






"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers import (
							    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
							    BertConfig,
							    DPRConfig,
							    TFDPRContextEncoder,
							    TFDPRQuestionEncoder,
							    TFDPRReader,
							)



class       __A		:

					def __init__(			self  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[int]=1_3     ,		__snake_case  :      Tuple=7     ,		__snake_case  :      Optional[Any]=True     ,		__snake_case  :      List[str]=True     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      List[str]=True     ,		__snake_case  :      Dict=9_9     ,		__snake_case  :      Dict=3_2     ,		__snake_case  :      Optional[int]=2     ,		__snake_case  :      List[Any]=4     ,		__snake_case  :      List[str]=3_7     ,		__snake_case  :      str="gelu"     ,		__snake_case  :      List[str]=0.1     ,		__snake_case  :      Tuple=0.1     ,		__snake_case  :      str=5_1_2     ,		__snake_case  :      Optional[int]=1_6     ,		__snake_case  :      List[str]=2     ,		__snake_case  :      str=0.02     ,		__snake_case  :      Optional[int]=3     ,		__snake_case  :      List[str]=4     ,		__snake_case  :      Any=None     ,		__snake_case  :      int=0     ,		)			->							int:
											__magic_name__:	int							=       parent
											__magic_name__:	Tuple							=       batch_size
											__magic_name__:	List[Any]							=       seq_length
											__magic_name__:	str							=       is_training
											__magic_name__:	Union[str, Any]							=       use_input_mask
											__magic_name__:	Any							=       use_token_type_ids
											__magic_name__:	Tuple							=       use_labels
											__magic_name__:	Tuple							=       vocab_size
											__magic_name__:	Tuple							=       hidden_size
											__magic_name__:	str							=       num_hidden_layers
											__magic_name__:	List[Any]							=       num_attention_heads
											__magic_name__:	Any							=       intermediate_size
											__magic_name__:	List[Any]							=       hidden_act
											__magic_name__:	Optional[int]							=       hidden_dropout_prob
											__magic_name__:	Dict							=       attention_probs_dropout_prob
											__magic_name__:	Union[str, Any]							=       max_position_embeddings
											__magic_name__:	Tuple							=       type_vocab_size
											__magic_name__:	List[str]							=       type_sequence_label_size
											__magic_name__:	Union[str, Any]							=       initializer_range
											__magic_name__:	List[str]							=       num_labels
											__magic_name__:	List[Any]							=       num_choices
											__magic_name__:	str							=       scope
											__magic_name__:	Dict							=       projection_dim

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							int:
											__magic_name__:	str							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size					)

											__magic_name__:	List[str]							=       None
											if self.use_input_mask:
																	# follow test_modeling_tf_ctrl.py
																	__magic_name__:	Dict							=       random_attention_mask([self.batch_size, self.seq_length]					)

											__magic_name__:	Dict							=       None
											if self.use_token_type_ids:
																	__magic_name__:	Tuple							=       ids_tensor([self.batch_size, self.seq_length]     ,		self.type_vocab_size					)

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

											__magic_name__:	List[str]							=       BertConfig(
											    vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		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     ,		is_decoder=__snake_case     ,		initializer_range=self.initializer_range     ,		)
											__magic_name__:	Any							=       DPRConfig(projection_dim=self.projection_dim     ,		**config.to_dict()					)

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

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Any     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Dict     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]					)			->							Optional[Any]:
											__magic_name__:	int							=       TFDPRContextEncoder(config=__snake_case					)
											__magic_name__:	Tuple							=       model(__snake_case     ,		attention_mask=__snake_case     ,		token_type_ids=__snake_case					)
											__magic_name__:	Optional[Any]							=       model(__snake_case     ,		token_type_ids=__snake_case					)
											__magic_name__:	Tuple							=       model(__snake_case					)
											self.parent.assertEqual(result.pooler_output.shape     ,		(self.batch_size, self.projection_dim or self.hidden_size)					)

