



'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					_UpperCamelCase											=			False

if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    PixaStructImageProcessor,
					    PixaStructProcessor,
					    PreTrainedTokenizerFast,
					    TaTokenizer,
					)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	,				reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."	,				)
@require_vision
@require_torch
class _A  (    unittest.TestCase    ):





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     tempfile.mkdtemp()

					__UpperCAmelCase   :      Dict										=     PixaStructImageProcessor()
					__UpperCAmelCase   :      Optional[int]										=     TaTokenizer.from_pretrained("""t5-small"""				)

					__UpperCAmelCase   :      str										=     PixaStructProcessor(__UpperCAmelCase ,       __UpperCAmelCase				)

					processor.save_pretrained(self.tmpdirname				)





		def 							__A       (     self ,       **__UpperCAmelCase				)       ->     Dict:

					'''simple docstring'''
					return AutoProcessor.from_pretrained(self.tmpdirname ,       **__UpperCAmelCase				).tokenizer





		def 							__A       (     self ,       **__UpperCAmelCase				)       ->     Tuple:

					'''simple docstring'''
					return AutoProcessor.from_pretrained(self.tmpdirname ,       **__UpperCAmelCase				).image_processor





		def 							__A       (     self				)       ->     int:

					'''simple docstring'''
					shutil.rmtree(self.tmpdirname				)





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     [np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta				)]

					__UpperCAmelCase   :      List[Any]										=     [Image.fromarray(np.moveaxis(__UpperCAmelCase ,       0 ,       -1				)				) for x in image_inputs]

					return image_inputs





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

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[Any]										=     PixaStructProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()				)
					processor.save_pretrained(self.tmpdirname				)

					__UpperCAmelCase   :      Union[str, Any]										=     self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""				)
					__UpperCAmelCase   :      List[str]										=     self.get_image_processor(do_normalize=__UpperCAmelCase ,       padding_value=1.0				)

					__UpperCAmelCase   :      Optional[int]										=     PixaStructProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token="""(BOS)""" ,       eos_token="""(EOS)""" ,       do_normalize=__UpperCAmelCase ,       padding_value=1.0				)

					self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()				)
					self.assertIsInstance(processor.tokenizer ,       __UpperCAmelCase				)

					self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()				)
					self.assertIsInstance(processor.image_processor ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     List[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.get_image_processor()
					__UpperCAmelCase   :      int										=     self.get_tokenizer()

					__UpperCAmelCase   :      List[str]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Union[str, Any]										=     image_processor(__UpperCAmelCase ,       return_tensors="""np"""				)
					__UpperCAmelCase   :      int										=     processor(images=__UpperCAmelCase ,       return_tensors="""np"""				)

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1E-2				)





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

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     self.get_image_processor()
					__UpperCAmelCase   :      int										=     self.get_tokenizer()

					__UpperCAmelCase   :      Optional[int]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[int]										=     """lower newer"""

					__UpperCAmelCase   :      List[Any]										=     processor(text=__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     tokenizer(__UpperCAmelCase ,       return_token_type_ids=__UpperCAmelCase ,       add_special_tokens=__UpperCAmelCase				)

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]				)





		def 							__A       (     self				)       ->     Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.get_image_processor()
					__UpperCAmelCase   :      Dict										=     self.get_tokenizer()

					__UpperCAmelCase   :      Any										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Union[str, Any]										=     """lower newer"""
					__UpperCAmelCase   :      List[str]										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Tuple										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase				)

					self.assertListEqual(
					    list(inputs.keys()				) ,       ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]				)

					# test if it raises when no input is passed
					with pytest.raises(__UpperCAmelCase				):
								processor()





		def 							__A       (     self				)       ->     Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     self.get_image_processor()
					__UpperCAmelCase   :      List[str]										=     self.get_tokenizer()

					__UpperCAmelCase   :      Optional[int]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Any										=     """lower newer"""
					__UpperCAmelCase   :      str										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Dict										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     [512, 1_024, 2_048, 4_096]
					__UpperCAmelCase   :      Dict										=     [770, 770, 770, 770]
					# with text
					for i, max_patch in enumerate(__UpperCAmelCase				):
								__UpperCAmelCase   :      List[Any]										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase ,       max_patches=__UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[0] ,       __UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[1] ,       expected_hidden_size[i]				)

					# without text input
					for i, max_patch in enumerate(__UpperCAmelCase				):
								__UpperCAmelCase   :      List[str]										=     processor(images=__UpperCAmelCase ,       max_patches=__UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[0] ,       __UpperCAmelCase				)
								self.assertEqual(inputs["""flattened_patches"""][0].shape[1] ,       expected_hidden_size[i]				)





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

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     self.get_image_processor()
					__UpperCAmelCase   :      List[str]										=     self.get_tokenizer()

					__UpperCAmelCase   :      Any										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Optional[Any]										=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase   :      Tuple										=     processor.batch_decode(__UpperCAmelCase				)
					__UpperCAmelCase   :      Any										=     tokenizer.batch_decode(__UpperCAmelCase				)

					self.assertListEqual(__UpperCAmelCase ,       __UpperCAmelCase				)





		def 							__A       (     self				)       ->     Dict:

					'''simple docstring'''
					__UpperCAmelCase   :      List[Any]										=     self.get_image_processor()
					__UpperCAmelCase   :      Union[str, Any]										=     self.get_tokenizer()

					__UpperCAmelCase   :      Union[str, Any]										=     PixaStructProcessor(tokenizer=__UpperCAmelCase ,       image_processor=__UpperCAmelCase				)

					__UpperCAmelCase   :      Dict										=     """lower newer"""
					__UpperCAmelCase   :      Tuple										=     self.prepare_image_inputs()

					__UpperCAmelCase   :      Union[str, Any]										=     processor(text=__UpperCAmelCase ,       images=__UpperCAmelCase				)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(
					    list(inputs.keys()				) ,       ["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]				)

					__UpperCAmelCase   :      List[str]										=     processor(text=__UpperCAmelCase				)

					# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
					self.assertListEqual(list(inputs.keys()				) ,       ["""input_ids""", """attention_mask"""]				)









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





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

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

					if not self.min_threshold < threshold < self.max_threshold:
								__UpperCAmelCase   :      str										=     f'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error				)
											self.error_table[y][x + 2] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x] += int(8 / 32 * current_error				)
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					_UpperCamelCase											=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 126, 130, 140)
					]

					for burkes in burkes_instances:
										burkes.process()

					for burkes in burkes_instances:
										imshow(
										    F'Original image with dithering threshold: {burkes.threshold}',
										    burkes.output_img,
										)

					waitKey(0)
					destroyAllWindows()




