






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:
 lowerCamelCase	:							Dict				=False

if is_vision_available():
 from PIL import Image

 from transformers import PixaStructImageProcessor





class 			__a  (    unittest.TestCase       ):







 def __init__(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[str]							,   SCREAMING_SNAKE_CASE					:   List[str]=7							,   SCREAMING_SNAKE_CASE					:   str=3							,   SCREAMING_SNAKE_CASE					:   List[str]=18							,   SCREAMING_SNAKE_CASE					:   Any=30							,   SCREAMING_SNAKE_CASE					:   Tuple=4_00							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Any=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  size if size is not None else {"height": 20, "width": 20}
      UpperCamelCase__			:   Any       				=  parent
      UpperCamelCase__			:   Optional[Any]       				=  batch_size
      UpperCamelCase__			:   Union[str, Any]       				=  num_channels
      UpperCamelCase__			:   Optional[int]       				=  image_size
      UpperCamelCase__			:   List[str]       				=  min_resolution
      UpperCamelCase__			:   int       				=  max_resolution
      UpperCamelCase__			:   List[Any]       				=  size
      UpperCamelCase__			:   List[str]       				=  do_normalize
      UpperCamelCase__			:   str       				=  do_convert_rgb
      UpperCamelCase__			:   str       				=  [5_12, 10_24, 20_48, 40_96]
      UpperCamelCase__			:   Optional[Any]       				=  patch_size if patch_size is not None else {"height": 16, "width": 16}







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
      UpperCamelCase__			:   str       				=  Image.open(requests.get(SCREAMING_SNAKE_CASE							,   stream=SCREAMING_SNAKE_CASE						).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 			__a  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       int        = PixaStructImageProcessor if is_vision_available() else None







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  PixaStructImageProcessingTester(self						)







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_normalize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_convert_rgb"						)						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.image_processor_tester.prepare_dummy_image()

      UpperCamelCase__			:   Dict       				=  self.image_processing_class(**self.image_processor_dict						)
      UpperCamelCase__			:   Tuple       				=  20_48

      UpperCamelCase__			:   Dict       				=  image_processor(SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						)
      self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,   torch.tensor(0.0_6_0_6						)							,   atol=1e-3							,   rtol=1e-3						)						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   str       				=  prepare_image_inputs(self.image_processor_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   Image.Image						)

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

           # Test batched
           UpperCamelCase__			:   int       				=  image_processor(
               SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,   )







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


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   int       				=  prepare_image_inputs(self.image_processor_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   Image.Image						)

      # Test not batched input
      UpperCamelCase__			:   int       				=  (
          (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
          * self.image_processor_tester.num_channels
      ) + 2

      UpperCamelCase__			:   Optional[Any]       				=  True

      for max_patch in self.image_processor_tester.max_patches:
           # Test not batched input
           with self.assertRaises(SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Optional[Any]       				=  image_processor(
                    image_inputs[0]							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).flattened_patches

           UpperCamelCase__			:   Union[str, Any]       				=  "Hello"

           UpperCamelCase__			:   Optional[Any]       				=  image_processor(
               image_inputs[0]							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE							,   header_text=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (1, max_patch, expected_hidden_dim)							,   )

           # Test batched
           UpperCamelCase__			:   Optional[Any]       				=  image_processor(
               SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE							,   header_text=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,   )







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__			:   List[Any]       				=  prepare_image_inputs(self.image_processor_tester							,   equal_resolution=SCREAMING_SNAKE_CASE							,   numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   np.ndarray						)

      UpperCamelCase__			:   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
           UpperCamelCase__			:   Any       				=  image_processor(
               image_inputs[0]							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (1, max_patch, expected_hidden_dim)							,   )

           # Test batched
           UpperCamelCase__			:   Tuple       				=  image_processor(
               SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,   )







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__			:   Tuple       				=  prepare_image_inputs(self.image_processor_tester							,   equal_resolution=SCREAMING_SNAKE_CASE							,   torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)

      # Test not batched input
      UpperCamelCase__			:   Optional[int]       				=  (
          (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
           UpperCamelCase__			:   List[Any]       				=  image_processor(
               image_inputs[0]							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (1, max_patch, expected_hidden_dim)							,   )

           # Test batched
           UpperCamelCase__			:   Tuple       				=  image_processor(
               SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).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 			__a  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       Tuple        = PixaStructImageProcessor if is_vision_available() else None







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  PixaStructImageProcessingTester(self							,   num_channels=4						)
      UpperCamelCase__			:   List[Any]       				=  3







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_normalize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_convert_rgb"						)						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   str       				=  prepare_image_inputs(self.image_processor_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   Image.Image						)

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

           # Test batched
           UpperCamelCase__			:   List[str]       				=  image_processor(
               SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   max_patches=SCREAMING_SNAKE_CASE						).flattened_patches
           self.assertEqual(
               encoded_images.shape							,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,   )







lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()