						Union[str, Any]  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,Image.Image		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.image_processor_tester.get_expected_values(lowercase_		)

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

						# Test batched
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	image_processing(lowercase_   ,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        __lowerCAmelCase (       self			:				Dict		):
						# Initialize image_processing
						lowerCAmelCase__	:						Optional[Any]  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						lowerCAmelCase__	:						Tuple  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,numpify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,np.ndarray		)

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

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

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

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

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







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						# Initialize image_processing
						lowerCAmelCase__	:						Tuple  				=	self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						lowerCAmelCase__	:						Any  				=	prepare_image_inputs(self.image_processor_tester   ,equal_resolution=lowercase_   ,torchify=lowercase_		)
						for image in image_inputs:
							self.assertIsInstance(lowercase_   ,torch.Tensor		)

						# Test not batched input
						lowerCAmelCase__	:						List[Any]  				=	image_processing(image_inputs[0]   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	self.image_processor_tester.get_expected_values(lowercase_		)

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

						# Test batched
						lowerCAmelCase__	:						str  				=	image_processing(lowercase_   ,return_tensors='''pt'''		).pixel_values

						lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	self.image_processor_tester.get_expected_values(lowercase_   ,batched=lowercase_		)

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







					@slow
					def        __lowerCAmelCase (       self			:				Tuple		):
						# prepare image and target
						lowerCAmelCase__	:						Tuple  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						Union[str, Any]  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						str  				=	{'''image_id''': 3_9_7_6_9, '''annotations''': target}

						# encode them
						lowerCAmelCase__	:						Optional[Any]  				=	DetaImageProcessor()
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Dict  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						List[str]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Optional[int]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						Any  				=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify orig_size
						lowerCAmelCase__	:						int  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						str  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)







					@slow
					def        __lowerCAmelCase (       self			:				Any		):
						# prepare image, target and masks_path
						lowerCAmelCase__	:						Dict  				=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
						with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''   ,'''r'''		) as f:
							lowerCAmelCase__	:						str  				=	json.loads(f.read()		)

						lowerCAmelCase__	:						Tuple  				=	{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

						lowerCAmelCase__	:						Optional[Any]  				=	pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

						# encode them
						lowerCAmelCase__	:						str  				=	DetaImageProcessor(format='''coco_panoptic'''		)
						lowerCAmelCase__	:						Optional[int]  				=	image_processing(images=lowercase_   ,annotations=lowercase_   ,masks_path=lowercase_   ,return_tensors='''pt'''		)

						# verify pixel values
						lowerCAmelCase__	:						Any  				=	torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
						self.assertEqual(encoding['''pixel_values'''].shape   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	torch.tensor([0.2796, 0.3138, 0.3481]		)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]   ,lowercase_   ,atol=1E-4		)		)

						# verify area
						lowerCAmelCase__	:						Tuple  				=	torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']   ,lowercase_		)		)
						# verify boxes
						lowerCAmelCase__	:						Optional[Any]  				=	torch.Size([6, 4]		)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,lowercase_		)
						lowerCAmelCase__	:						str  				=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,lowercase_   ,atol=1E-3		)		)
						# verify image_id
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([3_9_7_6_9]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,lowercase_		)		)
						# verify is_crowd
						lowerCAmelCase__	:						Union[str, Any]  				=	torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,lowercase_		)		)
						# verify class_labels
						lowerCAmelCase__	:						List[str]  				=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,lowercase_		)		)
						# verify masks
						lowerCAmelCase__	:						Optional[int]  				=	8_2_2_8_7_3
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,lowercase_		)
						# verify orig_size
						lowerCAmelCase__	:						List[Any]  				=	torch.tensor([4_8_0, 6_4_0]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,lowercase_		)		)
						# verify size
						lowerCAmelCase__	:						Optional[Any]  				=	torch.tensor([8_0_0, 1_0_6_6]		)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,lowercase_		)		)




