
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
  import numpy as np
  import torch

if is_vision_available():
  from PIL import Image


def 		_lowerCamelCase       (   _a			,   _a=False			,   _a=False			,   _a=False  ):
  """simple docstring"""




  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _lowerCamelCase      =						[]
  for i in range(image_processor_tester.batch_size  ):
    if equal_resolution:
      _lowerCamelCase      =						image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      _lowerCamelCase      =						image_processor_tester.min_resolution
      if getattr(a_			,   '''size_divisor'''			,   a_  ):
        # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
        _lowerCamelCase      =						max(image_processor_tester.size_divisor			,   a_  )
      _lowerCamelCase      =						np.random.choice(np.arange(a_			,   image_processor_tester.max_resolution  )			,   2  )
    image_inputs.append(
        np.random.randint(2_5_5			,   size=(image_processor_tester.num_channels, width, height)			,   dtype=np.uinta  )  )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    _lowerCamelCase      =						[Image.fromarray(np.moveaxis(a_			,   0			,   -1  )  ) for image in image_inputs]

  if torchify:
    _lowerCamelCase      =						[torch.from_numpy(a_  ) for image in image_inputs]

  return image_inputs


def 		_lowerCamelCase       (   _a			,   _a=1_0			,   _a=1_0			,   _a=False			,   _a=False  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  for i in range(image_processor_tester.num_frames  ):
    video.append(np.random.randint(2_5_5			,   size=(image_processor_tester.num_channels, width, height)			,   dtype=np.uinta  )  )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    _lowerCamelCase      =						[Image.fromarray(np.moveaxis(a_			,   0			,   -1  )  ) for frame in video]

  if torchify:
    _lowerCamelCase      =						[torch.from_numpy(a_  ) for frame in video]

  return video


def 		_lowerCamelCase       (   _a			,   _a=False			,   _a=False			,   _a=False  ):
  """simple docstring"""




  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  _lowerCamelCase      =						[]
  for i in range(image_processor_tester.batch_size  ):
    if equal_resolution:
      _lowerCamelCase      =						image_processor_tester.max_resolution
    else:
      _lowerCamelCase      =						np.random.choice(
          np.arange(image_processor_tester.min_resolution			,   image_processor_tester.max_resolution  )			,   2  )
      _lowerCamelCase      =						prepare_video(
          image_processor_tester=a_			,   width=a_			,   height=a_			,   numpify=a_			,   torchify=a_			,   )
    video_inputs.append(a_  )

  return video_inputs






class 		__magic_name__				:


  """simple docstring"""





  _UpperCamelCase						 = None


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)
    _lowerCamelCase      =						json.loads(image_processor.to_json_string()		)
    for key, value in self.image_processor_dict.items():
      self.assertEqual(obj[key]	,		_lowerCamelCase		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _lowerCamelCase      =						os.path.join(_lowerCamelCase	,		'''image_processor.json'''		)
      image_processor_first.to_json_file(_lowerCamelCase		)
      _lowerCamelCase      =						self.image_processing_class.from_json_file(_lowerCamelCase		)

    self.assertEqual(image_processor_second.to_dict()	,		image_processor_first.to_dict()		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _lowerCamelCase      =						image_processor_first.save_pretrained(_lowerCamelCase		)[0]
      check_json_file_has_correct_format(_lowerCamelCase		)
      _lowerCamelCase      =						self.image_processing_class.from_pretrained(_lowerCamelCase		)

    self.assertEqual(image_processor_second.to_dict()	,		image_processor_first.to_dict()		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class()
    self.assertIsNotNone(_lowerCamelCase		)


  @require_torch
  @require_vision
  def     _UpperCAmelCase  (					self		):
    if self.test_cast_dtype is not None:
      # Initialize image_processor
      _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)

      # create random PyTorch tensors
      _lowerCamelCase      =						prepare_image_inputs(self.image_processor_tester	,		equal_resolution=_lowerCamelCase	,		torchify=_lowerCamelCase		)

      _lowerCamelCase      =						image_processor(_lowerCamelCase	,		return_tensors='''pt'''		)
      # for layoutLM compatiblity
      self.assertEqual(encoding.pixel_values.device	,		torch.device('''cpu'''		)		)
      self.assertEqual(encoding.pixel_values.dtype	,		torch.floataa		)

      _lowerCamelCase      =						image_processor(_lowerCamelCase	,		return_tensors='''pt'''		).to(torch.floataa		)
      self.assertEqual(encoding.pixel_values.device	,		torch.device('''cpu'''		)		)
      self.assertEqual(encoding.pixel_values.dtype	,		torch.floataa		)

