





import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


_lowerCAmelCase 				=     pytest.mark.integration

@require_faiss
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(a__       ) for x in np.arange(30       ).tolist()]}       )
         return dset
      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				Dataset			      =	self._create_dummy_dataset()
         A_     :				List[Any]			      =	dset.map(
             lambda a__      ,       a__       : {"vecs": i * np.ones(5      ,       dtype=np.floataa       )}      ,       with_indices=a__      ,       keep_in_memory=a__       )
         A_     :				str			      =	dset.add_faiss_index("""vecs"""      ,       batch_size=100      ,       metric_type=faiss.METRIC_INNER_PRODUCT       )
         A_      ,				A_     :				int			      =	dset.get_nearest_examples("""vecs"""      ,       np.ones(5      ,       dtype=np.floataa       )       )
         self.assertEqual(examples["""filename"""][0]      ,       """my_name-train_29"""       )
         dset.drop_index("""vecs"""       )
      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				Dataset			      =	self._create_dummy_dataset()
         dset.add_faiss_index_from_external_arrays(
             external_arrays=np.ones((30, 5)       ) * np.arange(30       ).reshape(-1      ,       1       )      ,       index_name="""vecs"""      ,       batch_size=100      ,       metric_type=faiss.METRIC_INNER_PRODUCT      ,       )
         A_      ,				A_     :				str			      =	dset.get_nearest_examples("""vecs"""      ,       np.ones(5      ,       dtype=np.floataa       )       )
         self.assertEqual(examples["""filename"""][0]      ,       """my_name-train_29"""       )
      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				Dataset			      =	self._create_dummy_dataset()
         dset.add_faiss_index_from_external_arrays(
             external_arrays=np.ones((30, 5)       ) * np.arange(30       ).reshape(-1      ,       1       )      ,       index_name="""vecs"""      ,       metric_type=faiss.METRIC_INNER_PRODUCT      ,       )

         # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
         # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
         # see https://bugs.python.org/issue14243 and
         # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
         with tempfile.NamedTemporaryFile(delete=a__       ) as tmp_file:
            dset.save_faiss_index("""vecs"""      ,       tmp_file.name       )
            dset.load_faiss_index("""vecs2"""      ,       tmp_file.name       )
         os.unlink(tmp_file.name       )

         A_      ,				A_     :				List[Any]			      =	dset.get_nearest_examples("""vecs2"""      ,       np.ones(5      ,       dtype=np.floataa       )       )
         self.assertEqual(examples["""filename"""][0]      ,       """my_name-train_29"""       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dataset			      =	self._create_dummy_dataset()
         dset.add_faiss_index_from_external_arrays(
             external_arrays=np.ones((30, 5)       ) * np.arange(30       ).reshape(-1      ,       1       )      ,       index_name="""vecs"""       )
         dset.drop_index("""vecs"""       )
         self.assertRaises(a__      ,       partial(dset.get_nearest_examples      ,       """vecs2"""      ,       np.ones(5      ,       dtype=np.floataa       )       )       )






      def 		_lowerCamelCase      (		self       ):
         from elasticsearch import Elasticsearch

         A_     :				Dataset			      =	self._create_dummy_dataset()
         with patch("""elasticsearch.Elasticsearch.search"""       ) as mocked_search, patch(
             """elasticsearch.client.IndicesClient.create"""       ) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""       ) as mocked_bulk:
            A_     :				Dict			      =	{"""acknowledged""": True}
            mocked_bulk.return_value([(True, None)] * 30       )
            A_     :				List[str]			      =	{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
            A_     :				Tuple			      =	Elasticsearch()

            dset.add_elasticsearch_index("""filename"""      ,       es_client=a__       )
            A_      ,				A_     :				List[str]			      =	dset.get_nearest_examples("""filename"""      ,       """my_name-train_29"""       )
            self.assertEqual(examples["""filename"""][0]      ,       """my_name-train_29"""       )






@require_faiss
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				str			      =	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT       )

