



"""simple docstring"""





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.esm.modeling_esmfold import EsmForProteinFolding



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase=1_3				,     _lowercase=7				,     _lowercase=False				,     _lowercase=True				,     _lowercase=False				,     _lowercase=False				,     _lowercase=1_9				,     _lowercase=3_2				,     _lowercase=5				,     _lowercase=4				,     _lowercase=3_7				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.1				,     _lowercase=5_1_2				,     _lowercase=1_6				,     _lowercase=2				,     _lowercase=0.02				,     _lowercase=3				,     _lowercase=4				,     _lowercase=None				,     )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Any            =      parent
								snake_case_ :			str            =      batch_size
								snake_case_ :			Dict            =      seq_length
								snake_case_ :			List[str]            =      is_training
								snake_case_ :			Tuple            =      use_input_mask
								snake_case_ :			str            =      use_token_type_ids
								snake_case_ :			Any            =      use_labels
								snake_case_ :			Any            =      vocab_size
								snake_case_ :			Union[str, Any]            =      hidden_size
								snake_case_ :			Any            =      num_hidden_layers
								snake_case_ :			Dict            =      num_attention_heads
								snake_case_ :			Dict            =      intermediate_size
								snake_case_ :			List[str]            =      hidden_act
								snake_case_ :			Any            =      hidden_dropout_prob
								snake_case_ :			Optional[int]            =      attention_probs_dropout_prob
								snake_case_ :			List[Any]            =      max_position_embeddings
								snake_case_ :			Optional[Any]            =      type_vocab_size
								snake_case_ :			List[str]            =      type_sequence_label_size
								snake_case_ :			str            =      initializer_range
								snake_case_ :			List[str]            =      num_labels
								snake_case_ :			Optional[Any]            =      num_choices
								snake_case_ :			str            =      scope





		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size )

								snake_case_ :			int            =      None
								if self.use_input_mask:
														snake_case_ :			List[str]            =      random_attention_mask([self.batch_size, self.seq_length] )

								snake_case_ :			List[Any]            =      None
								snake_case_ :			Optional[Any]            =      None
								snake_case_ :			Optional[int]            =      None
								if self.use_labels:
														snake_case_ :			List[str]            =      ids_tensor([self.batch_size]				,     self.type_sequence_label_size )
														snake_case_ :			Union[str, Any]            =      ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels )
														snake_case_ :			str            =      ids_tensor([self.batch_size]				,     self.num_choices )

								snake_case_ :			str            =      self.get_config()

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			Dict            =      EsmConfig(
								    vocab_size=3_3				,     hidden_size=self.hidden_size				,     pad_token_id=1				,     num_hidden_layers=self.num_hidden_layers				,     num_attention_heads=self.num_attention_heads				,     intermediate_size=self.intermediate_size				,     hidden_act=self.hidden_act				,     hidden_dropout_prob=self.hidden_dropout_prob				,     attention_probs_dropout_prob=self.attention_probs_dropout_prob				,     max_position_embeddings=self.max_position_embeddings				,     type_vocab_size=self.type_vocab_size				,     initializer_range=self.initializer_range				,     is_folding_model=_lowercase				,     esmfold_config={"""trunk""": {"""num_blocks""": 2}, """fp16_esm""": False}				,     )
								return config





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      EsmForProteinFolding(config=_lowercase ).float()
								model.to(_lowercase )
								model.eval()
								snake_case_ :			Tuple            =      model(_lowercase				,     attention_mask=_lowercase )
								snake_case_ :			int            =      model(_lowercase )
								snake_case_ :			Any            =      model(_lowercase )

								self.parent.assertEqual(result.positions.shape				,     (8, self.batch_size, self.seq_length, 1_4, 3) )
								self.parent.assertEqual(result.angles.shape				,     (8, self.batch_size, self.seq_length, 7, 2) )





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		(
								    snake_case_
								)      ,		
								) :			Dict            =      config_and_inputs
								snake_case_ :			str            =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




@require_torch
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							False

		_lowerCamelCase      	=							(EsmForProteinFolding,) if is_torch_available() else ()
		_lowerCamelCase      	=							()
		_lowerCamelCase      	=							{} if is_torch_available() else {}
		_lowerCamelCase      	=							False





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      EsmFoldModelTester(self )
								snake_case_ :			Union[str, Any]            =      ConfigTester(self				,     config_class=_lowercase				,     hidden_size=3_7 )





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								self.config_tester.run_common_tests()





		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			str            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase )





		@unittest.skip("""Does not support attention outputs""" )
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								pass





		@unittest.skip
		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								pass





		@unittest.skip("""Esm does not support embedding resizing""" )
		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								pass





		@unittest.skip("""Esm does not support embedding resizing""" )
		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support passing input embeds!""" )
		def  UpperCAmelCase__				(       self )			->    List[Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support head pruning.""" )
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support head pruning.""" )
		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support head pruning.""" )
		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support head pruning.""" )
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support head pruning.""" )
		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not output hidden states in the normal way.""" )
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMfold does not output hidden states in the normal way.""" )
		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold only has one output format.""" )
		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								pass





		@unittest.skip("""This test doesn't work for ESMFold and doesn't test core functionality""" )
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold does not support input chunking.""" )
		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold doesn't respect you and it certainly doesn't respect your initialization arguments.""" )
		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold doesn't support torchscript compilation.""" )
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								pass





		@unittest.skip("""ESMFold doesn't support data parallel.""" )
		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								pass





