
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 MobileNetVaImageProcessor




class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase=7		,						lowerCamelCase=3		,						lowerCamelCase=18		,						lowerCamelCase=30		,						lowerCamelCase=4_00		,						lowerCamelCase=True		,						lowerCamelCase=None		,						lowerCamelCase=True		,						lowerCamelCase=None		,						):
          snake_case__    =					size if size is not None else {"shortest_edge": 20}
          snake_case__    =					crop_size if crop_size is not None else {"height": 18, "width": 18}
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					num_channels
          snake_case__    =					image_size
          snake_case__    =					min_resolution
          snake_case__    =					max_resolution
          snake_case__    =					do_resize
          snake_case__    =					size
          snake_case__    =					do_center_crop
          snake_case__    =					crop_size



      def A_      (      self			):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }





@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       unittest.TestCase			):
      _A    :		List[str]     						=  MobileNetVaImageProcessor if is_vision_available() else None
      def A_      (      self			):
          snake_case__    =					MobileNetVaImageProcessingTester(self			)
      @property
      def A_      (      self			):
          return self.image_processor_tester.prepare_image_processor_dict()
      def A_      (      self			):
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_resize"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"size"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"do_center_crop"			)			)
          self.assertTrue(hasattr(lowerCamelCase		,						"crop_size"			)			)
      def A_      (      self			):
          snake_case__    =					self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size		,						{"shortest_edge": 20}			)
          self.assertEqual(image_processor.crop_size		,						{"height": 18, "width": 18}			)

          snake_case__    =					self.image_processing_class.from_dict(self.image_processor_dict		,						size=42		,						crop_size=84			)
          self.assertEqual(image_processor.size		,						{"shortest_edge": 42}			)
          self.assertEqual(image_processor.crop_size		,						{"height": 84, "width": 84}			)
      def A_      (      self			):
          pass
      def A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          snake_case__    =					prepare_image_inputs(self.image_processor_tester		,						equal_resolution=lowerCamelCase			)
          for image in image_inputs:
              self.assertIsInstance(lowerCamelCase		,						Image.Image			)

          # Test not batched input
          snake_case__    =					image_processing(image_inputs[0]		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )		,						)

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )		,						)
      def A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          snake_case__    =					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
          snake_case__    =					image_processing(image_inputs[0]		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )		,						)

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )		,						)



      def A_      (      self			):
          # Initialize image_processing
          snake_case__    =					self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          snake_case__    =					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
          snake_case__    =					image_processing(image_inputs[0]		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )		,						)

          # Test batched
          snake_case__    =					image_processing(lowerCamelCase		,						return_tensors="pt"			).pixel_values
          self.assertEqual(
              encoded_images.shape		,						(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size["height"],
                  self.image_processor_tester.crop_size["width"],
              )		,						)





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__  							=	299_792_458

# Symbols
__magic_name__							,   __magic_name__							,   __magic_name__							,   __magic_name__  							=	symbols('''ct x y z''')





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if velocity > c:
        raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"				)
    elif velocity < 1:
        # Usually the speed should be much higher than 1 (c order of magnitude)
        raise ValueError("Speed must be greater than or equal to 1!"				)

    return velocity / c





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return 1 / sqrt(1 - beta(__lowerCAmelCase				) ** 2				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return np.array(
        [
            [gamma(__lowerCAmelCase				), -gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), 0, 0],
            [-gamma(__lowerCAmelCase				) * beta(__lowerCAmelCase				), gamma(__lowerCAmelCase				), 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ]				)





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase = None				):
    # Ensure event is not empty
    if event is None:
        snake_case__    =					np.array([ct, x, y, z]				)  # Symbolic four vector
    else:
        event[0] *= c  # x0 is ct (speed of light * time)

    return transformation_matrix(__lowerCAmelCase				) @ event


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Example of symbolic vector:
     __magic_name__  							=	transform(29_979_245)
     print('''Example of four vector: ''')
     print(F'''ct\' = {four_vector[0]}''')
     print(F'''x\' = {four_vector[1]}''')
     print(F'''y\' = {four_vector[2]}''')
     print(F'''z\' = {four_vector[3]}''')

     # Substitute symbols with numerical values
     __magic_name__  							=	{ct: c, x: 1, y: 1, z: 1}
     __magic_name__  							=	[four_vector[i].subs(sub_dict) for i in range(4)]

     print(F'''\n{numerical_vector}''')



