






'''simple docstring'''





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 	a__				(				lowerCamelCase__			,     unittest.TestCase      ):





			'''simple docstring'''

			A	:							int							= PreTrainedTokenizerFast
			A	:							Tuple							= False
			A	:							Tuple							= True
			A	:							Optional[int]							= '''tokenizer_file'''
			def 				lowerCAmelCase    (     self       : Dict  )			->		str:
						__A=  False  # because we don't have pretrained_vocab_files_map
						super().setUp()
						__A=  True

						__A=  ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
						__A=  'SaulLu/dummy-tokenizer-bytelevel-bpe'

						# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
						__A=  [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

						__A=  PreTrainedTokenizerFast.from_pretrained(model_paths[0]  )
						tokenizer.save_pretrained(self.tmpdirname  )
			def 				lowerCAmelCase    (     self       : List[str]  )			->		int:
						# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
						# model
						pass
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
						# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
						# model
						pass
			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
						# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
						# model
						pass
			def 				lowerCAmelCase    (     self       : str  )			->		int:
						# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
						pass
			def 				lowerCAmelCase    (     self       : List[str]  )			->		Tuple:
						__A=  self.tmpdirname
						# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
												try:
															__A=  tempfile.mkdtemp()
															__A=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,						**SCREAMING_SNAKE_CASE_  )

															tokenizer.save_pretrained(self.tmpdirname  )
															super().test_training_new_tokenizer()
												finally:
															# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
															# is restored
															shutil.rmtree(self.tmpdirname  )
															__A=  tmpdirname_orig
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
						__A=  self.tmpdirname
						# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
												try:
															__A=  tempfile.mkdtemp()
															__A=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,						**SCREAMING_SNAKE_CASE_  )

															tokenizer.save_pretrained(self.tmpdirname  )
															super().test_training_new_tokenizer_with_special_tokens_change()
												finally:
															# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
															# is restored
															shutil.rmtree(self.tmpdirname  )
															__A=  tmpdirname_orig

			def 				lowerCAmelCase    (     self       : List[str]  )			->		Optional[Any]:
						__A=  self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name  )

						__A=  ('a' for _ in range(1_000  ))
						__A=  tokenizer.train_new_from_iterator(text_iterator=SCREAMING_SNAKE_CASE_ ,						length=1_000 ,						vocab_size=50  )

						__A=  new_tokenizer.encode('a🤗'  )
						self.assertEqual(SCREAMING_SNAKE_CASE_ ,						[64, 172, 253, 97, 245]  )


@require_tokenizers
class 	a__				(				unittest.TestCase      ):





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		Tuple:
						__A=  AutoTokenizer.from_pretrained('bert-base-cased'  )
						__A=  json.loads(tokenizer._tokenizer.to_str()  )
						__A=  len(SCREAMING_SNAKE_CASE_  )

						with tempfile.TemporaryDirectory() as tmp_dir:
									# Hack to save this in the tokenizer_config.json
									__A=  ['tokenizer.4.0.0.json']
									tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_  )
									json.dump(SCREAMING_SNAKE_CASE_ ,						open(os.path.join(SCREAMING_SNAKE_CASE_ ,						'tokenizer.4.0.0.json'  ) ,						'w'  )  )

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									__A=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_  ) ,						len(SCREAMING_SNAKE_CASE_  ) + 1  )
									__A=  json.loads(new_tokenizer._tokenizer.to_str()  )
									self.assertIn('huggingface' ,						json_tokenizer['model']['vocab']  )

									# Will need to be adjusted if we reach v42 and this test is still here.
									# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
									shutil.move(os.path.join(SCREAMING_SNAKE_CASE_ ,						'tokenizer.4.0.0.json'  ) ,						os.path.join(SCREAMING_SNAKE_CASE_ ,						'tokenizer.42.0.0.json'  )  )
									__A=  ['tokenizer.42.0.0.json']
									tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_  )
									__A=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
									self.assertEqual(len(SCREAMING_SNAKE_CASE_  ) ,						len(SCREAMING_SNAKE_CASE_  )  )
									__A=  json.loads(new_tokenizer._tokenizer.to_str()  )
									self.assertNotIn('huggingface' ,						json_tokenizer['model']['vocab']  )

			def 				lowerCAmelCase    (     self       : str  )			->		List[str]:
						# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
						__A=  'hf-internal-testing/test-two-tokenizers'

						# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
						__A=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
						self.assertEqual(len(SCREAMING_SNAKE_CASE_  ) ,						28_997  )
						__A=  json.loads(tokenizer._tokenizer.to_str()  )
						self.assertIn('huggingface' ,						json_tokenizer['model']['vocab']  )

						# Testing an older version by monkey-patching the version in the module it's used.
						import transformers as old_transformers

						__A=  '3.0.0'
						__A=  old_transformers.models.auto.AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
						self.assertEqual(len(SCREAMING_SNAKE_CASE_  ) ,						28_996  )
						__A=  json.loads(old_tokenizer._tokenizer.to_str()  )
						self.assertNotIn('huggingface' ,						json_tokenizer['model']['vocab']  )


@require_tokenizers
class 	a__				(				unittest.TestCase      ):





			'''simple docstring'''

			def 				lowerCAmelCase    (     self       : List[Any]  )			->		Optional[Any]:
						# See https://github.com/huggingface/transformers/pull/12550
						# and https://github.com/huggingface/tokenizers/issues/537
						__A=  PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'  )
						__A=  'The Matrix is a 1999 science fiction action film.'

						with concurrent.futures.ThreadPoolExecutor() as executor:
									__A=  [executor.submit(self.fetch ,						SCREAMING_SNAKE_CASE_ ,						SCREAMING_SNAKE_CASE_  ) for i in range(10  )]
									__A=  [future.result() for future in futures]
									self.assertEqual(SCREAMING_SNAKE_CASE_ ,						[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10  )]  )

			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Any  )			->		List[str]:
						return tokenizer.encode(SCREAMING_SNAKE_CASE_ ,						truncation='longest_first' ,						padding='longest'  )







'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int  )			->		Optional[int]:
						__A=  0
						# max greyscale value for #FFFFFF
						__A=  int(self.get_greyscale(255 ,						255 ,						255  )  )

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

						__A=  input_img
						__A=  threshold
						__A,  __A=  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
						__A=  [
						    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
						]
						__A=  np.ones((self.width, self.height, 3) ,						np.uinta  ) * 255
			@classmethod
			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		float:
						return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red

			def 				lowerCAmelCase    (     self       : str  )			->		None:
						for y in range(self.height  ):
									for x in range(self.width  ):
												__A=  int(self.get_greyscale(*self.input_img[y][x]  )  )
												if self.threshold > greyscale + self.error_table[y][x]:
															__A=  (0, 0, 0)
															__A=  greyscale + self.error_table[x][y]
												else:
															__A=  (255, 255, 255)
															__A=  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, 1_2_6, 1_3_0, 1_4_0)
					]

					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()