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      int     ,		__snake_case  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      List[Any]					)			->							int:
											__magic_name__:	Any							=       TFDPRQuestionEncoder(config=__snake_case					)
											__magic_name__:	Optional[int]							=       model(__snake_case     ,		attention_mask=__snake_case     ,		token_type_ids=__snake_case					)
											__magic_name__:	List[Any]							=       model(__snake_case     ,		token_type_ids=__snake_case					)
											__magic_name__:	int							=       model(__snake_case					)
											self.parent.assertEqual(result.pooler_output.shape     ,		(self.batch_size, self.projection_dim or self.hidden_size)					)

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      Optional[int]     ,		__snake_case  :      str     ,		__snake_case  :      str     ,		__snake_case  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      int					)			->							List[Any]:
											__magic_name__:	List[str]							=       TFDPRReader(config=__snake_case					)
											__magic_name__:	Dict							=       model(__snake_case     ,		attention_mask=__snake_case					)

											self.parent.assertEqual(result.start_logits.shape     ,		(self.batch_size, self.seq_length)					)
											self.parent.assertEqual(result.end_logits.shape     ,		(self.batch_size, self.seq_length)					)
											self.parent.assertEqual(result.relevance_logits.shape     ,		(self.batch_size,)					)






					def   lowerCamelCase__   (			self  :      Dict					)			->							Tuple:
											__magic_name__:	int							=       self.prepare_config_and_inputs()
											(
											    (
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			(
											    __magic_name__
											),			
											):	Optional[int]							=       config_and_inputs
											__magic_name__:	List[str]							=       {"""input_ids""": input_ids}
											return config, inputs_dict



@require_tf
class       __A		(   SCREAMING_SNAKE_CASE_     ,SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				(
					    (
					        TFDPRContextEncoder,
					        TFDPRQuestionEncoder,
					        TFDPRReader,
					    )
					    if is_tf_available()
					    else ()
					)
					UpperCAmelCase__								=				{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Any					)			->							List[Any]:
											__magic_name__:	List[Any]							=       TFDPRModelTester(self					)
											__magic_name__:	Optional[Any]							=       ConfigTester(self     ,		config_class=__snake_case     ,		hidden_size=3_7					)

					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											self.config_tester.run_common_tests()

					def   lowerCamelCase__   (			self  :      List[str]					)			->							str:
											__magic_name__:	str							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_dpr_context_encoder(*__snake_case					)

					def   lowerCamelCase__   (			self  :      str					)			->							Optional[Any]:
											__magic_name__:	int							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_dpr_question_encoder(*__snake_case					)

					def   lowerCamelCase__   (			self  :      Dict					)			->							List[str]:
											__magic_name__:	Tuple							=       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_dpr_reader(*__snake_case					)






					@slow
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							int:
											for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Tuple							=       TFDPRContextEncoder.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)

											for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	str							=       TFDPRContextEncoder.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)

											for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Optional[Any]							=       TFDPRQuestionEncoder.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)

											for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__magic_name__:	Optional[int]							=       TFDPRReader.from_pretrained(__snake_case					)
																	self.assertIsNotNone(__snake_case					)



@require_tf
class       __A		(   unittest.TestCase			):

					@slow
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							Optional[int]:
											__magic_name__:	int							=       TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""					)

											__magic_name__:	Optional[Any]							=       tf.constant(
											    [[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_0_3, 2_0_2_6, 3_8_9_9, 1_0_1_4_0, 1_0_2_9, 1_0_2]]					)  # [CLS] hello, is my dog cute? [SEP]
											__magic_name__:	Optional[Any]							=       model(__snake_case					)[0]  # embedding shape = (1, 768)
											# compare the actual values for a slice.
											__magic_name__:	List[Any]							=       tf.constant(
											    [
											        [
											            0.03236253,
											            0.12753335,
											            0.16818509,
											            0.00279786,
											            0.3896933,
											            0.24264945,
											            0.2178971,
											            -0.02335227,
											            -0.08481959,
											            -0.14324117,
											        ]
											    ]					)
											self.assertTrue(numpy.allclose(output[:, :1_0].numpy()     ,		expected_slice.numpy()     ,		atol=1E-4					)					)






