




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 DeiTImageProcessor

class      UpperCAmelCase						(			unittest.TestCase   ):






   def __init__(	self   :       str						,					UpperCamelCase_   :       Dict						,					UpperCamelCase_   :       Tuple=7						,					UpperCamelCase_   :       List[str]=3						,					UpperCamelCase_   :       int=18						,					UpperCamelCase_   :       Tuple=30						,					UpperCamelCase_   :       Optional[int]=4_00						,					UpperCamelCase_   :       Dict=True						,					UpperCamelCase_   :       int=None						,					UpperCamelCase_   :       Optional[int]=True						,					UpperCamelCase_   :       Dict=None						,					UpperCamelCase_   :       int=True						,					UpperCamelCase_   :       str=[0.5, 0.5, 0.5]						,					UpperCamelCase_   :       str=[0.5, 0.5, 0.5]						,					)->			Union[str, Any]:
     _lowercase: Any						     =    size if size is not None else {'height': 20, 'width': 20}
     _lowercase: int						     =    crop_size if crop_size is not None else {'height': 18, 'width': 18}

     _lowercase: Dict						     =    parent
     _lowercase: List[Any]						     =    batch_size
     _lowercase: int						     =    num_channels
     _lowercase: Any						     =    image_size
     _lowercase: List[str]						     =    min_resolution
     _lowercase: Tuple						     =    max_resolution
     _lowercase: Optional[int]						     =    do_resize
     _lowercase: Optional[int]						     =    size
     _lowercase: int						     =    do_center_crop
     _lowercase: Optional[int]						     =    crop_size
     _lowercase: Tuple						     =    do_normalize
     _lowercase: Optional[Any]						     =    image_mean
     _lowercase: int						     =    image_std






   def  _a						(	self   :       int     )->			List[str]:
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }

@require_torch
@require_vision
class      UpperCAmelCase						(			A   ,					unittest.TestCase   ):
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					DeiTImageProcessor if is_vision_available() else None
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					True






   def  _a						(	self   :       Union[str, Any]     )->			Optional[int]:
     _lowercase: Any						     =    DeiTImageProcessingTester(self     )






   @property
   def  _a						(	self   :       Union[str, Any]     )->			Tuple:
     return self.image_processor_tester.prepare_image_processor_dict()






   def  _a						(	self   :       Optional[Any]     )->			Optional[int]:
     _lowercase: List[str]						     =    self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'do_resize'     )     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'size'     )     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'do_center_crop'     )     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'center_crop'     )     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'do_normalize'     )     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'image_mean'     )     )
     self.assertTrue(hasattr(UpperCamelCase_						,					'image_std'     )     )






   def  _a						(	self   :       Any     )->			Tuple:
     _lowercase: Optional[int]						     =    self.image_processing_class.from_dict(self.image_processor_dict     )
     self.assertEqual(image_processor.size						,					{'height': 20, 'width': 20}     )
     self.assertEqual(image_processor.crop_size						,					{'height': 18, 'width': 18}     )

     _lowercase: int						     =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84     )
     self.assertEqual(image_processor.size						,					{'height': 42, 'width': 42}     )
     self.assertEqual(image_processor.crop_size						,					{'height': 84, 'width': 84}     )






   def  _a						(	self   :       str     )->			Optional[int]:
     pass






   def  _a						(	self   :       Optional[Any]     )->			Dict:
     # Initialize image_processing
     _lowercase: Dict						     =    self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     _lowercase: str						     =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=UpperCamelCase_     )
     for image in image_inputs:
       self.assertIsInstance(UpperCamelCase_						,					Image.Image     )

     # Test not batched input
     _lowercase: int						     =    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
     _lowercase: Tuple						     =    image_processing(UpperCamelCase_						,					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   :       str     )->			Union[str, Any]:
     # Initialize image_processing
     _lowercase: List[Any]						     =    self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     _lowercase: int						     =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=UpperCamelCase_						,					numpify=UpperCamelCase_     )
     for image in image_inputs:
       self.assertIsInstance(UpperCamelCase_						,					np.ndarray     )

     # Test not batched input
     _lowercase: int						     =    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
     _lowercase: Optional[int]						     =    image_processing(UpperCamelCase_						,					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   :       Union[str, Any]     )->			List[Any]:
     # Initialize image_processing
     _lowercase: List[str]						     =    self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     _lowercase: Union[str, Any]						     =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=UpperCamelCase_						,					torchify=UpperCamelCase_     )
     for image in image_inputs:
       self.assertIsInstance(UpperCamelCase_						,					torch.Tensor     )

     # Test not batched input
     _lowercase: List[str]						     =    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
     _lowercase: Dict						     =    image_processing(UpperCamelCase_						,					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 __future__ import annotations

from pathlib import Path


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		bool:





  """simple docstring"""





  _lowercase: tuple[int, int]						     =    (xa, ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: float						     =    -vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)
  _lowercase: float						     =    +vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)

  return a > 0 and b > 0 and a + b < 1


def 		_lowerCamelCase				(					UpperCamelCase = "p102_triangles.txt"							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    Path(UpperCamelCase							).parent.joinpath(UpperCamelCase							).read_text(encoding='utf-8'							)

  _lowercase: list[list[int]]						     =    []
  for line in data.strip().split('\n'							):
    triangles.append([int(UpperCamelCase							) for number in line.split(','							)]							)

  _lowercase: int						     =    0
  _lowercase: list[int]

  for triangle in triangles:
    ret += contains_origin(*UpperCamelCase							)

  return ret


if __name__ == "__main__":
   print(f'''{solution() = }''')
