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			: List[Any]							   =		False

if is_vision_available():
   from PIL import Image

   from transformers import PixaStructImageProcessor







class __snake_case				(						unittest.TestCase		):




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



     """simple docstring"""






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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
     SCREAMING_SNAKE_CASE__										=      Image.open(requests.get(_lowercase       ,	stream=_lowercase      ).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				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							PixaStructImageProcessor if is_vision_available() else None




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      PixaStructImageProcessingTester(self      )




   @property
   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(_lowercase       ,	"""do_normalize"""      )      )
     self.assertTrue(hasattr(_lowercase       ,	"""do_convert_rgb"""      )      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processor_tester.prepare_dummy_image()

     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     SCREAMING_SNAKE_CASE__										=      20_48

     SCREAMING_SNAKE_CASE__										=      image_processor(_lowercase       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      )
     self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,	torch.tensor(0.06_06      )       ,	atol=1E-3       ,	rtol=1E-3      )      )




   def 	__a						(    self			:				int      ):



     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      (
         (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__										=      image_processor(
           image_inputs[0]       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

       # Test batched
       SCREAMING_SNAKE_CASE__										=      image_processor(
           _lowercase       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__										=      True

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

       SCREAMING_SNAKE_CASE__										=      """Hello"""

       SCREAMING_SNAKE_CASE__										=      image_processor(
           image_inputs[0]       ,	return_tensors="""pt"""       ,	max_patches=_lowercase       ,	header_text=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

       # Test batched
       SCREAMING_SNAKE_CASE__										=      image_processor(
           _lowercase       ,	return_tensors="""pt"""       ,	max_patches=_lowercase       ,	header_text=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__										=      prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_lowercase       ,	numpify=_lowercase      )
     for image in image_inputs:
       self.assertIsInstance(_lowercase       ,	np.ndarray      )

     SCREAMING_SNAKE_CASE__										=      (
         (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__										=      image_processor(
           image_inputs[0]       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

       # Test batched
       SCREAMING_SNAKE_CASE__										=      image_processor(
           _lowercase       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__										=      prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_lowercase       ,	torchify=_lowercase      )
     for image in image_inputs:
       self.assertIsInstance(_lowercase       ,	torch.Tensor      )

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      (
         (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__										=      image_processor(
           image_inputs[0]       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

       # Test batched
       SCREAMING_SNAKE_CASE__										=      image_processor(
           _lowercase       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).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				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							PixaStructImageProcessor if is_vision_available() else None




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      PixaStructImageProcessingTester(self       ,	num_channels=4      )
     SCREAMING_SNAKE_CASE__										=      3




   @property
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(_lowercase       ,	"""do_normalize"""      )      )
     self.assertTrue(hasattr(_lowercase       ,	"""do_convert_rgb"""      )      )




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



     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      (
         (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__										=      image_processor(
           image_inputs[0]       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(1, max_patch, expected_hidden_dim)       ,	)

       # Test batched
       SCREAMING_SNAKE_CASE__										=      image_processor(
           _lowercase       ,	return_tensors="""pt"""       ,	max_patches=_lowercase      ).flattened_patches
       self.assertEqual(
           encoded_images.shape       ,	(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,	)

import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
