from __future__ import annotations







def     _a				(      lowercase__  :			list[int | float]     ,  lowercase__  :			int     ,  lowercase__  :			int ):







     '''simple docstring'''


     if len(lowercase__ ) == 0:
          raise ValueError('find_max() arg is an empty sequence' )
     if (
         left >= len(lowercase__ )
         or left < -len(lowercase__ )
         or right >= len(lowercase__ )
         or right < -len(lowercase__ )
     ):
          raise IndexError('list index out of range' )
     if left == right:
          return nums[left]
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(left + right) >> 1  # the middle
     SCREAMING_SNAKE_CASE__				:				int			=		find_max(lowercase__     ,  lowercase__     ,  lowercase__ )  # find max in range[left, mid]
     SCREAMING_SNAKE_CASE__				:				Tuple			=		find_max(lowercase__     ,  mid + 1     ,  lowercase__ )  # find max in range[mid + 1, right]

     return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)




import unittest

import numpy as np
import requests

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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      SCREAMING_SNAKE_CASE__		:					Any						=					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor

class       snake_case					(      unittest.TestCase						):




  def __init__(      self     :	List[Any]     , a_     :	Optional[int]     , a_     :	Dict=7     , a_     :	Any=3     , a_     :	Any=18     , a_     :	int=30     , a_     :	int=400     , a_     :	List[Any]=None     , a_     :	int=True     , a_     :	int=True     , a_     :	Dict=None     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		size if size is not None else {'height': 20, 'width': 20}
       SCREAMING_SNAKE_CASE__				:				str			=		parent
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		batch_size
       SCREAMING_SNAKE_CASE__				:				Any			=		num_channels
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		min_resolution
       SCREAMING_SNAKE_CASE__				:				Dict			=		max_resolution
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_normalize
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		do_convert_rgb
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[512, 1024, 2048, 4096]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size if patch_size is not None else {'height': 16, 'width': 16}




  def 	__lowercase(      self     :	Optional[Any]				)->       str:




       """simple docstring"""
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




  def 	__lowercase(      self     :	Dict				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       SCREAMING_SNAKE_CASE__				:				str			=		Image.open(requests.get(a_     , stream=a_				).raw				).convert('RGB'				)
       return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	List[str]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		PixaStructImageProcessingTester(self				)




  @property
  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	List[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.image_processor_tester.prepare_dummy_image()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		2048

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(a_     , return_tensors='pt'     , max_patches=a_				)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()     , torch.tensor(0.0606				)     , atol=1e-3     , rtol=1e-3				)				)




  def 	__lowercase(      self     :	Any				)->       Tuple:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Dict			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       SCREAMING_SNAKE_CASE__				:				int			=		True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(a_				):
                 SCREAMING_SNAKE_CASE__				:				Dict			=		image_processor(
                     image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		'Hello'

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Any			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_     , header_text=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , numpify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , np.ndarray				)

       SCREAMING_SNAKE_CASE__				:				str			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				str			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				int			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




  def 	__lowercase(      self     :	str				)->       Optional[Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				Any			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_     , torchify=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , torch.Tensor				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Any			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'   ,						)
@require_torch
@require_vision
class       snake_case					(      UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     PixaStructImageProcessor if is_vision_available() else None




  def 	__lowercase(      self     :	Union[str, Any]				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		PixaStructImageProcessingTester(self     , num_channels=4				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		3




  @property
  def 	__lowercase(      self     :	Any				)->       Any:




       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(a_     , 'do_normalize'				)				)
       self.assertTrue(hasattr(a_     , 'do_convert_rgb'				)				)




  def 	__lowercase(      self     :	str				)->       Union[str, Any]:




       """simple docstring"""
       # Initialize image_processor
       SCREAMING_SNAKE_CASE__				:				str			=		self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       SCREAMING_SNAKE_CASE__				:				Tuple			=		prepare_image_inputs(self.image_processor_tester     , equal_resolution=a_				)
       for image in image_inputs:
            self.assertIsInstance(a_     , Image.Image				)

       # Test not batched input
       SCREAMING_SNAKE_CASE__				:				Dict			=		(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(
                image_inputs[0]     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (1, max_patch, expected_hidden_dim)     , )

            # Test batched
            SCREAMING_SNAKE_CASE__				:				Tuple			=		image_processor(
                a_     , return_tensors='pt'     , max_patches=a_				).flattened_patches
            self.assertEqual(
                encoded_images.shape     , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)     , )



