

'''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							(	lowercase__						:  int,					lowercase__						:  List[str]=False,					lowercase__						:  Tuple=False,					lowercase__						:  List[Any]=False ):






     '''simple docstring'''





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

     __lowercase					=[]
     for i in range(image_processor_tester.batch_size ):
          if equal_resolution:
               __lowercase					=__lowercase					=image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               __lowercase					=image_processor_tester.min_resolution
               if getattr(lowercase__,					'size_divisor',					lowercase__ ):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    __lowercase					=max(image_processor_tester.size_divisor,					lowercase__ )
               __lowercase		,     __lowercase					=np.random.choice(np.arange(lowercase__,					image_processor_tester.max_resolution ),					2 )
          image_inputs.append(
              np.random.randint(2_55,					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
          __lowercase					=[Image.fromarray(np.moveaxis(lowercase__,					0,					-1 ) ) for image in image_inputs]

     if torchify:
          __lowercase					=[torch.from_numpy(lowercase__ ) for image in image_inputs]

     return image_inputs

def 				__UpperCamelCase							(	lowercase__						:  Dict,					lowercase__						:  List[str]=10,					lowercase__						:  List[str]=10,					lowercase__						:  List[Any]=False,					lowercase__						:  List[str]=False ):






     '''simple docstring'''





     __lowercase					=[]
     for i in range(image_processor_tester.num_frames ):
          video.append(np.random.randint(2_55,					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
          __lowercase					=[Image.fromarray(np.moveaxis(lowercase__,					0,					-1 ) ) for frame in video]

     if torchify:
          __lowercase					=[torch.from_numpy(lowercase__ ) for frame in video]

     return video

def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  List[Any]=False,					lowercase__						:  Dict=False,					lowercase__						:  Any=False ):






     '''simple docstring'''





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

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

     return video_inputs







class   lowerCAmelCase     :
       lowerCAmelCase_		= None







       def 			snake_case   (							self			: int      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Any      ):







            """simple docstring"""







            __lowercase					=self.image_processing_class(**self.image_processor_dict      )

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

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







       def 			snake_case   (							self			: List[Any]      ):







            """simple docstring"""







            __lowercase					=self.image_processing_class(**self.image_processor_dict      )

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

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







       def 			snake_case   (							self			: Optional[int]      ):







            """simple docstring"""







            __lowercase					=self.image_processing_class()
            self.assertIsNotNone(__lowercase      )







       @require_torch
       @require_vision
       def 			snake_case   (							self			: Optional[Any]      ):







            """simple docstring"""







            if self.test_cast_dtype is not None:
                 # Initialize image_processor
                 __lowercase					=self.image_processing_class(**self.image_processor_dict      )

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

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

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

                 __lowercase					=image_processor(__lowercase						,      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(__lowercase      ):
                      __lowercase					=image_processor(__lowercase						,      return_tensors='pt'      ).to(torch.bfloataa						,      'cpu'      )

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

# Define the Gravitational Constant G and the function
UpperCAmelCase				   =  6.6_7_4_3E-1_1  # unit of G : m^3 * kg^-1 * s^-2

def 				__UpperCamelCase							(	lowercase__						:  float,					lowercase__						:  float,					lowercase__						:  float,					lowercase__						:  float ):






     '''simple docstring'''





     __lowercase					=mass_a * mass_a

     if (force, mass_a, mass_a, distance).count(0 ) != 1:
          raise ValueError('One and only one argument must be 0' )
     if force < 0:
          raise ValueError('Gravitational force can not be negative' )
     if distance < 0:
          raise ValueError('Distance can not be negative' )
     if mass_a < 0 or mass_a < 0:
          raise ValueError('Mass can not be negative' )
     if force == 0:
          __lowercase					=GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
          return {"force": force}
     elif mass_a == 0:
          __lowercase					=(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
          return {"mass_1": mass_a}
     elif mass_a == 0:
          __lowercase					=(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
          return {"mass_2": mass_a}
     elif distance == 0:
          __lowercase					=(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
          return {"distance": distance}
     raise ValueError('One and only one argument must be 0' )


# Run doctest
if __name__ == "__main__":
       import doctest

       doctest.testmod()


