


'''simple docstring'''
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


__snake_case							      =     pytest.mark.integration
@require_faiss
class    lowercase	(							A__					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(UpperCamelCase_							) for x in np.arange(30							).tolist()]}							)
							return dset




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :Dataset												= self._create_dummy_dataset()
							UpperCamelCase__   :Any												= dset.map(
							    lambda UpperCamelCase_    ,   UpperCamelCase_							: {"vecs": i * np.ones(5    ,   dtype=np.floataa							)}    ,   with_indices=UpperCamelCase_    ,   keep_in_memory=UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= dset.add_faiss_index('''vecs'''    ,   batch_size=100    ,   metric_type=faiss.METRIC_INNER_PRODUCT							)
							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= 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							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :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    ,   )
							UpperCamelCase__					,				UpperCamelCase__   :str												= dset.get_nearest_examples('''vecs'''    ,   np.ones(5    ,   dtype=np.floataa							)							)
							self.assertEqual(examples['''filename'''][0]    ,   '''my_name-train_29'''							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :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=UpperCamelCase_							) as tmp_file:
												dset.save_faiss_index('''vecs'''    ,   tmp_file.name							)
												dset.load_faiss_index('''vecs2'''    ,   tmp_file.name							)
							os.unlink(tmp_file.name							)

							UpperCamelCase__					,				UpperCamelCase__   :str												= dset.get_nearest_examples('''vecs2'''    ,   np.ones(5    ,   dtype=np.floataa							)							)
							self.assertEqual(examples['''filename'''][0]    ,   '''my_name-train_29'''							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :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(UpperCamelCase_    ,   partial(dset.get_nearest_examples    ,   '''vecs2'''    ,   np.ones(5    ,   dtype=np.floataa							)							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							from elasticsearch import Elasticsearch

							UpperCamelCase__   :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:
												UpperCamelCase__   :Optional[int]												= {'''acknowledged''': True}
												mocked_bulk.return_value([(True, None)] * 30							)
												UpperCamelCase__   :str												= {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
												UpperCamelCase__   :Optional[Any]												= Elasticsearch()

												dset.add_elasticsearch_index('''filename'''    ,   es_client=UpperCamelCase_							)
												UpperCamelCase__					,				UpperCamelCase__   :Tuple												= dset.get_nearest_examples('''filename'''    ,   '''my_name-train_29'''							)
												self.assertEqual(examples['''filename'''][0]    ,   '''my_name-train_29'''							)



@require_faiss
class    lowercase	(							A__					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :List[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
							UpperCamelCase__   :Any												= np.zeros(5    ,   dtype=np.floataa							)
							UpperCamelCase__   :Optional[int]												= 1
							UpperCamelCase__					,				UpperCamelCase__   :str												= index.search(UpperCamelCase_							)
							self.assertRaises(UpperCamelCase_    ,   index.search    ,   query.reshape(-1    ,   1							)							)
							self.assertGreater(scores[0]    ,   0							)
							self.assertEqual(indices[0]    ,   1							)

							# batched queries
							UpperCamelCase__   :List[str]												= np.eye(5    ,   dtype=np.floataa							)[::-1]
							UpperCamelCase__					,				UpperCamelCase__   :Optional[Any]												= index.search_batch(UpperCamelCase_							)
							self.assertRaises(UpperCamelCase_    ,   index.search_batch    ,   queries[0]							)
							UpperCamelCase__   :List[Any]												= [scores[0] for scores in total_scores]
							UpperCamelCase__   :Tuple												= [indices[0] for indices in total_indices]
							self.assertGreater(np.min(UpperCamelCase_							)    ,   0							)
							self.assertListEqual([4, 3, 2, 1, 0]    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :Tuple												= FaissIndex(string_factory='''Flat'''							)
							index.add_vectors(np.eye(5    ,   dtype=np.floataa							)							)
							self.assertIsInstance(index.faiss_index    ,   faiss.IndexFlat							)
							UpperCamelCase__   :Dict												= FaissIndex(string_factory='''LSH'''							)
							index.add_vectors(np.eye(5    ,   dtype=np.floataa							)							)
							self.assertIsInstance(index.faiss_index    ,   faiss.IndexLSH							)
							with self.assertRaises(UpperCamelCase_							):
												UpperCamelCase__   :Optional[int]												= FaissIndex(string_factory='''Flat'''    ,   custom_index=faiss.IndexFlat(5							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :List[Any]												= faiss.IndexFlat(5							)
							UpperCamelCase__   :List[Any]												= FaissIndex(custom_index=UpperCamelCase_							)
							index.add_vectors(np.eye(5    ,   dtype=np.floataa							)							)
							self.assertIsInstance(index.faiss_index    ,   faiss.IndexFlat							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							import faiss

							UpperCamelCase__   :str												= 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=UpperCamelCase_							) as tmp_file:
												index.save(tmp_file.name							)
												UpperCamelCase__   :Optional[Any]												= FaissIndex.load(tmp_file.name							)
							os.unlink(tmp_file.name							)

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



@require_faiss
def 			a      (			__a   )  ->	Tuple:



					'''simple docstring'''






					import faiss

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

					UpperCamelCase__   :Optional[int]												= '''index.faiss'''
					UpperCamelCase__   :Tuple												= f'''mock://{index_name}'''
					index.save(__a ,					storage_options=mockfs.storage_options   )
					UpperCamelCase__   :List[Any]												= FaissIndex.load(__a ,					storage_options=mockfs.storage_options   )

					UpperCamelCase__   :Tuple												= np.zeros(5 ,					dtype=np.floataa   )
					UpperCamelCase__   :int												= 1
					UpperCamelCase__					,				UpperCamelCase__   :str												= index.search(__a   )
					assert scores[0] > 0
					assert indices[0] == 1







