

'''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 lowerCamelCase      (						__lowerCAmelCase							,							unittest.TestCase							):
				snake_case_           =   PreTrainedTokenizerFast
				snake_case_           =   False
				snake_case_           =   True
				snake_case_           =   '''tokenizer_file'''

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					snake_case							 =				False  # because we don't have pretrained_vocab_files_map
					super().setUp()
					snake_case							 =				True

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

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

					snake_case							 =				PreTrainedTokenizerFast.from_pretrained(model_paths[0]   )
					tokenizer.save_pretrained(self.tmpdirname   )

				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
					# model
					pass

				def 				_lowerCamelCase						(					self   )						->       Dict:
					# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
					# model
					pass

				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
					# model
					pass

				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
					pass

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					snake_case							 =				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:
								snake_case							 =				tempfile.mkdtemp()
								snake_case							 =				self.rust_tokenizer_class.from_pretrained(lowercase_,	**lowercase_   )

								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   )
								snake_case							 =				tmpdirname_orig

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					snake_case							 =				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:
								snake_case							 =				tempfile.mkdtemp()
								snake_case							 =				self.rust_tokenizer_class.from_pretrained(lowercase_,	**lowercase_   )

								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   )
								snake_case							 =				tmpdirname_orig






				def 				_lowerCamelCase						(					self   )						->       int:
					snake_case							 =				self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name   )

					snake_case							 =				('a' for _ in range(1000   ))
					snake_case							 =				tokenizer.train_new_from_iterator(text_iterator=lowercase_,	length=1000,	vocab_size=50   )

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







@require_tokenizers
class lowerCamelCase      (						unittest.TestCase							):

				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					snake_case							 =				AutoTokenizer.from_pretrained('bert-base-cased'   )
					snake_case							 =				json.loads(tokenizer._tokenizer.to_str()   )
					snake_case							 =				len(lowercase_   )

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

						# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
						snake_case							 =				AutoTokenizer.from_pretrained(lowercase_   )
						self.assertEqual(len(lowercase_   ),	len(lowercase_   ) + 1   )
						snake_case							 =				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(lowercase_,	'tokenizer.4.0.0.json'   ),	os.path.join(lowercase_,	'tokenizer.42.0.0.json'   )   )
						snake_case							 =				['tokenizer.42.0.0.json']
						tokenizer.save_pretrained(lowercase_   )
						snake_case							 =				AutoTokenizer.from_pretrained(lowercase_   )
						self.assertEqual(len(lowercase_   ),	len(lowercase_   )   )
						snake_case							 =				json.loads(new_tokenizer._tokenizer.to_str()   )
						self.assertNotIn('huggingface',	json_tokenizer['model']['vocab']   )






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

					# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
					snake_case							 =				AutoTokenizer.from_pretrained(lowercase_   )
					self.assertEqual(len(lowercase_   ),	28997   )
					snake_case							 =				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

					snake_case							 =				'3.0.0'
					snake_case							 =				old_transformers.models.auto.AutoTokenizer.from_pretrained(lowercase_   )
					self.assertEqual(len(lowercase_   ),	28996   )
					snake_case							 =				json.loads(old_tokenizer._tokenizer.to_str()   )
					self.assertNotIn('huggingface',	json_tokenizer['model']['vocab']   )







@require_tokenizers
class lowerCamelCase      (						unittest.TestCase							):

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

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






				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_   )						->       Optional[int]:
					return tokenizer.encode(lowercase_,	truncation='longest_first',	padding='longest'   )








'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey

def     __magic_name__       ( A					,   A							)			->		Union[str, Any]:
	snake_case , snake_case							 =				img.shape[0], img.shape[1]

	def to_grayscale(A					,   A					,   A							):
		return 0.2_126 * red + 0.587 * green + 0.114 * blue

	def normalize(A							):
		return min(A					,   2_5_5							)

	for i in range(A							):
		for j in range(A							):
			snake_case							 =				int(to_grayscale(*img[i][j]							)							)
			snake_case							 =				[
			    normalize(A							),
			    normalize(greyscale + factor							),
			    normalize(greyscale + 2 * factor							),
			]

	return img


if __name__ == "__main__":
						# read original image
						lowerCAmelCase_						   =			{
						    percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
						}

						for percentage, img in images.items():
												make_sepia(img, percentage)

						for percentage, img in images.items():
												imshow(f"Original image with sepia (factor: {percentage})", img)

						waitKey(0)
						destroyAllWindows()





