


import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowerCAmelCase__  :str             =							'''<<<<<<< This should probably be modified because it mentions: '''

lowerCAmelCase__  :int             =							'''=======
>>>>>>>
'''

lowerCAmelCase__  :Dict             =							[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

lowerCAmelCase__  :int             =							[
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'''tfds\.core''', R'''datasets'''),
    (R'''tf\.io\.gfile\.GFile''', R'''open'''),
    (R'''tf\.([\w\d]+)''', R'''datasets.Value(\'\1\')'''),
    (R'''tfds\.features\.Text\(\)''', R'''datasets.Value(\'string\')'''),
    (R'''tfds\.features\.Text\(''', R'''datasets.Value(\'string\'),'''),
    (R'''features\s*=\s*tfds.features.FeaturesDict\(''', R'''features=datasets.Features('''),
    (R'''tfds\.features\.FeaturesDict\(''', R'''dict('''),
    (R'''The TensorFlow Datasets Authors''', R'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (R'''tfds\.''', R'''datasets.'''),
    (R'''dl_manager\.manual_dir''', R'''self.config.data_dir'''),
    (R'''self\.builder_config''', R'''self.config'''),
]

def 	lowerCAmelCase__			(      a__:		Namespace	)  ->		Optional[int]:


							'''simple docstring'''





							return ConvertCommand(args.tfds_path      ,  args.datasets_directory	)







class 		__a				(    UpperCAmelCase      ):
		@staticmethod
		def      UpperCAmelCase__    (      _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      parser.add_parser(
									    'convert'      ,   help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'      ,   )
									train_parser.add_argument(
									    '--tfds_path'      ,   type=_SCREAMING_SNAKE_CASE      ,   required=_SCREAMING_SNAKE_CASE      ,   help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'      ,   )
									train_parser.add_argument(
									    '--datasets_directory'      ,   type=_SCREAMING_SNAKE_CASE      ,   required=_SCREAMING_SNAKE_CASE      ,   help='Path to the HuggingFace Datasets folder.'						)
									train_parser.set_defaults(func=_SCREAMING_SNAKE_CASE						)
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      get_logger('datasets-cli/converting'						)

									_UpperCAmelCase					=      tfds_path
									_UpperCAmelCase					=      datasets_directory






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




									"""simple docstring"""





									if os.path.isdir(self._tfds_path						):
																_UpperCAmelCase					=      os.path.abspath(self._tfds_path						)
									elif os.path.isfile(self._tfds_path						):
																_UpperCAmelCase					=      os.path.dirname(self._tfds_path						)
									else:
																raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'						)

									_UpperCAmelCase					=      os.path.abspath(self._datasets_directory						)

									self._logger.info(f'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''						)

									_UpperCAmelCase					=      []
									_UpperCAmelCase					=      []
									_UpperCAmelCase					=      {}

									if os.path.isdir(self._tfds_path						):
																_UpperCAmelCase					=      os.listdir(_SCREAMING_SNAKE_CASE						)
									else:
																_UpperCAmelCase					=      [os.path.basename(self._tfds_path						)]

									for f_name in file_names:
																self._logger.info(f'''Looking at file {f_name}'''						)
																_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

																if not os.path.isfile(_SCREAMING_SNAKE_CASE						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																							self._logger.info('Skipping file'						)
																							continue

																with open(_SCREAMING_SNAKE_CASE      ,   encoding='utf-8'						) as f:
																							_UpperCAmelCase					=      f.readlines()

																_UpperCAmelCase					=      []
																_UpperCAmelCase					=      False
																_UpperCAmelCase					=      False
																_UpperCAmelCase					=      []
																for line in lines:
																							_UpperCAmelCase					=      line

																							# Convert imports
																							if "import tensorflow.compat.v2 as tf" in out_line:
																														continue
																							elif "@tfds.core" in out_line:
																														continue
																							elif "builder=self" in out_line:
																														continue
																							elif "import tensorflow_datasets.public_api as tfds" in out_line:
																														_UpperCAmelCase					=      'import datasets\n'
																							elif "import tensorflow" in out_line:
																														# order is important here
																														_UpperCAmelCase					=      ''
																														continue
																							elif "from absl import logging" in out_line:
																														_UpperCAmelCase					=      'from datasets import logging\n'
																							elif "getLogger" in out_line:
																														_UpperCAmelCase					=      out_line.replace('getLogger'      ,   'get_logger'						)
																							elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																														_UpperCAmelCase					=      True
																														_UpperCAmelCase					=      list(filter(lambda _SCREAMING_SNAKE_CASE						: e in out_line      ,   _SCREAMING_SNAKE_CASE						)						)
																														out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_SCREAMING_SNAKE_CASE						) + '\n'						)
																														out_lines.append(_SCREAMING_SNAKE_CASE						)
																														out_lines.append(_SCREAMING_SNAKE_CASE						)
																														continue
																							else:
																														for pattern, replacement in TO_CONVERT:
																																					_UpperCAmelCase					=      re.sub(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

