
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union

from .. import config
from .filelock import FileLock
from .logging import get_logger


__magic_name__  							=	get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	:
      def __init__(      self		,						lowerCamelCase = None			):
          snake_case__    =					(
              os.path.join(lowerCamelCase		,						config.EXTRACTED_DATASETS_DIR			) if cache_dir else config.EXTRACTED_DATASETS_PATH
          )
          snake_case__    =					Extractor
      def A_      (      self		,						lowerCamelCase			):
          from .file_utils import hash_url_to_filename

          # Path where we extract compressed archives
          # We extract in the cache dir, and get the extracted path name by hashing the original path"
          snake_case__    =					os.path.abspath(lowerCamelCase			)
          return os.path.join(self.extract_dir		,						hash_url_to_filename(lowerCamelCase			)			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase			):
          return force_extract or (
              not os.path.isfile(lowerCamelCase			) and not (os.path.isdir(lowerCamelCase			) and os.listdir(lowerCamelCase			))
          )



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase = False			):
          snake_case__    =					self.extractor.infer_extractor_format(lowerCamelCase			)
          if not extractor_format:
              return input_path
          snake_case__    =					self._get_output_path(lowerCamelCase			)
          if self._do_extract(lowerCamelCase		,						lowerCamelCase			):
              self.extractor.extract(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)
          return output_path





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      @classmethod
      @abstractmethod
      def A_      (      cls		,						lowerCamelCase		,						**lowerCamelCase			):
          ...



      @staticmethod
      @abstractmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          ...





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       __UpperCamelCase			):
      _A    :		List[bytes]     						=  []
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          with open(lowerCamelCase		,						"rb"			) as f:
              return f.read(lowerCamelCase			)



      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						lowerCamelCase = b""			):
          if not magic_number:
              snake_case__    =					max(len(lowerCamelCase			) for cls_magic_number in cls.magic_numbers			)
              try:
                  snake_case__    =					cls.read_magic_number(lowerCamelCase		,						lowerCamelCase			)
              except OSError:
                  return False
          return any(magic_number.startswith(lowerCamelCase			) for cls_magic_number in cls.magic_numbers			)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						**lowerCamelCase			):
          return tarfile.is_tarfile(lowerCamelCase			)
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):

          def resolved(lowerCamelCase			) -> str:
              return os.path.realpath(os.path.abspath(lowerCamelCase			)			)

          def badpath(lowerCamelCase		,						lowerCamelCase			) -> bool:
              # joinpath will ignore base if path is absolute
              return not resolved(os.path.join(lowerCamelCase		,						lowerCamelCase			)			).startswith(lowerCamelCase			)

          def badlink(lowerCamelCase		,						lowerCamelCase			) -> bool:
              # Links are interpreted relative to the directory containing the link
              snake_case__    =					resolved(os.path.join(lowerCamelCase		,						os.path.dirname(info.name			)			)			)
              return badpath(info.linkname		,						base=lowerCamelCase			)

          snake_case__    =					resolved(lowerCamelCase			)

          for finfo in members:
              if badpath(finfo.name		,						lowerCamelCase			):
                  logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)"""			)
              elif finfo.issym() and badlink(lowerCamelCase		,						lowerCamelCase			):
                  logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}"""			)
              elif finfo.islnk() and badlink(lowerCamelCase		,						lowerCamelCase			):
                  logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}"""			)
              else:
                  yield finfo



      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          os.makedirs(lowerCamelCase		,						exist_ok=lowerCamelCase			)
          snake_case__    =					tarfile.open(lowerCamelCase			)
          tar_file.extractall(lowerCamelCase		,						members=TarExtractor.safemembers(lowerCamelCase		,						lowerCamelCase			)			)
          tar_file.close()





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		List[str]     						=  [b'\x1F\x8B']
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          with gzip.open(lowerCamelCase		,						"rb"			) as gzip_file:
              with open(lowerCamelCase		,						"wb"			) as extracted_file:
                  shutil.copyfileobj(lowerCamelCase		,						lowerCamelCase			)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Optional[Any]     						=  [
          b'PK\x03\x04',
          b'PK\x05\x06',  # empty archive
          b'PK\x07\x08',  # spanned archive
      ]
      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						lowerCamelCase = b""			):
          if super().is_extractable(lowerCamelCase		,						magic_number=lowerCamelCase			):
              return True
          try:
              # Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
              # From: https://github.com/python/cpython/pull/5053
              from zipfile import (
                  _CD_SIGNATURE,
                  _ECD_DISK_NUMBER,
                  _ECD_DISK_START,
                  _ECD_ENTRIES_TOTAL,
                  _ECD_OFFSET,
                  _ECD_SIZE,
                  _EndRecData,
                  sizeCentralDir,
                  stringCentralDir,
                  structCentralDir,
              )

