


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:
     snake_case    :   Dict	    =					False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor







class 		_snake_case  (				unittest.TestCase							):




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=7 ,  _lowerCamelCase=3 ,  _lowerCamelCase=18 ,  _lowerCamelCase=30 ,  _lowerCamelCase=400 ,  _lowerCamelCase=None ,  _lowerCamelCase=True ,  _lowerCamelCase=True ,  _lowerCamelCase=None ,  ):
       a						:Optional[int]										=	size if size is not None else {'''height''': 20, '''width''': 20}
       a						:str										=	parent
       a						:List[str]										=	batch_size
       a						:str										=	num_channels
       a						:str										=	image_size
       a						:Any										=	min_resolution
       a						:Tuple										=	max_resolution
       a						:Tuple										=	size
       a						:Union[str, Any]										=	do_normalize
       a						:int										=	do_convert_rgb
       a						:Any										=	[512, 1024, 2048, 4096]
       a						:Tuple										=	patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
       a						:str										=	Image.open(requests.get(_lowerCamelCase ,  stream=_lowerCamelCase   ).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  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	PixaStructImageProcessor if is_vision_available() else None




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	PixaStructImageProcessingTester(self   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_normalize'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_convert_rgb'''   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	self.image_processor_tester.prepare_dummy_image()

       a						:Dict										=	self.image_processing_class(**self.image_processor_dict   )
       a						:int										=	2048

       a						:str										=	image_processor(_lowerCamelCase ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean() ,  torch.tensor(0.0606   ) ,  atol=1e-3 ,  rtol=1e-3   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processor
       a						:int										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       a						:Optional[Any]										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  Image.Image   )

       # Test not batched input
       a						:Tuple										=	(
           (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
             a						:Any										=	image_processor(
                 image_inputs[0] ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).flattened_patches
             self.assertEqual(
                 encoded_images.shape ,  (1, max_patch, expected_hidden_dim) ,  )

             # Test batched
             a						:str										=	image_processor(
                 _lowerCamelCase ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).flattened_patches
             self.assertEqual(
                 encoded_images.shape ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,  )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processor
       a						:Any										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       a						:Optional[int]										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  Image.Image   )

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

       a						:List[str]										=	True

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

             a						:Any										=	'''Hello'''

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processor
       a						:Dict										=	self.image_processing_class(**self.image_processor_dict   )
       # create random numpy tensors
       a						:Any										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase ,  numpify=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  np.ndarray   )

       a						:Tuple										=	(
           (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
             a						:Union[str, Any]										=	image_processor(
                 image_inputs[0] ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).flattened_patches
             self.assertEqual(
                 encoded_images.shape ,  (1, max_patch, expected_hidden_dim) ,  )

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processor
       a						:str										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       a						:Union[str, Any]										=	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
       a						:Dict										=	(
           (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
             a						:Tuple										=	image_processor(
                 image_inputs[0] ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).flattened_patches
             self.assertEqual(
                 encoded_images.shape ,  (1, max_patch, expected_hidden_dim) ,  )

             # Test batched
             a						:Optional[int]										=	image_processor(
                 _lowerCamelCase ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).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  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	PixaStructImageProcessor if is_vision_available() else None




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	PixaStructImageProcessingTester(self ,  num_channels=4   )
       a						:Tuple										=	3




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return self.image_processor_tester.prepare_image_processor_dict()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_normalize'''   )   )
       self.assertTrue(hasattr(_lowerCamelCase ,  '''do_convert_rgb'''   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # Initialize image_processor
       a						:Any										=	self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       a						:Dict										=	prepare_image_inputs(self.image_processor_tester ,  equal_resolution=_lowerCamelCase   )
       for image in image_inputs:
             self.assertIsInstance(_lowerCamelCase ,  Image.Image   )

       # Test not batched input
       a						:int										=	(
           (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
             a						:Dict										=	image_processor(
                 image_inputs[0] ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).flattened_patches
             self.assertEqual(
                 encoded_images.shape ,  (1, max_patch, expected_hidden_dim) ,  )

             # Test batched
             a						:Dict										=	image_processor(
                 _lowerCamelCase ,  return_tensors='''pt''' ,  max_patches=_lowerCamelCase   ).flattened_patches
             self.assertEqual(
                 encoded_images.shape ,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) ,  )









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


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





