
'''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 DetaImageProcessor






class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		def __init__(      self				:			Optional[int]  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			Union[str, Any]=7  ,	UpperCamelCase__				:			Dict=3  ,	UpperCamelCase__				:			int=30  ,	UpperCamelCase__				:			Dict=400  ,	UpperCamelCase__				:			List[Any]=True  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			Dict=True  ,	UpperCamelCase__				:			str=[0.5, 0.5, 0.5]  ,	UpperCamelCase__				:			List[Any]=[0.5, 0.5, 0.5]  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			Optional[int]=1 / 255  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	)						->			Union[str, Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			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       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			Union[str, Any]=False      )						->			Union[str, Any]:






				"""simple docstring"""







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

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

				return expected_height, expected_width







@require_torch
@require_vision
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						DetaImageProcessor if is_vision_available() else None



		def       lowerCAmelCase				(      self				:			Any      )						->			Dict:






				"""simple docstring"""







				snake_case  :       int     			=						DetaImageProcessingTester(self      )



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       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  ,	'''do_rescale'''      )      )
				self.assertTrue(hasattr(__UpperCAmelCase  ,	'''do_pad'''      )      )
				self.assertTrue(hasattr(__UpperCAmelCase  ,	'''size'''      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Dict     			=						self.image_processing_class.from_dict(self.image_processor_dict      )
				self.assertEqual(image_processor.size  ,	{'''shortest_edge''': 18, '''longest_edge''': 1333}      )
				self.assertEqual(image_processor.do_pad  ,	__UpperCAmelCase      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			str:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.image_processing_class(**self.image_processor_dict      )
				# create random PIL images
				snake_case  :       Any     			=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__UpperCAmelCase      )
				for image in image_inputs:
						self.assertIsInstance(__UpperCAmelCase  ,	Image.Image      )

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

				snake_case  :       Optional[int]     			=						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
				snake_case  :       Tuple     			=						self.image_processor_tester.get_expected_values(__UpperCAmelCase  ,	batched=__UpperCAmelCase      )

				snake_case  :       Optional[Any]     			=						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				:			Optional[Any]      )						->			Dict:






				"""simple docstring"""







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

				snake_case  :       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
				snake_case  :       Tuple     			=						image_processing(__UpperCAmelCase  ,	return_tensors='''pt'''      ).pixel_values

				snake_case  :       List[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				:			Any      )						->			Optional[Any]:






				"""simple docstring"""







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

				snake_case  :       Optional[int]     			=						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
				snake_case  :       str     			=						image_processing(__UpperCAmelCase  ,	return_tensors='''pt'''      ).pixel_values

				snake_case  :       List[Any]     			=						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				:			Any      )						->			Union[str, Any]:






				"""simple docstring"""







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

				snake_case  :       int     			=						{'image_id': 3_9769, 'annotations': target}

				# encode them
				snake_case  :       int     			=						DetaImageProcessor()
				snake_case  :       List[Any]     			=						image_processing(images=__UpperCAmelCase  ,	annotations=__UpperCAmelCase  ,	return_tensors='''pt'''      )

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

				snake_case  :       Optional[int]     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,	__UpperCAmelCase  ,	atol=1e-4      )      )

				# verify area
				snake_case  :       Dict     			=						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''']  ,	__UpperCAmelCase      )      )
				# verify boxes
				snake_case  :       Tuple     			=						torch.Size([6, 4]      )
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,	__UpperCAmelCase      )
				snake_case  :       Dict     			=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,	__UpperCAmelCase  ,	atol=1e-3      )      )
				# verify image_id
				snake_case  :       Any     			=						torch.tensor([3_9769]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,	__UpperCAmelCase      )      )
				# verify is_crowd
				snake_case  :       Union[str, Any]     			=						torch.tensor([0, 0, 0, 0, 0, 0]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,	__UpperCAmelCase      )      )
				# verify class_labels
				snake_case  :       Optional[Any]     			=						torch.tensor([75, 75, 63, 65, 17, 17]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,	__UpperCAmelCase      )      )
				# verify orig_size
				snake_case  :       str     			=						torch.tensor([480, 640]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,	__UpperCAmelCase      )      )
				# verify size
				snake_case  :       Any     			=						torch.tensor([800, 1066]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,	__UpperCAmelCase      )      )