              with open(lowerCamelCase		,						"rb"			) as fp:
                  snake_case__    =					_EndRecData(lowerCamelCase			)
                  if endrec:
                      if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
                          return True  # Empty zipfiles are still zipfiles
                      elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
                          fp.seek(endrec[_ECD_OFFSET]			)  # Central directory is on the same disk
                          if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
                              snake_case__    =					fp.read(lowerCamelCase			)  # CD is where we expect it to be
                              if len(lowerCamelCase			) == sizeCentralDir:
                                  snake_case__    =					struct.unpack(lowerCamelCase		,						lowerCamelCase			)  # CD is the right size
                                  if centdir[_CD_SIGNATURE] == stringCentralDir:
                                      return True  # First central directory entry  has correct magic number
              return False
          except Exception:  # catch all errors in case future python versions change the zipfile internals
              return False



      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          os.makedirs(lowerCamelCase		,						exist_ok=lowerCamelCase			)
          with zipfile.ZipFile(lowerCamelCase		,						"r"			) as zip_file:
              zip_file.extractall(lowerCamelCase			)
              zip_file.close()





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Optional[int]     						=  [b'\xFD\x37\x7A\x58\x5A\x00']
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          with lzma.open(lowerCamelCase			) as compressed_file:
              with open(lowerCamelCase		,						"wb"			) as extracted_file:
                  shutil.copyfileobj(lowerCamelCase		,						lowerCamelCase			)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Any     						=  [b'Rar!\x1a\x07\x00', b'Rar!\x1a\x07\x01\x00']  # RAR_ID  # RAR5_ID
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          if not config.RARFILE_AVAILABLE:
              raise ImportError("Please pip install rarfile"			)
          import rarfile

          os.makedirs(lowerCamelCase		,						exist_ok=lowerCamelCase			)
          snake_case__    =					rarfile.RarFile(lowerCamelCase			)
          rf.extractall(lowerCamelCase			)
          rf.close()





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Union[str, Any]     						=  [b'\x28\xb5\x2F\xFD']
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          if not config.ZSTANDARD_AVAILABLE:
              raise ImportError("Please pip install zstandard"			)
          import zstandard as zstd

          snake_case__    =					zstd.ZstdDecompressor()
          with open(lowerCamelCase		,						"rb"			) as ifh, open(lowerCamelCase		,						"wb"			) as ofh:
              dctx.copy_stream(lowerCamelCase		,						lowerCamelCase			)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		str     						=  [b'\x42\x5A\x68']
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          with bza.open(lowerCamelCase		,						"rb"			) as compressed_file:
              with open(lowerCamelCase		,						"wb"			) as extracted_file:
                  shutil.copyfileobj(lowerCamelCase		,						lowerCamelCase			)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Tuple     						=  [b'\x37\x7A\xBC\xAF\x27\x1C']
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          if not config.PY7ZR_AVAILABLE:
              raise ImportError("Please pip install py7zr"			)
          import pyazr

          os.makedirs(lowerCamelCase		,						exist_ok=lowerCamelCase			)
          with pyazr.SevenZipFile(lowerCamelCase		,						"r"			) as archive:
              archive.extractall(lowerCamelCase			)





class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Optional[int]     						=  [b'\x04\x22\x4D\x18']
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          if not config.LZ4_AVAILABLE:
              raise ImportError("Please pip install lz4"			)
          import lza.frame

          with lza.frame.open(lowerCamelCase		,						"rb"			) as compressed_file:
              with open(lowerCamelCase		,						"wb"			) as extracted_file:
                  shutil.copyfileobj(lowerCamelCase		,						lowerCamelCase			)





