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 DetrImageProcessor




class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




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



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
    _UpperCamelCase:  List[Any]    						=			parent
    _UpperCamelCase:  Any    						=			batch_size
    _UpperCamelCase:  List[str]    						=			num_channels
    _UpperCamelCase:  int    						=			min_resolution
    _UpperCamelCase:  Any    						=			max_resolution
    _UpperCamelCase:  Optional[Any]    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			size
    _UpperCamelCase:  str    						=			do_rescale
    _UpperCamelCase:  List[Any]    						=			rescale_factor
    _UpperCamelCase:  List[Any]    						=			do_normalize
    _UpperCamelCase:  List[str]    						=			image_mean
    _UpperCamelCase:  str    						=			image_std
    _UpperCamelCase:  Optional[Any]    						=			do_pad



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_pad": self.do_pad,
    }



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	int		,	_lowercase       :	Dict=False				):



    """simple docstring"""



    if not batched:
     _UpperCamelCase:  str    						=			image_inputs[0]
     if isinstance(_lowercase		,	Image.Image				):
      _UpperCamelCase						,				_UpperCamelCase:  Tuple    						=			image.size
     else:
      _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			image.shape[1], image.shape[2]
     if w < h:
      _UpperCamelCase:  Dict    						=			int(self.size['''shortest_edge'''] * h / w				)
      _UpperCamelCase:  str    						=			self.size['''shortest_edge''']
     elif w > h:
      _UpperCamelCase:  Dict    						=			self.size['''shortest_edge''']
      _UpperCamelCase:  List[Any]    						=			int(self.size['''shortest_edge'''] * w / h				)
     else:
      _UpperCamelCase:  Tuple    						=			self.size['''shortest_edge''']
      _UpperCamelCase:  Dict    						=			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:  Union[str, Any]    						=			max(_lowercase		,	key=lambda _lowercase				: item[0]				)[0]
     _UpperCamelCase:  str    						=			max(_lowercase		,	key=lambda _lowercase				: item[1]				)[1]

    return expected_height, expected_width




@require_torch
@require_vision
class 			__magic_name__	(		__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     int	      = DetrImageProcessor if is_vision_available() else None



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			DetrImageProcessingTester(self				)



   @property
   def 	lowerCAmelCase    (						self       :	List[str]				):



    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(_lowercase		,	'''image_mean'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''image_std'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_normalize'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_rescale'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''rescale_factor'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_resize'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''size'''				)				)
    self.assertTrue(hasattr(_lowercase		,	'''do_pad'''				)				)



   def 	lowerCAmelCase    (						self       :	Dict				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			self.image_processing_class.from_dict(self.image_processor_dict				)
    self.assertEqual(image_processor.size		,	{'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
    self.assertEqual(image_processor.do_pad		,	_lowercase				)

    _UpperCamelCase:  Tuple    						=			self.image_processing_class.from_dict(
        self.image_processor_dict		,	size=42		,	max_size=84		,	pad_and_return_pixel_mask=_lowercase				)
    self.assertEqual(image_processor.size		,	{'''shortest_edge''': 42, '''longest_edge''': 84}				)
    self.assertEqual(image_processor.do_pad		,	_lowercase				)



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



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

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

    _UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			self.image_processor_tester.get_expected_values(_lowercase				)

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

    # Test batched
    _UpperCamelCase						,				_UpperCamelCase:  List[Any]    						=			self.image_processor_tester.get_expected_values(_lowercase		,	batched=_lowercase				)

    _UpperCamelCase:  Optional[Any]    						=			image_processing(_lowercase		,	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       :	int				):



    """simple docstring"""



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

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

    _UpperCamelCase						,				_UpperCamelCase:  Any    						=			self.image_processor_tester.get_expected_values(_lowercase				)

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

    # Test batched
    _UpperCamelCase:  Dict    						=			image_processing(_lowercase		,	return_tensors='''pt'''				).pixel_values

    _UpperCamelCase						,				_UpperCamelCase:  List[str]    						=			self.image_processor_tester.get_expected_values(_lowercase		,	batched=_lowercase				)