		@slow
		def       lowerCAmelCase				(      self				:			Dict      )						->			int:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						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  :       Dict     			=						json.loads(f.read()      )

				snake_case  :       Dict     			=						{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

				snake_case  :       Union[str, Any]     			=						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

				# encode them
				snake_case  :       Dict     			=						DetaImageProcessor(format='''coco_panoptic'''      )
				snake_case  :       Optional[int]     			=						image_processing(images=__UpperCAmelCase  ,	annotations=__UpperCAmelCase  ,	masks_path=__UpperCAmelCase  ,	return_tensors='''pt'''      )

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

				snake_case  :       Union[str, Any]     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
				self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,	__UpperCAmelCase  ,	atol=1e-4      )      )

				# verify area
				snake_case  :       Tuple     			=						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''']  ,	__UpperCAmelCase      )      )
				# verify boxes
				snake_case  :       int     			=						torch.Size([6, 4]      )
				self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,	__UpperCAmelCase      )
				snake_case  :       Optional[Any]     			=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,	__UpperCAmelCase  ,	atol=1e-3      )      )
				# verify image_id
				snake_case  :       Optional[int]     			=						torch.tensor([3_9769]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,	__UpperCAmelCase      )      )
				# verify is_crowd
				snake_case  :       Tuple     			=						torch.tensor([0, 0, 0, 0, 0, 0]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,	__UpperCAmelCase      )      )
				# verify class_labels
				snake_case  :       List[str]     			=						torch.tensor([17, 17, 63, 75, 75, 93]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,	__UpperCAmelCase      )      )
				# verify masks
				snake_case  :       Optional[int]     			=						82_2873
				self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,	__UpperCAmelCase      )
				# verify orig_size
				snake_case  :       Optional[int]     			=						torch.tensor([480, 640]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,	__UpperCAmelCase      )      )
				# verify size
				snake_case  :       Union[str, Any]     			=						torch.tensor([800, 1066]      )
				self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,	__UpperCAmelCase      )      )









"""simple docstring"""





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


__A											=						{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  if got_ver is None or want_ver is None:
    raise ValueError(
        F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
        F" reinstalling {pkg}."     )
  if not ops[op](version.parse(_SCREAMING_SNAKE_CASE     )	,  version.parse(_SCREAMING_SNAKE_CASE     )     ):
    raise ImportError(
        F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE = None     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :List[str]											=				F"\n{hint}" if hint is not None else ''

  # non-versioned check
  if re.match(r'^[\w_\-\d]+$'	,  _SCREAMING_SNAKE_CASE     ):
    lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :Any											=				requirement, None, None
  else:
    lowerCAmelCase__     :List[str]											=				re.findall(r'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'	,  _SCREAMING_SNAKE_CASE     )
    if not match:
      raise ValueError(
          'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
          F" got {requirement}"     )
    lowerCAmelCase__				,							lowerCAmelCase__     :Union[str, Any]											=				match[0]
    lowerCAmelCase__     :List[Any]											=				want_full.split(','     )  # there could be multiple requirements
    lowerCAmelCase__     :Any											=				{}
    for w in want_range:
      lowerCAmelCase__     :Tuple											=				re.findall(r'^([\s!=<>]{1,2})(.+)'	,  _SCREAMING_SNAKE_CASE     )
      if not match:
        raise ValueError(
            'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
            F" but got {requirement}"     )
      lowerCAmelCase__				,							lowerCAmelCase__     :int											=				match[0]
      lowerCAmelCase__     :str											=				want_ver
      if op not in ops:
        raise ValueError(F"{requirement}: need one of {list(ops.keys()     )}, but got {op}"     )

    # special case
  if pkg == "python":
    lowerCAmelCase__     :Any											=				'.'.join([str(_SCREAMING_SNAKE_CASE     ) for x in sys.version_info[:3]]     )
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    return

  # check if any version is installed
  try:
    lowerCAmelCase__     :List[Any]											=				importlib.metadata.version(_SCREAMING_SNAKE_CASE     )
  except importlib.metadata.PackageNotFoundError:
    raise importlib.metadata.PackageNotFoundError(
        F"The '{requirement}' distribution was not found and is required by this application. {hint}"     )

  # check that the right version is installed if version number or a range was provided
  if want_ver is not None:
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )




def 					__A  (_SCREAMING_SNAKE_CASE     )   ->List[Any]:





  """simple docstring"""


  lowerCAmelCase__     :Optional[Any]											=				'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
  return require_version(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