class        _SCREAMING_SNAKE_CASE	:
      #  Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
      _A    :		Dict[str, Type[BaseExtractor]]     						=  {
          "tar": TarExtractor,
          "gzip": GzipExtractor,
          "zip": ZipExtractor,
          "xz": XzExtractor,
          "rar": RarExtractor,
          "zstd": ZstdExtractor,
          "bz2": BzipaExtractor,
          "7z": SevenZipExtractor,  # <Added version="2.4.0"/>
          "lz4": LzaExtractor,  # <Added version="2.4.0"/>
      }
      @classmethod
      def A_      (      cls			):
          return max(
              len(lowerCamelCase			)
              for extractor in cls.extractors.values()
              if issubclass(lowerCamelCase		,						lowerCamelCase			)
              for extractor_magic_number in extractor.magic_numbers			)
      @staticmethod
      def A_      (      lowerCamelCase		,						lowerCamelCase			):
          try:
              return MagicNumberBaseExtractor.read_magic_number(lowerCamelCase		,						magic_number_length=lowerCamelCase			)
          except OSError:
              return b""
      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						lowerCamelCase = False			):
          warnings.warn(
              "Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
              "Use 'infer_extractor_format' instead."		,						category=lowerCamelCase		,						)
          snake_case__    =					cls.infer_extractor_format(lowerCamelCase			)
          if extractor_format:
              return True if not return_extractor else (True, cls.extractors[extractor_format])
          return False if not return_extractor else (False, None)
      @classmethod
      def A_      (      cls		,						lowerCamelCase			):  # <Added version="2.4.0"/>
          snake_case__    =					cls._get_magic_number_max_length()
          snake_case__    =					cls._read_magic_number(lowerCamelCase		,						lowerCamelCase			)
          for extractor_format, extractor in cls.extractors.items():
              if extractor.is_extractable(lowerCamelCase		,						magic_number=lowerCamelCase			):
                  return extractor_format



      @classmethod
      def A_      (      cls		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase = None		,						lowerCamelCase = "deprecated"		,						):
          os.makedirs(os.path.dirname(lowerCamelCase			)		,						exist_ok=lowerCamelCase			)
          # Prevent parallel extractions
          snake_case__    =					str(Path(lowerCamelCase			).with_suffix(".lock"			)			)
          with FileLock(lowerCamelCase			):
              shutil.rmtree(lowerCamelCase		,						ignore_errors=lowerCamelCase			)
              if extractor_format or extractor != "deprecated":
                  if extractor != "deprecated" or not isinstance(lowerCamelCase		,						lowerCamelCase			):  # passed as positional arg
                      warnings.warn(
                          "Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
                          "Use 'extractor_format' instead."		,						category=lowerCamelCase		,						)
                      snake_case__    =					extractor if extractor != "deprecated" else extractor_format
                  else:
                      snake_case__    =					cls.extractors[extractor_format]
                  return extractor.extract(lowerCamelCase		,						lowerCamelCase			)
              else:
                  warnings.warn(
                      "Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
                      "exception in 3.0.0."		,						category=lowerCamelCase		,						)
                  for extractor in cls.extractors.values():
                      if extractor.is_extractable(lowerCamelCase			):
                          return extractor.extract(lowerCamelCase		,						lowerCamelCase			)





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        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase=7		,						lowerCamelCase=3		,						lowerCamelCase=30		,						lowerCamelCase=4_00		,						lowerCamelCase=True		,						lowerCamelCase=None		,						lowerCamelCase=True		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=[0.5, 0.5, 0.5]		,						lowerCamelCase=True		,						lowerCamelCase=1 / 2_55		,						lowerCamelCase=True		,						):
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          snake_case__    =					size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					num_channels
          snake_case__    =					min_resolution
          snake_case__    =					max_resolution
          snake_case__    =					do_resize
          snake_case__    =					size
          snake_case__    =					do_normalize
          snake_case__    =					image_mean
          snake_case__    =					image_std
          snake_case__    =					do_rescale
          snake_case__    =					rescale_factor
          snake_case__    =					do_pad
      def A_      (      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 A_      (      self		,						lowerCamelCase		,						lowerCamelCase=False			):
          if not batched:
              snake_case__    =					image_inputs[0]
              if isinstance(lowerCamelCase		,						Image.Image			):
                  snake_case__    ,       snake_case__    =					image.size
              else:
                  snake_case__    ,       snake_case__    =					image.shape[1], image.shape[2]
              if w < h:
                  snake_case__    =					int(self.size["shortest_edge"] * h / w			)
                  snake_case__    =					self.size["shortest_edge"]
              elif w > h:
                  snake_case__    =					self.size["shortest_edge"]
                  snake_case__    =					int(self.size["shortest_edge"] * w / h			)
              else:
                  snake_case__    =					self.size["shortest_edge"]
                  snake_case__    =					self.size["shortest_edge"]