         # add vectors
         index.add_vectors(np.eye(5      ,       dtype=np.floataa       )       )
         self.assertIsNotNone(index.faiss_index       )
         self.assertEqual(index.faiss_index.ntotal      ,       5       )
         index.add_vectors(np.zeros((5, 5)      ,       dtype=np.floataa       )       )
         self.assertEqual(index.faiss_index.ntotal      ,       10       )

         # single query
         A_     :				Tuple			      =	np.zeros(5      ,       dtype=np.floataa       )
         A_     :				Tuple			      =	1
         A_      ,				A_     :				List[Any]			      =	index.search(a__       )
         self.assertRaises(a__      ,       index.search      ,       query.reshape(-1      ,       1       )       )
         self.assertGreater(scores[0]      ,       0       )
         self.assertEqual(indices[0]      ,       1       )

         # batched queries
         A_     :				str			      =	np.eye(5      ,       dtype=np.floataa       )[::-1]
         A_      ,				A_     :				Dict			      =	index.search_batch(a__       )
         self.assertRaises(a__      ,       index.search_batch      ,       queries[0]       )
         A_     :				List[Any]			      =	[scores[0] for scores in total_scores]
         A_     :				Optional[int]			      =	[indices[0] for indices in total_indices]
         self.assertGreater(np.min(a__       )      ,       0       )
         self.assertListEqual([4, 3, 2, 1, 0]      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				List[str]			      =	FaissIndex(string_factory="""Flat"""       )
         index.add_vectors(np.eye(5      ,       dtype=np.floataa       )       )
         self.assertIsInstance(index.faiss_index      ,       faiss.IndexFlat       )
         A_     :				Optional[int]			      =	FaissIndex(string_factory="""LSH"""       )
         index.add_vectors(np.eye(5      ,       dtype=np.floataa       )       )
         self.assertIsInstance(index.faiss_index      ,       faiss.IndexLSH       )
         with self.assertRaises(a__       ):
            A_     :				str			      =	FaissIndex(string_factory="""Flat"""      ,       custom_index=faiss.IndexFlat(5       )       )
      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				str			      =	faiss.IndexFlat(5       )
         A_     :				Tuple			      =	FaissIndex(custom_index=a__       )
         index.add_vectors(np.eye(5      ,       dtype=np.floataa       )       )
         self.assertIsInstance(index.faiss_index      ,       faiss.IndexFlat       )






      def 		_lowerCamelCase      (		self       ):
         import faiss

         A_     :				List[Any]			      =	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT       )
         index.add_vectors(np.eye(5      ,       dtype=np.floataa       )       )

         # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
         # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
         # see https://bugs.python.org/issue14243 and
         # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
         with tempfile.NamedTemporaryFile(delete=a__       ) as tmp_file:
            index.save(tmp_file.name       )
            A_     :				Dict			      =	FaissIndex.load(tmp_file.name       )
         os.unlink(tmp_file.name       )

         A_     :				str			      =	np.zeros(5      ,       dtype=np.floataa       )
         A_     :				int			      =	1
         A_      ,				A_     :				List[str]			      =	index.search(a__       )
         self.assertGreater(scores[0]      ,       0       )
         self.assertEqual(indices[0]      ,       1       )






@require_faiss
def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   import faiss

   A_     :				Any			      =	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT     )
   index.add_vectors(np.eye(5  ,dtype=np.floataa     )     )

   A_     :				Union[str, Any]			      =	"""index.faiss"""
   A_     :				List[Any]			      =	f"""mock://{index_name}"""
   index.save(_lowerCAmelCase  ,storage_options=mockfs.storage_options     )
   A_     :				str			      =	FaissIndex.load(_lowerCAmelCase  ,storage_options=mockfs.storage_options     )

   A_     :				Dict			      =	np.zeros(5  ,dtype=np.floataa     )
   A_     :				Optional[int]			      =	1
   A_      ,				A_     :				Optional[Any]			      =	index.search(_lowerCAmelCase     )
   assert scores[0] > 0
   assert indices[0] == 1







