import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					_snake_case              =      tempfile.mkdtemp()

					_snake_case              =      CLIPImageProcessor()
					_snake_case              =      BertTokenizer.from_pretrained(
					    "hf-internal-testing/tiny-random-BertModel"  ,					model_input_names=["input_ids", "attention_mask"]   )

					_snake_case              =      GitProcessor(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )

					processor.save_pretrained(self.tmpdirname   )



		def 				_a				(			self    :				List[Any]  ,					**__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return AutoProcessor.from_pretrained(self.tmpdirname  ,					**__SCREAMING_SNAKE_CASE   ).tokenizer



		def 				_a				(			self    :				Optional[int]  ,					**__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					return AutoProcessor.from_pretrained(self.tmpdirname  ,					**__SCREAMING_SNAKE_CASE   ).image_processor



		def 				_a				(			self    :				Optional[int]   ):






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



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






					"""simple docstring"""
					_snake_case              =      [np.random.randint(255  ,					size=(3, 30, 400)  ,					dtype=np.uinta   )]

					_snake_case              =      [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE  ,					0  ,					-1   )   ) for x in image_inputs]

					return image_inputs



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					_snake_case              =      GitProcessor(tokenizer=self.get_tokenizer()  ,					image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					_snake_case              =      self.get_tokenizer(bos_token="(BOS)"  ,					eos_token="(EOS)"   )
					_snake_case              =      self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE  ,					padding_value=1.0   )

					_snake_case              =      GitProcessor.from_pretrained(
					    self.tmpdirname  ,					bos_token="(BOS)"  ,					eos_token="(EOS)"  ,					do_normalize=__SCREAMING_SNAKE_CASE  ,					padding_value=1.0   )

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

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



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      self.get_image_processor()
					_snake_case              =      self.get_tokenizer()

					_snake_case              =      GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,					image_processor=__SCREAMING_SNAKE_CASE   )

					_snake_case              =      self.prepare_image_inputs()

					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					return_tensors="np"   )
					_snake_case              =      processor(images=__SCREAMING_SNAKE_CASE  ,					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    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      self.get_image_processor()
					_snake_case              =      self.get_tokenizer()

					_snake_case              =      GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,					image_processor=__SCREAMING_SNAKE_CASE   )

					_snake_case              =      "lower newer"

					_snake_case              =      processor(text=__SCREAMING_SNAKE_CASE   )

					_snake_case              =      tokenizer(__SCREAMING_SNAKE_CASE  ,					return_token_type_ids=__SCREAMING_SNAKE_CASE   )

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



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.get_image_processor()
					_snake_case              =      self.get_tokenizer()

					_snake_case              =      GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,					image_processor=__SCREAMING_SNAKE_CASE   )

					_snake_case              =      "lower newer"
					_snake_case              =      self.prepare_image_inputs()

					_snake_case              =      processor(text=__SCREAMING_SNAKE_CASE  ,					images=__SCREAMING_SNAKE_CASE   )

					self.assertListEqual(list(inputs.keys()   )  ,					["input_ids", "attention_mask", "pixel_values"]   )

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



		def 				_a				(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      self.get_image_processor()
					_snake_case              =      self.get_tokenizer()

					_snake_case              =      GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,					image_processor=__SCREAMING_SNAKE_CASE   )

					_snake_case              =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					_snake_case              =      processor.batch_decode(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

					self.assertListEqual(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )







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






					"""simple docstring"""
					_snake_case              =      self.get_image_processor()
					_snake_case              =      self.get_tokenizer()

					_snake_case              =      GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,					image_processor=__SCREAMING_SNAKE_CASE   )

					_snake_case              =      "lower newer"
					_snake_case              =      self.prepare_image_inputs()

					_snake_case              =      processor(text=__SCREAMING_SNAKE_CASE  ,					images=__SCREAMING_SNAKE_CASE   )

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


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

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

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      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
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red







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






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      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
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    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()