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

          return expected_height, expected_width





@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		List[str]     						=  DetaImageProcessor if is_vision_available() else None
      def A_      (      self			):
          snake_case__    =					DetaImageProcessingTester(self			)
      @property
      def A_      (      self			):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_      (      self			):
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowerCamelCase		,						"image_mean"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"image_std"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_normalize"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_resize"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_rescale"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_pad"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"size"			)			)
      def A_      (      self			):
          snake_case__    =					self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size		,						{"shortest_edge": 18, "longest_edge": 13_33}			)
          self.assertEqual(image_processor.do_pad		,						lowerCamelCase			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          snake_case__    =					prepare_image_inputs(self.image_processor_tester		,						equal_resolution=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						Image.Image			)

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

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase			)

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

          # Test batched
          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          snake_case__    =					image_processing(lowerCamelCase		,						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 A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          snake_case__    =					prepare_image_inputs(self.image_processor_tester		,						equal_resolution=lowerCamelCase		,						numpify=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						np.ndarray			)

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

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase			)

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

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,						)
      def A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          snake_case__    =					prepare_image_inputs(self.image_processor_tester		,						equal_resolution=lowerCamelCase		,						torchify=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						torch.Tensor			)

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

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase			)

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

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values

          snake_case__    ,       snake_case__    =					self.image_processor_tester.get_expected_values(lowerCamelCase		,						batched=lowerCamelCase			)

          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )		,						)
      @slow
      def A_      (      self			):
          # prepare image and target
          snake_case__    =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
          with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"		,						"r"			) as f:
              snake_case__    =					json.loads(f.read()			)

          snake_case__    =					{"image_id": 3_97_69, "annotations": target}

          # encode them
          snake_case__    =					DetaImageProcessor()
          snake_case__    =					image_processing(images=lowerCamelCase		,						annotations=lowerCamelCase		,						return_tensors="pt"			)

          # verify pixel values
          snake_case__    =					torch.Size([1, 3, 8_00, 10_66]			)
          self.assertEqual(encoding["pixel_values"].shape		,						lowerCamelCase			)

          snake_case__    =					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]		,						lowerCamelCase		,						atol=1e-4			)			)

          # verify area
          snake_case__    =					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"]		,						lowerCamelCase			)			)
          # verify boxes
          snake_case__    =					torch.Size([6, 4]			)
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,						lowerCamelCase			)
          snake_case__    =					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]		,						lowerCamelCase		,						atol=1e-3			)			)
          # verify image_id
          snake_case__    =					torch.tensor([3_97_69]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						lowerCamelCase			)			)
          # verify is_crowd
          snake_case__    =					torch.tensor([0, 0, 0, 0, 0, 0]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						lowerCamelCase			)			)
          # verify class_labels
          snake_case__    =					torch.tensor([75, 75, 63, 65, 17, 17]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						lowerCamelCase			)			)
          # verify orig_size
          snake_case__    =					torch.tensor([4_80, 6_40]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						lowerCamelCase			)			)
          # verify size
          snake_case__    =					torch.tensor([8_00, 10_66]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						lowerCamelCase			)			)



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

          snake_case__    =					{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

          snake_case__    =					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"			)

          # encode them
          snake_case__    =					DetaImageProcessor(format="coco_panoptic"			)
          snake_case__    =					image_processing(images=lowerCamelCase		,						annotations=lowerCamelCase		,						masks_path=lowerCamelCase		,						return_tensors="pt"			)

          # verify pixel values
          snake_case__    =					torch.Size([1, 3, 8_00, 10_66]			)
          self.assertEqual(encoding["pixel_values"].shape		,						lowerCamelCase			)

          snake_case__    =					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]		,						lowerCamelCase		,						atol=1e-4			)			)

          # verify area
          snake_case__    =					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"]		,						lowerCamelCase			)			)
          # verify boxes
          snake_case__    =					torch.Size([6, 4]			)
          self.assertEqual(encoding["labels"][0]["boxes"].shape		,						lowerCamelCase			)
          snake_case__    =					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]		,						lowerCamelCase		,						atol=1e-3			)			)
          # verify image_id
          snake_case__    =					torch.tensor([3_97_69]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,						lowerCamelCase			)			)
          # verify is_crowd
          snake_case__    =					torch.tensor([0, 0, 0, 0, 0, 0]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,						lowerCamelCase			)			)
          # verify class_labels
          snake_case__    =					torch.tensor([17, 17, 63, 75, 75, 93]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,						lowerCamelCase			)			)
          # verify masks
          snake_case__    =					82_28_73
          self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,						lowerCamelCase			)
          # verify orig_size
          snake_case__    =					torch.tensor([4_80, 6_40]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,						lowerCamelCase			)			)
          # verify size
          snake_case__    =					torch.tensor([8_00, 10_66]			)
          self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,						lowerCamelCase			)			)



