

from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize







class   __lowercase				(_UpperCAmelCase			):
 _UpperCamelCase        =  1
 _UpperCamelCase        =  2
 _UpperCamelCase        =  3
 def __repr__(							self ) ->Tuple:



       '''simple docstring'''





       return f"""{self.__class__.__name__}.{self.name}"""







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Union[str, Any] = point
      return degrees(atana(y - miny       ,     x - minx						)						)







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Dict = starting
      __lowerCAmelCase :						Union[str, Any] = via
      __lowerCAmelCase :						Any = target
      __lowerCAmelCase :						Tuple = degrees(atana(ya - ya       ,     xa - xa						)						)
      via_angle %= 3_6_0
      __lowerCAmelCase :						Tuple = degrees(atana(ya - ya       ,     xa - xa						)						)
      target_angle %= 3_6_0
      # t-
      #  \ \
      #   \ v
      #    \|
      #     s
      # via_angle is always lower than target_angle, if direction is left.
      # If they are same, it means they are on a same line of convex hull.
      if target_angle > via_angle:
            return Direction.left
      elif target_angle == via_angle:
            return Direction.straight
      else:
            return Direction.right







def 		_lowercase			(					lowercase__						):

      if len(lowercase__						) <= 2:
            # There is no convex hull
            raise ValueError('''graham_scan: argument must contain more than 3 points.'''						)
      if len(lowercase__						) == 3:
            return points
      # find the lowest and the most left point
      __lowerCAmelCase :						Optional[Any] = 0
      __lowerCAmelCase :						int = maxsize, maxsize
      for i, point in enumerate(lowercase__						):
            __lowerCAmelCase :						str = point[0]
            __lowerCAmelCase :						Optional[int] = point[1]
            if y < miny:
                  __lowerCAmelCase :						Tuple = y
                  __lowerCAmelCase :						Any = x
                  __lowerCAmelCase :						Optional[Any] = i
            if y == miny and x < minx:
                  __lowerCAmelCase :						int = x
                  __lowerCAmelCase :						Tuple = i

    # remove the lowest and the most left point from points for preparing for sort
      points.pop(lowercase__						)

      __lowerCAmelCase :						Tuple = sorted(lowercase__       ,     key=lambda lowercase__						: angle_comparer(lowercase__       ,     lowercase__       ,     lowercase__						)						)
      # This insert actually costs complexity,
      # and you should instead add (minx, miny) into stack later.
      # I'm using insert just for easy understanding.
      sorted_points.insert(0       ,     (minx, miny)						)

      __lowerCAmelCase :						deque[tuple[int, int]] = deque()
      stack.append(sorted_points[0]						)
      stack.append(sorted_points[1]						)
      stack.append(sorted_points[2]						)
      # In any ways, the first 3 points line are towards left.
      # Because we sort them the angle from minx, miny.
      __lowerCAmelCase :						Optional[Any] = Direction.left

      for i in range(3       ,     len(lowercase__						)						):
            while True:
                  __lowerCAmelCase :						Optional[int] = stack[-2]
                  __lowerCAmelCase :						Optional[int] = stack[-1]
                  __lowerCAmelCase :						Tuple = sorted_points[i]
                  __lowerCAmelCase :						Dict = check_direction(lowercase__       ,     lowercase__       ,     lowercase__						)

                  if next_direction == Direction.left:
                        __lowerCAmelCase :						List[str] = Direction.left
                        break
                  if next_direction == Direction.straight:
                        if current_direction == Direction.left:
                              # We keep current_direction as left.
                              # Because if the straight line keeps as straight,
                              # we want to know if this straight line is towards left.
                              break
                        elif current_direction == Direction.right:
                              # If the straight line is towards right,
                              # every previous points on those straigh line is not convex hull.
                              stack.pop()
                  if next_direction == Direction.right:
                        stack.pop()
            stack.append(sorted_points[i]						)
      return list(lowercase__						)