                # Take care of saving utilities (to later move them together with main script)
																							if "tensorflow_datasets" in out_line:
																														_UpperCAmelCase					=      re.match(R'from\stensorflow_datasets.*import\s([^\.\r\n]+)'      ,   _SCREAMING_SNAKE_CASE						)
																														tfds_imports.extend(imp.strip() for imp in match.group(1						).split(','						)						)
																														_UpperCAmelCase					=      'from . import ' + match.group(1						)

																							# Check we have not forget anything
																							if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																														raise ValueError(f'''Error converting {out_line.strip()}'''						)

																							if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																														_UpperCAmelCase					=      True
																							out_lines.append(_SCREAMING_SNAKE_CASE						)

																if is_builder or "wmt" in f_name:
																							# We create a new directory for each dataset
																							_UpperCAmelCase					=      f_name.replace('.py'      ,   ''						)
																							_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      os.path.join(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																							os.makedirs(_SCREAMING_SNAKE_CASE      ,   exist_ok=_SCREAMING_SNAKE_CASE						)
																							self._logger.info(f'''Adding directory {output_dir}'''						)
																							imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}						)
																else:
																							# Utilities will be moved at the end
																							utils_files.append(_SCREAMING_SNAKE_CASE						)

																if needs_manual_update:
																							with_manual_update.append(_SCREAMING_SNAKE_CASE						)

																with open(_SCREAMING_SNAKE_CASE      ,   'w'      ,   encoding='utf-8'						) as f:
																							f.writelines(_SCREAMING_SNAKE_CASE						)
																self._logger.info(f'''Converted in {output_file}'''						)

									for utils_file in utils_files:
																try:
																							_UpperCAmelCase					=      os.path.basename(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      imports_to_builder_map[f_name.replace('.py'      ,   ''						)]
																							self._logger.info(f'''Moving {dest_folder} to {utils_file}'''						)
																							shutil.copy(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																except KeyError:
																							self._logger.error(f'''Cannot find destination folder for {utils_file}. Please copy manually.'''						)

									if with_manual_update:
																for file_path in with_manual_update:
																							self._logger.warning(
																							    f'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''						)




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 		__a				(    unittest.TestCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=400      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=1 / 255      ,   _SCREAMING_SNAKE_CASE=True      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      min_resolution
									_UpperCAmelCase					=      max_resolution
									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean
									_UpperCAmelCase					=      image_std
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_pad
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	Optional[Any]:




									"""simple docstring"""





									if not batched:
																_UpperCAmelCase					=      image_inputs[0]
																if isinstance(_SCREAMING_SNAKE_CASE      ,   Image.Image						):
																							_UpperCAmelCase		,	_UpperCAmelCase					=      image.size
																else:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      image.shape[1], image.shape[2]
																if w < h:
																							_UpperCAmelCase					=      int(self.size['shortest_edge'] * h / w						)
																							_UpperCAmelCase					=      self.size['shortest_edge']
																elif w > h:
																							_UpperCAmelCase					=      self.size['shortest_edge']
																							_UpperCAmelCase					=      int(self.size['shortest_edge'] * w / h						)
																else:
																							_UpperCAmelCase					=      self.size['shortest_edge']
																							_UpperCAmelCase					=      self.size['shortest_edge']

									else:
																_UpperCAmelCase					=      []
																for image in image_inputs:
																							_UpperCAmelCase		,	_UpperCAmelCase					=      self.get_expected_values([image]						)
																							expected_values.append((expected_height, expected_width)						)
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[0]						)[0]
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[1]						)[1]

									return expected_height, expected_width



@require_torch
@require_vision
class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Tuple           =     ConditionalDetrImageProcessor if is_vision_available() else None
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      ConditionalDetrImageProcessingTester(self						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_mean'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_std'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_normalize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_resize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'size'						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      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      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.image_processing_class.from_dict(
									    self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 42, 'longest_edge': 84}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   Image.Image						)

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

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

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

									# Test batched
									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   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      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_UpperCAmelCase					=      prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=_SCREAMING_SNAKE_CASE      ,   numpify=_SCREAMING_SNAKE_CASE						)
									for image in image_inputs:
																self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,   np.ndarray						)

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

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

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

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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

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

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE						)

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

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

									_UpperCAmelCase		,	_UpperCAmelCase					=      self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      {'image_id': 39769, 'annotations': target}

									# encode them
									_UpperCAmelCase					=      ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'						)
									_UpperCAmelCase					=      image_processing(images=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									# verify pixel values
									_UpperCAmelCase					=      torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding['pixel_values'].shape      ,   _SCREAMING_SNAKE_CASE						)

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

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






		@slow
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,   'r'						) as f:
																_UpperCAmelCase					=      json.loads(f.read()						)

									_UpperCAmelCase					=      {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

									_UpperCAmelCase					=      pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'						)

									# encode them
									_UpperCAmelCase					=      ConditionalDetrImageProcessor(format='coco_panoptic'						)
									_UpperCAmelCase					=      image_processing(images=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   masks_path=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

									# verify pixel values
									_UpperCAmelCase					=      torch.Size([1, 3, 800, 1066]						)
									self.assertEqual(encoding['pixel_values'].shape      ,   _SCREAMING_SNAKE_CASE						)

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

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