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	):




	def __init__(     self						:     Optional[int]					,      __A						:     Union[str, Any]					,      __A						:     int=7					,      __A						:     int=3					,      __A						:     int=30					,      __A						:     Dict=400					,      __A						:     str=True					,      __A						:     str=None					,      __A						:     str=True					,      __A						:     Optional[int]=[0.5, 0.5, 0.5]					,      __A						:     List[str]=[0.5, 0.5, 0.5]					,      __A						:     Optional[Any]=True					,      __A						:     int=1 / 255					,      __A						:     List[Any]=True					,      ):
		# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
		__A :			Union[str, Any]		    =			parent
		__A :			Union[str, Any]		    =			batch_size
		__A :			Union[str, Any]		    =			num_channels
		__A :			Optional[Any]		    =			min_resolution
		__A :			Union[str, Any]		    =			max_resolution
		__A :			Any		    =			do_resize
		__A :			Union[str, Any]		    =			size
		__A :			Optional[int]		    =			do_normalize
		__A :			Dict		    =			image_mean
		__A :			Optional[int]		    =			image_std
		__A :			Tuple		    =			do_rescale
		__A :			Optional[Any]		    =			rescale_factor
		__A :			Tuple		    =			do_pad




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

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

		return expected_height, expected_width






@require_torch
@require_vision
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				DetaImageProcessor if is_vision_available() else None




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Tuple		    =			DetaImageProcessingTester(self							)




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return self.image_processor_tester.prepare_image_processor_dict()




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




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Dict		    =			self.image_processing_class.from_dict(self.image_processor_dict							)
		self.assertEqual(image_processor.size					,      {"""shortest_edge""": 18, """longest_edge""": 1333}							)
		self.assertEqual(image_processor.do_pad					,      __A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		__A :			List[Any]		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      Image.Image							)

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

		__A    ,				__A :			int		    =			self.image_processor_tester.get_expected_values(__A							)

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

		# Test batched
		__A    ,				__A :			List[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

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

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

		__A    ,				__A :			Optional[int]		    =			self.image_processor_tester.get_expected_values(__A							)

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

		# Test batched
		__A :			int		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

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

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

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

		__A    ,				__A :			Optional[int]		    =			self.image_processor_tester.get_expected_values(__A							)

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

		# Test batched
		__A :			Tuple		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Any		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

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

		__A :			int		    =			{"""image_id""": 3_9769, """annotations""": target}

		# encode them
		__A :			List[str]		    =			DetaImageProcessor()
		__A :			List[str]		    =			image_processing(images=__A					,      annotations=__A					,      return_tensors="""pt"""							)

		# verify pixel values
		__A :			Tuple		    =			torch.Size([1, 3, 800, 1066]							)
		self.assertEqual(encoding["""pixel_values"""].shape					,      __A							)

		__A :			Union[str, Any]		    =			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]					,      __A					,      atol=1e-4							)							)

		# verify area
		__A :			Tuple		    =			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"""]					,      __A							)							)
		# verify boxes
		__A :			Tuple		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			List[str]		    =			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]					,      __A					,      atol=1e-3							)							)
		# verify image_id
		__A :			List[str]		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Dict		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			List[Any]		    =			torch.tensor([75, 75, 63, 65, 17, 17]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify orig_size
		__A :			str		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# prepare image, target and masks_path
		__A :			Optional[int]		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""					,      """r"""							) as f:
			__A :			Tuple		    =			json.loads(f.read()							)

		__A :			Optional[int]		    =			{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

		__A :			Any		    =			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

		# encode them
		__A :			Any		    =			DetaImageProcessor(format="""coco_panoptic"""							)
		__A :			int		    =			image_processing(images=__A					,      annotations=__A					,      masks_path=__A					,      return_tensors="""pt"""							)

		# verify pixel values
		__A :			Tuple		    =			torch.Size([1, 3, 800, 1066]							)
		self.assertEqual(encoding["""pixel_values"""].shape					,      __A							)

		__A :			Optional[Any]		    =			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]					,      __A					,      atol=1e-4							)							)

		# verify area
		__A :			Union[str, Any]		    =			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"""]					,      __A							)							)
		# verify boxes
		__A :			Union[str, Any]		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			Union[str, Any]		    =			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]					,      __A					,      atol=1e-3							)							)
		# verify image_id
		__A :			Any		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Any		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			Optional[Any]		    =			torch.tensor([17, 17, 63, 75, 75, 93]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify masks
		__A :			List[str]		    =			82_2873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,      __A							)
		# verify orig_size
		__A :			Tuple		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)





