'''simple docstring'''
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:
      lowercase__  :      Dict        =					False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor







class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



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






  def  lowerCAmelCase__ (      self			:       Any  )				->Any:
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[Any]:
        UpperCAmelCase_            =  '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
        UpperCAmelCase_            =  Image.open(requests.get(UpperCAmelCase__  ,							stream=UpperCAmelCase__  ).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 lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	PixaStructImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Union[str, Any]:
        UpperCAmelCase_            =  PixaStructImageProcessingTester(self  )






  @property
  def  lowerCAmelCase__ (      self			:       Dict  )				->Dict:
        return self.image_processor_tester.prepare_image_processor_dict()






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Any:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_normalize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_convert_rgb'''  )  )






  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        UpperCAmelCase_            =  self.image_processor_tester.prepare_dummy_image()

        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        UpperCAmelCase_            =  2048

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






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Any:
        # Initialize image_processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

        # Test not batched input
        UpperCAmelCase_            =  (
            (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_            =  image_processor(
                  image_inputs[0]  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

              # Test batched
              UpperCAmelCase_            =  image_processor(
                  UpperCAmelCase__  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Any:
        # Initialize image_processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

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

        UpperCAmelCase_            =  True

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

              UpperCAmelCase_            =  '''Hello'''

              UpperCAmelCase_            =  image_processor(
                  image_inputs[0]  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ,							header_text=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

              # Test batched
              UpperCAmelCase_            =  image_processor(
                  UpperCAmelCase__  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ,							header_text=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






  def  lowerCAmelCase__ (      self			:       int  )				->Optional[int]:
        # Initialize image_processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							numpify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							np.ndarray  )

        UpperCAmelCase_            =  (
            (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_            =  image_processor(
                  image_inputs[0]  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

              # Test batched
              UpperCAmelCase_            =  image_processor(
                  UpperCAmelCase__  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






  def  lowerCAmelCase__ (      self			:       Optional[Any]  )				->str:
        # Initialize image_processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  (
            (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_            =  image_processor(
                  image_inputs[0]  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

              # Test batched
              UpperCAmelCase_            =  image_processor(
                  UpperCAmelCase__  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).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 lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	PixaStructImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Dict:
        UpperCAmelCase_            =  PixaStructImageProcessingTester(self  ,							num_channels=4  )
        UpperCAmelCase_            =  3






  @property
  def  lowerCAmelCase__ (      self			:       int  )				->Dict:
        return self.image_processor_tester.prepare_image_processor_dict()






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Any:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_normalize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_convert_rgb'''  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->List[Any]:
        # Initialize image_processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

        # Test not batched input
        UpperCAmelCase_            =  (
            (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_            =  image_processor(
                  image_inputs[0]  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(1, max_patch, expected_hidden_dim)  ,							)

              # Test batched
              UpperCAmelCase_            =  image_processor(
                  UpperCAmelCase__  ,							return_tensors='''pt'''  ,							max_patches=UpperCAmelCase__  ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,							)






'''simple docstring'''
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ''''''
      for word_or_phrase in separated:
            if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
                  raise Exception('''join() accepts only strings to be joined'''					)
            joined += word_or_phrase + separator
      return joined.strip(_UpperCamelCase					)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