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



   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



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

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

    _UpperCamelCase						,				_UpperCamelCase:  Optional[int]    						=			self.image_processor_tester.get_expected_values(_lowercase				)

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

    # Test batched
    _UpperCamelCase:  int    						=			image_processing(_lowercase		,	return_tensors='''pt'''				).pixel_values

    _UpperCamelCase						,				_UpperCamelCase:  str    						=			self.image_processor_tester.get_expected_values(_lowercase		,	batched=_lowercase				)

    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       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			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:  List[str]    						=			{'''image_id''': 39_769, '''annotations''': target}

    # encode them
    _UpperCamelCase:  str    						=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''				)
    _UpperCamelCase:  Optional[int]    						=			image_processing(images=_lowercase		,	annotations=_lowercase		,	return_tensors='''pt'''				)

    # verify pixel values
    _UpperCamelCase:  Optional[Any]    						=			torch.Size([1, 3, 800, 1_066]				)
    self.assertEqual(encoding['''pixel_values'''].shape		,	_lowercase				)

    _UpperCamelCase:  str    						=			torch.tensor([0.2796, 0.3138, 0.3481]				)
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,	_lowercase		,	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''']		,	_lowercase				)				)
    # verify boxes
    _UpperCamelCase:  Optional[int]    						=			torch.Size([6, 4]				)
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,	_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,	_lowercase		,	atol=1E-3				)				)
    # verify image_id
    _UpperCamelCase:  Union[str, Any]    						=			torch.tensor([39_769]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,	_lowercase				)				)
    # verify is_crowd
    _UpperCamelCase:  int    						=			torch.tensor([0, 0, 0, 0, 0, 0]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,	_lowercase				)				)
    # verify class_labels
    _UpperCamelCase:  str    						=			torch.tensor([75, 75, 63, 65, 17, 17]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,	_lowercase				)				)
    # verify orig_size
    _UpperCamelCase:  Union[str, Any]    						=			torch.tensor([480, 640]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,	_lowercase				)				)
    # verify size
    _UpperCamelCase:  List[str]    						=			torch.tensor([800, 1_066]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,	_lowercase				)				)



   @slow
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """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:  Dict    						=			json.loads(f.read()				)

    _UpperCamelCase:  Any    						=			{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

    _UpperCamelCase:  Optional[int]    						=			pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

    # encode them
    _UpperCamelCase:  List[Any]    						=			DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''				)
    _UpperCamelCase:  int    						=			image_processing(images=_lowercase		,	annotations=_lowercase		,	masks_path=_lowercase		,	return_tensors='''pt'''				)

    # verify pixel values
    _UpperCamelCase:  int    						=			torch.Size([1, 3, 800, 1_066]				)
    self.assertEqual(encoding['''pixel_values'''].shape		,	_lowercase				)

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

    # verify area
    _UpperCamelCase:  Any    						=			torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,	_lowercase				)				)
    # verify boxes
    _UpperCamelCase:  int    						=			torch.Size([6, 4]				)
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,	_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,	_lowercase		,	atol=1E-3				)				)
    # verify image_id
    _UpperCamelCase:  Dict    						=			torch.tensor([39_769]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,	_lowercase				)				)
    # verify is_crowd
    _UpperCamelCase:  Optional[int]    						=			torch.tensor([0, 0, 0, 0, 0, 0]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,	_lowercase				)				)
    # verify class_labels
    _UpperCamelCase:  Union[str, Any]    						=			torch.tensor([17, 17, 63, 75, 75, 93]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,	_lowercase				)				)
    # verify masks
    _UpperCamelCase:  Any    						=			822_873
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,	_lowercase				)
    # verify orig_size
    _UpperCamelCase:  Optional[Any]    						=			torch.tensor([480, 640]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,	_lowercase				)				)
    # verify size
    _UpperCamelCase:  Tuple    						=			torch.tensor([800, 1_066]				)
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,	_lowercase				)				)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_           =				{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				[
      '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MegaForCausalLM''',
      '''MegaForMaskedLM''',
      '''MegaForMultipleChoice''',
      '''MegaForQuestionAnswering''',
      '''MegaForSequenceClassification''',
      '''MegaForTokenClassification''',
      '''MegaModel''',
      '''MegaPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)