@require_elasticsearch
class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def 		_lowerCamelCase      (		self       ):
         from elasticsearch import Elasticsearch

         with patch("""elasticsearch.Elasticsearch.search"""       ) as mocked_search, patch(
             """elasticsearch.client.IndicesClient.create"""       ) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""       ) as mocked_bulk:
            A_     :				str			      =	Elasticsearch()
            A_     :				Optional[Any]			      =	{"""acknowledged""": True}
            A_     :				Union[str, Any]			      =	ElasticSearchIndex(es_client=a__       )
            mocked_bulk.return_value([(True, None)] * 3       )
            index.add_documents(["""foo""", """bar""", """foobar"""]       )

            # single query
            A_     :				int			      =	"""foo"""
            A_     :				Dict			      =	{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
            A_      ,				A_     :				Dict			      =	index.search(a__       )
            self.assertEqual(scores[0]      ,       1       )
            self.assertEqual(indices[0]      ,       0       )

            # single query with timeout
            A_     :				List[Any]			      =	"""foo"""
            A_     :				Dict			      =	{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
            A_      ,				A_     :				Union[str, Any]			      =	index.search(a__      ,       request_timeout=30       )
            self.assertEqual(scores[0]      ,       1       )
            self.assertEqual(indices[0]      ,       0       )

            # batched queries
            A_     :				Tuple			      =	["""foo""", """bar""", """foobar"""]
            A_     :				Any			      =	{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
            A_      ,				A_     :				Optional[Any]			      =	index.search_batch(a__       )
            A_     :				Union[str, Any]			      =	[scores[0] for scores in total_scores]
            A_     :				List[Any]			      =	[indices[0] for indices in total_indices]
            self.assertGreater(np.min(a__       )      ,       0       )
            self.assertListEqual([1, 1, 1]      ,       a__       )

            # batched queries with timeout
            A_     :				List[str]			      =	["""foo""", """bar""", """foobar"""]
            A_     :				List[str]			      =	{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
            A_      ,				A_     :				Optional[int]			      =	index.search_batch(a__      ,       request_timeout=30       )
            A_     :				Any			      =	[scores[0] for scores in total_scores]
            A_     :				List[Any]			      =	[indices[0] for indices in total_indices]
            self.assertGreater(np.min(a__       )      ,       0       )
            self.assertListEqual([1, 1, 1]      ,       a__       )













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    _UpperCAmelCase							(		unittest.TestCase     ):
      def __init__(		self      ,       a__      ,       a__=7      ,       a__=3      ,       a__=30      ,       a__=400      ,       a__=True      ,       a__=None      ,       a__=True      ,       a__=[0.5, 0.5, 0.5]      ,       a__=[0.5, 0.5, 0.5]      ,       a__=True      ,       a__=1 / 255      ,       a__=True      ,       ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         A_     :				Optional[int]			      =	size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
         A_     :				str			      =	parent
         A_     :				Dict			      =	batch_size
         A_     :				str			      =	num_channels
         A_     :				List[str]			      =	min_resolution
         A_     :				List[Any]			      =	max_resolution
         A_     :				Optional[Any]			      =	do_resize
         A_     :				int			      =	size
         A_     :				List[Any]			      =	do_normalize
         A_     :				List[Any]			      =	image_mean
         A_     :				Dict			      =	image_std
         A_     :				Tuple			      =	do_rescale
         A_     :				List[Any]			      =	rescale_factor
         A_     :				Tuple			      =	do_pad
      def 		_lowerCamelCase      (		self       ):
         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      ,       a__      ,       a__=False       ):
         if not batched:
            A_     :				List[str]			      =	image_inputs[0]
            if isinstance(a__      ,       Image.Image       ):
               A_      ,				A_     :				Tuple			      =	image.size
            else:
               A_      ,				A_     :				List[Any]			      =	image.shape[1], image.shape[2]
            if w < h:
               A_     :				List[str]			      =	int(self.size["""shortest_edge"""] * h / w       )
               A_     :				Tuple			      =	self.size["""shortest_edge"""]
            elif w > h:
               A_     :				Tuple			      =	self.size["""shortest_edge"""]
               A_     :				Union[str, Any]			      =	int(self.size["""shortest_edge"""] * w / h       )
            else:
               A_     :				Optional[Any]			      =	self.size["""shortest_edge"""]
               A_     :				int			      =	self.size["""shortest_edge"""]