		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								pass




@require_torch
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@slow
		def  UpperCAmelCase__				(       self )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      EsmForProteinFolding.from_pretrained("""facebook/esmfold_v1""" ).float()
								model.eval()
								snake_case_ :			Any            =      torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
								snake_case_ :			Tuple            =      model(_lowercase )["""positions"""]
								snake_case_ :			Any            =      torch.tensor([2.5828, 0.7993, -10.9334]				,     dtype=torch.floataa )
								self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]				,     _lowercase				,     atol=1E-4 ) )








"""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    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def __init__(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=7       ,					__UpperCAmelCase=3       ,					__UpperCAmelCase=3_0       ,					__UpperCAmelCase=4_0_0       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=[0.5, 0.5, 0.5]       ,					__UpperCAmelCase=[0.5, 0.5, 0.5]       ,					__UpperCAmelCase=True       ,					__UpperCAmelCase=1 / 2_5_5       ,					__UpperCAmelCase=True       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
         lowerCAmelCase__     :List[Any]											=				parent
         lowerCAmelCase__     :int											=				batch_size
         lowerCAmelCase__     :Union[str, Any]											=				num_channels
         lowerCAmelCase__     :Any											=				min_resolution
         lowerCAmelCase__     :Dict											=				max_resolution
         lowerCAmelCase__     :Dict											=				do_resize
         lowerCAmelCase__     :Optional[Any]											=				size
         lowerCAmelCase__     :List[str]											=				do_normalize
         lowerCAmelCase__     :str											=				image_mean
         lowerCAmelCase__     :Tuple											=				image_std
         lowerCAmelCase__     :Dict											=				do_rescale
         lowerCAmelCase__     :Tuple											=				rescale_factor
         lowerCAmelCase__     :Optional[int]											=				do_pad






       def 	snake_case		(       self ):



         '''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       ,					__UpperCAmelCase       ,					__UpperCAmelCase=False ):



         '''simple docstring'''

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

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

         return expected_height, expected_width






@require_torch
@require_vision
class    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Any						    =   DetaImageProcessor if is_vision_available() else None






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				DetaImageProcessingTester(self )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				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 ):



         '''simple docstring'''

         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       ,					__UpperCAmelCase )






       def 	snake_case		(       self ):



         '''simple docstring'''

         pass






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowerCAmelCase__     :Any											=				prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=__UpperCAmelCase )
         for image in image_inputs:
           self.assertIsInstance(__UpperCAmelCase       ,					Image.Image )

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

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				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
         lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				self.image_processor_tester.get_expected_values(__UpperCAmelCase       ,					batched=__UpperCAmelCase )

         lowerCAmelCase__     :Optional[Any]											=				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 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowerCAmelCase__     :Dict											=				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
         lowerCAmelCase__     :List[Any]											=				image_processing(image_inputs[0]       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				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
         lowerCAmelCase__     :Tuple											=				image_processing(__UpperCAmelCase       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[str]											=				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 ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowerCAmelCase__     :List[Any]											=				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
         lowerCAmelCase__     :Any											=				image_processing(image_inputs[0]       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :Optional[int]											=				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
         lowerCAmelCase__     :str											=				image_processing(__UpperCAmelCase       ,					return_tensors='pt' ).pixel_values

         lowerCAmelCase__				,							lowerCAmelCase__     :List[Any]											=				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 ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,					'r' ) as f:
           lowerCAmelCase__     :Dict											=				json.loads(f.read() )

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

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

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

         lowerCAmelCase__     :Optional[int]											=				torch.tensor([0.27_96, 0.31_38, 0.34_81] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )

         # verify area
         lowerCAmelCase__     :Dict											=				torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,					__UpperCAmelCase ) )
         # verify boxes
         lowerCAmelCase__     :Tuple											=				torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,					__UpperCAmelCase       ,					atol=1E-3 ) )
         # verify image_id
         lowerCAmelCase__     :Any											=				torch.tensor([3_9_7_6_9] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,					__UpperCAmelCase ) )
         # verify is_crowd
         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,					__UpperCAmelCase ) )
         # verify class_labels
         lowerCAmelCase__     :Optional[Any]											=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,					__UpperCAmelCase ) )
         # verify orig_size
         lowerCAmelCase__     :str											=				torch.tensor([4_8_0, 6_4_0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,					__UpperCAmelCase ) )
         # verify size
         lowerCAmelCase__     :Any											=				torch.tensor([8_0_0, 1_0_6_6] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,					__UpperCAmelCase ) )






       @slow
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,					'r' ) as f:
           lowerCAmelCase__     :Dict											=				json.loads(f.read() )

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

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

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

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

         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([0.27_96, 0.31_38, 0.34_81] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,					__UpperCAmelCase       ,					atol=1E-4 ) )

         # verify area
         lowerCAmelCase__     :Tuple											=				torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,					__UpperCAmelCase ) )
         # verify boxes
         lowerCAmelCase__     :int											=				torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape       ,					__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,					__UpperCAmelCase       ,					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']       ,					__UpperCAmelCase ) )
         # verify is_crowd
         lowerCAmelCase__     :Tuple											=				torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,					__UpperCAmelCase ) )
         # 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']       ,					__UpperCAmelCase ) )
         # verify masks
         lowerCAmelCase__     :Optional[int]											=				8_2_2_8_7_3
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,					__UpperCAmelCase )
         # verify orig_size
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([4_8_0, 6_4_0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,					__UpperCAmelCase ) )
         # verify size
         lowerCAmelCase__     :Union[str, Any]											=				torch.tensor([8_0_0, 1_0_6_6] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,					__UpperCAmelCase ) )
