






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   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[Any]					,  SCREAMING_SNAKE_CASE__  :     str=False					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]=False					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]=False	):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    UpperCamelCase	:Union[str, Any]     							=       []
    for i in range(image_processor_tester.batch_size	):
        if equal_resolution:
            UpperCamelCase	:Union[str, Any]     							=       image_processor_tester.max_resolution
        else:
            # To avoid getting image width/height 0
            UpperCamelCase	:Tuple     							=       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`
                UpperCamelCase	:Optional[int]     							=       max(image_processor_tester.size_divisor					,  _UpperCAmelCase	)
            UpperCamelCase	:Optional[Any]     							=       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
        UpperCamelCase	:Any     							=       [Image.fromarray(np.moveaxis(_UpperCAmelCase					,  0					,  -1	)	) for image in image_inputs]

    if torchify:
        UpperCamelCase	:List[Any]     							=       [torch.from_numpy(_UpperCAmelCase	) for image in image_inputs]

    return image_inputs


def   _A       (    SCREAMING_SNAKE_CASE__  :     Tuple					,  SCREAMING_SNAKE_CASE__  :     List[str]=10					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]=10					,  SCREAMING_SNAKE_CASE__  :     Optional[int]=False					,  SCREAMING_SNAKE_CASE__  :     Optional[Any]=False	):
    UpperCamelCase	: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
        UpperCamelCase	:Optional[Any]     							=       [Image.fromarray(np.moveaxis(_UpperCAmelCase					,  0					,  -1	)	) for frame in video]

    if torchify:
        UpperCamelCase	:int     							=       [torch.from_numpy(_UpperCAmelCase	) for frame in video]

    return video


def   _A       (    SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     Any=False					,  SCREAMING_SNAKE_CASE__  :     Union[str, Any]=False					,  SCREAMING_SNAKE_CASE__  :     Tuple=False	):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

    return video_inputs







class       UpperCAmelCase_						:



      """simple docstring"""
      UpperCamelCase_    : Any									=None
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Dict     							=       self.image_processing_class(**self.image_processor_dict			)
          UpperCamelCase	:List[Any]     							=       json.loads(image_processor.to_json_string()			)
          for key, value in self.image_processor_dict.items():
              self.assertEqual(obj[key]   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[Any]     							=       self.image_processing_class(**self.image_processor_dict			)

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

          self.assertEqual(image_processor_second.to_dict()   ,    image_processor_first.to_dict()			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          UpperCamelCase	:Union[str, Any]     							=       self.image_processing_class(**self.image_processor_dict			)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCamelCase	:List[str]     							=       image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_			)[0]
              check_json_file_has_correct_format(SCREAMING_SNAKE_CASE_			)
              UpperCamelCase	:str     							=       self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_			)

          self.assertEqual(image_processor_second.to_dict()   ,    image_processor_first.to_dict()			)
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Optional[int]     							=       self.image_processing_class()
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)




      @require_torch
      @require_vision
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          if self.test_cast_dtype is not None:
              # Initialize image_processor
              UpperCamelCase	:str     							=       self.image_processing_class(**self.image_processor_dict			)

              # create random PyTorch tensors
              UpperCamelCase	:Optional[int]     							=       prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=SCREAMING_SNAKE_CASE_   ,    torchify=SCREAMING_SNAKE_CASE_			)

              UpperCamelCase	:Optional[Any]     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              # for layoutLM compatiblity
              self.assertEqual(encoding.pixel_values.device   ,    torch.device('''cpu'''			)			)
              self.assertEqual(encoding.pixel_values.dtype   ,    torch.floataa			)

              UpperCamelCase	:Dict     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			).to(torch.floataa			)
              self.assertEqual(encoding.pixel_values.device   ,    torch.device('''cpu'''			)			)
              self.assertEqual(encoding.pixel_values.dtype   ,    torch.floataa			)

              UpperCamelCase	:int     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    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(SCREAMING_SNAKE_CASE_			):
                  UpperCamelCase	:Dict     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			).to(torch.bfloataa   ,    '''cpu'''			)

              # Try with text + image feature
              UpperCamelCase	:Union[str, Any]     							=       image_processor(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]			)}			)
              UpperCamelCase	:List[str]     							=       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			)













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    UpperCamelCase	:Tuple     							=       float()
    try:
        UpperCamelCase	:Union[str, Any]     							=       mass * acceleration
    except Exception:
        return -0.0
    return force


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo
 __snake_case							 =  1_2.5
 __snake_case							 =  10
 __snake_case							 =  newtons_second_law_of_motion(mass, acceleration)
 print("""The force is """, force, """N""")