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

         return expected_height, expected_width






@require_torch
@require_vision
class    _UpperCAmelCase							(		_lowerCamelCase			,       unittest.TestCase     ):
      a						       =							DetaImageProcessor if is_vision_available() else None
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	DetaImageProcessingTester(self       )
      @property
      def 		_lowerCamelCase      (		self       ):
         return self.image_processor_tester.prepare_image_processor_dict()
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	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       ):
         A_     :				Any			      =	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       ):
         pass
      def 		_lowerCamelCase      (		self       ):
         # Initialize image_processing
         A_     :				List[Any]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_     :				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_     :				List[Any]			      =	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_      ,				A_     :				Optional[Any]			      =	self.image_processor_tester.get_expected_values(a__      ,       batched=a__       )

         A_     :				Optional[int]			      =	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       ):
         # Initialize image_processing
         A_     :				Any			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_     :				int			      =	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_     :				Union[str, Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Tuple			      =	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_     :				Optional[int]			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				Union[str, 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       ):
         # Initialize image_processing
         A_     :				List[str]			      =	self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_     :				Union[str, Any]			      =	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_     :				Optional[Any]			      =	image_processing(image_inputs[0]      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				str			      =	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_     :				str			      =	image_processing(a__      ,       return_tensors="""pt"""       ).pixel_values

         A_      ,				A_     :				List[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       ):
         # prepare image and target
         A_     :				int			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,       """r"""       ) as f:
            A_     :				int			      =	json.loads(f.read()       )

         A_     :				Union[str, Any]			      =	{"""image_id""": 39769, """annotations""": target}

         # encode them
         A_     :				str			      =	DetaImageProcessor()
         A_     :				int			      =	image_processing(images=a__      ,       annotations=a__      ,       return_tensors="""pt"""       )

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

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

         # verify area
         A_     :				int			      =	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"""]      ,       a__       )       )
         # verify boxes
         A_     :				str			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[Any]			      =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       a__      ,       atol=1E-3       )       )
         # verify image_id
         A_     :				Optional[Any]			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Optional[int]			      =	torch.tensor([75, 75, 63, 65, 17, 17]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify orig_size
         A_     :				Dict			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Tuple			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






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

         A_     :				Any			      =	{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

         A_     :				Tuple			      =	pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

         # encode them
         A_     :				str			      =	DetaImageProcessor(format="""coco_panoptic"""       )
         A_     :				Tuple			      =	image_processing(images=a__      ,       annotations=a__      ,       masks_path=a__      ,       return_tensors="""pt"""       )

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

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

         # verify area
         A_     :				Dict			      =	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"""]      ,       a__       )       )
         # verify boxes
         A_     :				List[Any]			      =	torch.Size([6, 4]       )
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,       a__       )
         A_     :				List[str]			      =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,       a__      ,       atol=1E-3       )       )
         # verify image_id
         A_     :				int			      =	torch.tensor([39769]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,       a__       )       )
         # verify is_crowd
         A_     :				str			      =	torch.tensor([0, 0, 0, 0, 0, 0]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,       a__       )       )
         # verify class_labels
         A_     :				Union[str, Any]			      =	torch.tensor([17, 17, 63, 75, 75, 93]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,       a__       )       )
         # verify masks
         A_     :				Tuple			      =	822873
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,       a__       )
         # verify orig_size
         A_     :				Union[str, Any]			      =	torch.tensor([480, 640]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,       a__       )       )
         # verify size
         A_     :				Union[str, Any]			      =	torch.tensor([800, 1066]       )
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,       a__       )       )