      _lowerCamelCase      =						image_processor(_lowerCamelCase	,		return_tensors='''pt'''		).to('''cpu'''	,		torch.bfloataa		)
      self.assertEqual(encoding.pixel_values.device	,		torch.device('''cpu'''		)		)
      self.assertEqual(encoding.pixel_values.dtype	,		torch.bfloataa		)

      with self.assertRaises(_lowerCamelCase		):
        _lowerCamelCase      =						image_processor(_lowerCamelCase	,		return_tensors='''pt'''		).to(torch.bfloataa	,		'''cpu'''		)

      # Try with text + image feature
      _lowerCamelCase      =						image_processor(_lowerCamelCase	,		return_tensors='''pt'''		)
      encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]		)}		)
      _lowerCamelCase      =						encoding.to(torch.floataa		)

      self.assertEqual(encoding.pixel_values.device	,		torch.device('''cpu'''		)		)
      self.assertEqual(encoding.pixel_values.dtype	,		torch.floataa		)
      self.assertEqual(encoding.input_ids.dtype	,		torch.long		)









'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 ViltImageProcessor


class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=7					,    _lowerCamelCase=3					,    _lowerCamelCase=18					,    _lowerCamelCase=30					,    _lowerCamelCase=400					,    _lowerCamelCase=True					,    _lowerCamelCase=None					,    _lowerCamelCase=2					,    _lowerCamelCase=True					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    _lowerCamelCase=[0.5, 0.5, 0.5]					,    )    ->    Tuple:
    A_  :		Dict						=					size if size is not None else {"""shortest_edge""": 30}
    A_  :		List[Any]						=					parent
    A_  :		Optional[Any]						=					batch_size
    A_  :		Tuple						=					num_channels
    A_  :		Any						=					image_size
    A_  :		str						=					min_resolution
    A_  :		str						=					max_resolution
    A_  :		Any						=					do_resize
    A_  :		List[Any]						=					size
    A_  :		Union[str, Any]						=					size_divisor
    A_  :		Dict						=					do_normalize
    A_  :		Dict						=					image_mean
    A_  :		Optional[Any]						=					image_std







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "size": self.size,
        "size_divisor": self.size_divisor,
    }







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=False  )    ->    Union[str, Any]:
    if not batched:
       A_  :		List[str]						=					self.size["""shortest_edge"""]
       A_  :		str						=					image_inputs[0]
       if isinstance(_lowerCamelCase					,    Image.Image  ):
          A_						,  A_  :		Dict						=					image.size
       else:
          A_						,  A_  :		Dict						=					image.shape[1], image.shape[2]
       A_  :		Dict						=					size / min(_lowerCamelCase					,    _lowerCamelCase  )
       if h < w:
          A_						,  A_  :		Any						=					size, scale * w
       else:
          A_						,  A_  :		List[str]						=					scale * h, size

       A_  :		Dict						=					int((1333 / 800) * size  )
       if max(_lowerCamelCase					,    _lowerCamelCase  ) > max_size:
          A_  :		Dict						=					max_size / max(_lowerCamelCase					,    _lowerCamelCase  )
          A_  :		str						=					newh * scale
          A_  :		Tuple						=					neww * scale

       A_						,  A_  :		str						=					int(newh + 0.5  ), int(neww + 0.5  )
       A_						,  A_  :		Union[str, Any]						=					(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

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

    return expected_height, expected_width


@require_torch
@require_vision
class 							_lowerCAmelCase	(  __A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     ViltImageProcessor if is_vision_available() else None







 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Optional[int]						=					ViltImageProcessingTester(self  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    return self.image_processor_tester.prepare_image_processor_dict()







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		List[str]						=					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					,    """size"""  )  )
    self.assertTrue(hasattr(_lowerCamelCase					,    """size_divisor"""  )  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 30}  )

    A_  :		Union[str, Any]						=					self.image_processing_class.from_dict(self.image_processor_dict					,    size=42  )
    self.assertEqual(image_processor.size					,    {"""shortest_edge""": 42}  )







 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    # Initialize image_processing
    A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    A_  :		Any						=					prepare_image_inputs(self.image_processor_tester					,    equal_resolution=_lowerCamelCase  )
    for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase					,    Image.Image  )

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

    A_						,  A_  :		Union[str, Any]						=					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
    A_  :		List[Any]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Union[str, Any]						=					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       UpperCAmelCase_       (      self  )    ->    str:
    # Initialize image_processing
    A_  :		Tuple						=					self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    A_  :		List[str]						=					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
    A_  :		Any						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		int						=					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
    A_  :		List[str]						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					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       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    # Initialize image_processing
    A_  :		List[str]						=					self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    A_  :		str						=					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
    A_  :		Optional[int]						=					image_processing(image_inputs[0]					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		List[str]						=					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
    A_  :		Any						=					image_processing(_lowerCamelCase					,    return_tensors="""pt"""  ).pixel_values

    A_						,  A_  :		Any						=					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,
        )					,    )
