




'''simple docstring'''





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

    _a							:       Union[str, Any]       					=			[]
    for i in range(image_processor_tester.batch_size	):
        if equal_resolution:
            _a							:       str       					=			image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            _a							:       List[Any]       					=			image_processor_tester.min_resolution
            if getattr(UpperCAmelCase_      ,  """size_divisor"""      ,  UpperCAmelCase_	):
                # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                _a							:       int       					=			max(image_processor_tester.size_divisor      ,  UpperCAmelCase_	)
            _a					,   _a							:       Tuple       					=			np.random.choice(np.arange(UpperCAmelCase_      ,  image_processor_tester.max_resolution	)      ,  2	)
        image_inputs.append(
            np.random.randint(255      ,  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
        _a							:       Tuple       					=			[Image.fromarray(np.moveaxis(UpperCAmelCase_      ,  0      ,  -1	)	) for image in image_inputs]

    if torchify:
        _a							:       int       					=			[torch.from_numpy(UpperCAmelCase_	) for image in image_inputs]

    return image_inputs





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=10      ,  UpperCAmelCase_=10      ,  UpperCAmelCase_=False      ,  UpperCAmelCase_=False	):
    _a							:       Union[str, Any]       					=			[]
    for i in range(image_processor_tester.num_frames	):
        video.append(np.random.randint(255      ,  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
        _a							:       Dict       					=			[Image.fromarray(np.moveaxis(UpperCAmelCase_      ,  0      ,  -1	)	) for frame in video]

    if torchify:
        _a							:       Optional[Any]       					=			[torch.from_numpy(UpperCAmelCase_	) for frame in video]

    return video





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_=False      ,  UpperCAmelCase_=False      ,  UpperCAmelCase_=False	):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

    return video_inputs





class     UpperCamelCase					:
  a    :Union[str, Any]      					=				None



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       int       					=			self.image_processing_class(**self.image_processor_dict   )
      _a							:       List[Any]       					=			json.loads(image_processor.to_json_string()   )
      for key, value in self.image_processor_dict.items():
          self.assertEqual(obj[key]		,    __lowerCAmelCase   )



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			self.image_processing_class(**self.image_processor_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
          _a							:       Dict       					=			os.path.join(__lowerCAmelCase		,    """image_processor.json"""   )
          image_processor_first.to_json_file(__lowerCAmelCase   )
          _a							:       Any       					=			self.image_processing_class.from_json_file(__lowerCAmelCase   )

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



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       Dict       					=			self.image_processing_class(**self.image_processor_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
          _a							:       Tuple       					=			image_processor_first.save_pretrained(__lowerCAmelCase   )[0]
          check_json_file_has_correct_format(__lowerCAmelCase   )
          _a							:       str       					=			self.image_processing_class.from_pretrained(__lowerCAmelCase   )

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



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[str]       					=			self.image_processing_class()
      self.assertIsNotNone(__lowerCAmelCase   )



  @require_torch
  @require_vision
  def 						a_       ( self   ):







      '''simple docstring'''


      if self.test_cast_dtype is not None:
          # Initialize image_processor
          _a							:       Optional[int]       					=			self.image_processing_class(**self.image_processor_dict   )

          # create random PyTorch tensors
          _a							:       Optional[Any]       					=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=__lowerCAmelCase		,    torchify=__lowerCAmelCase   )

          _a							:       Optional[int]       					=			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   )

          _a							:       Dict       					=			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   )

          _a							:       Any       					=			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   ):
              _a							:       Dict       					=			image_processor(__lowerCAmelCase		,    return_tensors="""pt"""   ).to(torch.bfloataa		,    """cpu"""   )

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





from __future__ import annotations

from enum import Enum




class     UpperCamelCase					(  snake_case__					):
  a    :Optional[int]      					=				2_4
  a    :Dict      					=				2_1
  a    :Dict      					=				1_8
  a    :int      					=				1_5
  a    :List[str]      					=				1_2
  a    :Tuple      					=				9
  a    :Optional[int]      					=				6
  a    :str      					=				3
  a    :Dict      					=				2
  a    :int      					=				1
  a    :List[Any]      					=				-1
  a    :Optional[Any]      					=				-2
  a    :Union[str, Any]      					=				-3
  a    :int      					=				-6
  a    :Union[str, Any]      					=				-9
  a    :Dict      					=				-1_2
  a    :Any      					=				-1_5
  a    :Tuple      					=				-1_8
  a    :Optional[Any]      					=				-2_1
  a    :Union[str, Any]      					=				-2_4






class     UpperCamelCase					(  snake_case__					):
  a    :Optional[Any]      					=				8
  a    :Optional[int]      					=				7
  a    :Optional[int]      					=				6
  a    :List[str]      					=				5
  a    :Union[str, Any]      					=				4
  a    :Union[str, Any]      					=				3
  a    :str      					=				2
  a    :Optional[int]      					=				1






def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       int       					=			SIUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			SIUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount





def UpperCAmelCase						(					UpperCAmelCase_      ,  UpperCAmelCase_      ,  UpperCAmelCase_      ,  ):
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Optional[Any]       					=			BinaryUnit[known_prefix.lower()]
    if isinstance(UpperCAmelCase_      ,  UpperCAmelCase_	):
        _a							:       Dict       					=			BinaryUnit[unknown_prefix.lower()]
    _a							:       float       					=			known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount


if __name__ == "__main__":
  import doctest

  doctest.testmod()