@require_elasticsearch
class    lowercase	(							A__					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							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:
												UpperCamelCase__   :Any												= Elasticsearch()
												UpperCamelCase__   :Any												= {'''acknowledged''': True}
												UpperCamelCase__   :int												= ElasticSearchIndex(es_client=UpperCamelCase_							)
												mocked_bulk.return_value([(True, None)] * 3							)
												index.add_documents(['''foo''', '''bar''', '''foobar''']							)

												# single query
												UpperCamelCase__   :Any												= '''foo'''
												UpperCamelCase__   :Dict												= {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
												UpperCamelCase__					,				UpperCamelCase__   :int												= index.search(UpperCamelCase_							)
												self.assertEqual(scores[0]    ,   1							)
												self.assertEqual(indices[0]    ,   0							)

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

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

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



'''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 ConditionalDetrImageProcessor
class    lowercase	(							unittest.TestCase					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=7    ,   UpperCamelCase_=3    ,   UpperCamelCase_=30    ,   UpperCamelCase_=400    ,   UpperCamelCase_=True    ,   UpperCamelCase_=None    ,   UpperCamelCase_=True    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=[0.5, 0.5, 0.5]    ,   UpperCamelCase_=True    ,   UpperCamelCase_=1 / 255    ,   UpperCamelCase_=True    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
							UpperCamelCase__   :str												= parent
							UpperCamelCase__   :List[Any]												= batch_size
							UpperCamelCase__   :Dict												= num_channels
							UpperCamelCase__   :str												= min_resolution
							UpperCamelCase__   :Optional[Any]												= max_resolution
							UpperCamelCase__   :int												= do_resize
							UpperCamelCase__   :Optional[Any]												= size
							UpperCamelCase__   :Tuple												= do_normalize
							UpperCamelCase__   :List[Any]												= image_mean
							UpperCamelCase__   :Dict												= image_std
							UpperCamelCase__   :Union[str, Any]												= do_rescale
							UpperCamelCase__   :Union[str, Any]												= rescale_factor
							UpperCamelCase__   :Union[str, Any]												= do_pad




		def     lowerCAmelCase__				(		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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


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

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

							return expected_height, expected_width



@require_torch
@require_vision
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				ConditionalDetrImageProcessor if is_vision_available() else None




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessingTester(self							)




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :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_    ,   '''size'''							)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= 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    ,   UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= self.image_processing_class.from_dict(
							    self.image_processor_dict    ,   size=42    ,   max_size=84    ,   pad_and_return_pixel_mask=UpperCamelCase_							)
							self.assertEqual(image_processor.size    ,   {'''shortest_edge''': 42, '''longest_edge''': 84}							)
							self.assertEqual(image_processor.do_pad    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							UpperCamelCase__   :List[str]												= prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=UpperCamelCase_							)
							for image in image_inputs:
												self.assertIsInstance(UpperCamelCase_    ,   Image.Image							)

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

							UpperCamelCase__					,				UpperCamelCase__   :str												= 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
							UpperCamelCase__					,				UpperCamelCase__   :str												= self.image_processor_tester.get_expected_values(UpperCamelCase_    ,   batched=UpperCamelCase_							)

							UpperCamelCase__   :List[str]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__					,				UpperCamelCase__   :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
							UpperCamelCase__   :Union[str, Any]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__					,				UpperCamelCase__   :Dict												= 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
							UpperCamelCase__   :List[str]												= image_processing(UpperCamelCase_    ,   return_tensors='''pt'''							).pixel_values

							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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

							UpperCamelCase__   :Any												= {'''image_id''': 39769, '''annotations''': target}

							# encode them
							UpperCamelCase__   :str												= ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''							)
							UpperCamelCase__   :List[Any]												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   return_tensors='''pt'''							)

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

							UpperCamelCase__   :str												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :str												= torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Optional[Any]												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[Any]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :int												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :List[str]												= torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify orig_size
							UpperCamelCase__   :Tuple												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,   '''r'''							) as f:
												UpperCamelCase__   :Tuple												= json.loads(f.read()							)

							UpperCamelCase__   :List[str]												= {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

							UpperCamelCase__   :Any												= pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

							# encode them
							UpperCamelCase__   :List[Any]												= ConditionalDetrImageProcessor(format='''coco_panoptic'''							)
							UpperCamelCase__   :Dict												= image_processing(images=UpperCamelCase_    ,   annotations=UpperCamelCase_    ,   masks_path=UpperCamelCase_    ,   return_tensors='''pt'''							)

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

							UpperCamelCase__   :Optional[int]												= torch.tensor([0.2796, 0.3138, 0.3481]							)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)

							# verify area
							UpperCamelCase__   :Tuple												= torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,   UpperCamelCase_							)							)
							# verify boxes
							UpperCamelCase__   :Any												= torch.Size([6, 4]							)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :List[Any]												= torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,   UpperCamelCase_    ,   atol=1e-3							)							)
							# verify image_id
							UpperCamelCase__   :List[str]												= torch.tensor([39769]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,   UpperCamelCase_							)							)
							# verify is_crowd
							UpperCamelCase__   :Union[str, Any]												= torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,   UpperCamelCase_							)							)
							# verify class_labels
							UpperCamelCase__   :str												= torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,   UpperCamelCase_							)							)
							# verify masks
							UpperCamelCase__   :Optional[Any]												= 822873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,   UpperCamelCase_							)
							# verify orig_size
							UpperCamelCase__   :List[str]												= torch.tensor([480, 640]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,   UpperCamelCase_							)							)
							# verify size
							UpperCamelCase__   :List[Any]												= torch.tensor([800, 1066]							)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,   UpperCamelCase_							)							)