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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BlipImageProcessor







class   __lowercase				(unittest.TestCase			):
 def __init__(							self				,							A_				,							A_=7				,							A_=3				,							A_=18				,							A_=30				,							A_=400				,							A_=True				,							A_=None				,							A_=True				,							A_=False				,							A_=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,							A_=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,							A_=True				,							) ->Dict:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = size if size is not None else {'''height''': 20, '''width''': 20}
       __lowerCAmelCase :						List[str] = parent
       __lowerCAmelCase :						List[str] = batch_size
       __lowerCAmelCase :						Optional[Any] = num_channels
       __lowerCAmelCase :						List[Any] = image_size
       __lowerCAmelCase :						Optional[int] = min_resolution
       __lowerCAmelCase :						Optional[int] = max_resolution
       __lowerCAmelCase :						List[Any] = do_resize
       __lowerCAmelCase :						Dict = size
       __lowerCAmelCase :						Dict = do_normalize
       __lowerCAmelCase :						Any = image_mean
       __lowerCAmelCase :						Optional[int] = image_std
       __lowerCAmelCase :						Any = do_pad
       __lowerCAmelCase :						Any = do_convert_rgb
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
           "do_pad": self.do_pad,
       }





 def      UpperCamelCase__  (							self				,							A_=False				,							A_=False				,							A_=False ) ->Dict:



       '''simple docstring'''





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

       if equal_resolution:
             __lowerCAmelCase :						int = []
             for i in range(self.batch_size ):
                   image_inputs.append(
                       np.random.randint(
                           255				,							size=(self.num_channels, self.max_resolution, self.max_resolution)				,							dtype=np.uinta ) )
       else:
             __lowerCAmelCase :						List[Any] = []
             for i in range(self.batch_size ):
                   __lowerCAmelCase,							__lowerCAmelCase :						Any = np.random.choice(np.arange(self.min_resolution				,							self.max_resolution )				,							2 )
                   image_inputs.append(np.random.randint(255				,							size=(self.num_channels, width, height)				,							dtype=np.uinta ) )

       if not numpify and not torchify:
             # PIL expects the channel dimension as last dimension
             __lowerCAmelCase :						List[str] = [Image.fromarray(np.moveaxis(A_				,							0				,							-1 ) ) for x in image_inputs]

       if torchify:
             __lowerCAmelCase :						int = [torch.from_numpy(A_ ) for x in image_inputs]

       return image_inputs







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						Union[str, Any] = BlipImageProcessingTester(self )
 @property
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->int:



       '''simple docstring'''





       __lowerCAmelCase :						str = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       pass
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Dict = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						List[str] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Optional[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						str = image_processor(A_				,							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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Optional[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       __lowerCAmelCase :						Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							numpify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							np.ndarray )

       # Test not batched input
       __lowerCAmelCase :						Dict = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[Any] = image_processor(A_				,							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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)





 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       __lowerCAmelCase :						str = self.image_processor_tester.prepare_inputs(equal_resolution=A_				,							torchify=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							torch.Tensor )

       # Test not batched input
       __lowerCAmelCase :						List[Any] = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						Tuple = image_processor(A_				,							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.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)







@require_torch
@require_vision
class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  BlipImageProcessor if is_vision_available() else None
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = BlipImageProcessingTester(self				,							num_channels=4 )
       __lowerCAmelCase :						str = 3
 @property
 def      UpperCamelCase__  (							self ) ->List[str]:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						int = self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(A_				,							'''do_resize''' ) )
       self.assertTrue(hasattr(A_				,							'''size''' ) )
       self.assertTrue(hasattr(A_				,							'''do_normalize''' ) )
       self.assertTrue(hasattr(A_				,							'''image_mean''' ) )
       self.assertTrue(hasattr(A_				,							'''image_std''' ) )
       self.assertTrue(hasattr(A_				,							'''do_convert_rgb''' ) )
 def      UpperCamelCase__  (							self ) ->Union[str, Any]:



       '''simple docstring'''





       pass





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       __lowerCAmelCase :						Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=A_ )
       for image in image_inputs:
             self.assertIsInstance(A_				,							Image.Image )

       # Test not batched input
       __lowerCAmelCase :						Tuple = image_processor(image_inputs[0]				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)

       # Test batched
       __lowerCAmelCase :						List[str] = image_processor(A_				,							return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_images.shape				,							(
               self.image_processor_tester.batch_size,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )				,							)


