






import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       Dict     =   RobertaTokenizer
		__snake_case      :       Union[str, Any]     =   RobertaTokenizerFast
		__snake_case      :       List[str]     =   True
		__snake_case      :       int     =   {"cls_token": "<s>"}







		def  A__      (					self		):
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						_A	:				List[Any]   =      [
						    '''l''',
						    '''o''',
						    '''w''',
						    '''e''',
						    '''r''',
						    '''s''',
						    '''t''',
						    '''i''',
						    '''d''',
						    '''n''',
						    '''\u0120''',
						    '''\u0120l''',
						    '''\u0120n''',
						    '''\u0120lo''',
						    '''\u0120low''',
						    '''er''',
						    '''\u0120lowest''',
						    '''\u0120newer''',
						    '''\u0120wider''',
						    '''<unk>''',
						]
						_A	:				Dict   =      dict(zip(A__       ,range(len(A__		)		)		)		)
						_A	:				List[str]   =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
						_A	:				List[str]   =      {'''unk_token''': '''<unk>'''}

						_A	:				Optional[int]   =      os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''vocab_file''']		)
						_A	:				List[str]   =      os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''merges_file''']		)
						with open(self.vocab_file       ,'''w'''       ,encoding='''utf-8'''		) as fp:
										fp.write(json.dumps(A__		) + '''\n'''		)
						with open(self.merges_file       ,'''w'''       ,encoding='''utf-8'''		) as fp:
										fp.write('''\n'''.join(A__		)		)







		def  A__      (					self       ,**A__		):
						kwargs.update(self.special_tokens_map		)
						return self.tokenizer_class.from_pretrained(self.tmpdirname       ,**A__		)







		def  A__      (					self       ,**A__		):
						kwargs.update(self.special_tokens_map		)
						return RobertaTokenizerFast.from_pretrained(self.tmpdirname       ,**A__		)







		def  A__      (					self       ,A__		):
						_A	:				List[Any]   =      '''lower newer'''
						_A	:				str   =      '''lower newer'''
						return input_text, output_text







		def  A__      (					self		):
						_A	:				List[Any]   =      self.tokenizer_class(self.vocab_file       ,self.merges_file       ,**self.special_tokens_map		)
						_A	:				Any   =      '''lower newer'''
						_A	:				Any   =      ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
						_A	:				Dict   =      tokenizer.tokenize(A__		)  # , add_prefix_space=True)
						self.assertListEqual(A__       ,A__		)

						_A	:				Optional[Any]   =      tokens + [tokenizer.unk_token]
						_A	:				Optional[Any]   =      [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(A__		)       ,A__		)







		def  A__      (					self		):
						_A	:				Optional[Any]   =      self.get_tokenizer()

						self.assertListEqual(tokenizer.encode('''Hello world!'''       ,add_special_tokens=A__		)       ,[0, 31414, 232, 328, 2]		)
						self.assertListEqual(
						    tokenizer.encode('''Hello world! cécé herlolip 418'''       ,add_special_tokens=A__		)       ,[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]       ,)







		@slow
		def  A__      (					self		):
						_A	:				str   =      self.tokenizer_class.from_pretrained('''roberta-base'''		)

						_A	:				Union[str, Any]   =      tokenizer.encode('''sequence builders'''       ,add_special_tokens=A__		)
						_A	:				Dict   =      tokenizer.encode('''multi-sequence build'''       ,add_special_tokens=A__		)

						_A	:				List[str]   =      tokenizer.encode(
						    '''sequence builders'''       ,add_special_tokens=A__       ,add_prefix_space=A__		)
						_A	:				Union[str, Any]   =      tokenizer.encode(
						    '''sequence builders'''       ,'''multi-sequence build'''       ,add_special_tokens=A__       ,add_prefix_space=A__		)

						_A	:				List[Any]   =      tokenizer.build_inputs_with_special_tokens(A__		)
						_A	:				Dict   =      tokenizer.build_inputs_with_special_tokens(A__       ,A__		)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode







		def  A__      (					self		):
						_A	:				Optional[Any]   =      self.get_tokenizer()

						_A	:				List[Any]   =      '''Encode this sequence.'''
						_A	:				Any   =      tokenizer.byte_encoder[''' '''.encode('''utf-8'''		)[0]]

						# Testing encoder arguments
						_A	:				Optional[int]   =      tokenizer.encode(A__       ,add_special_tokens=A__       ,add_prefix_space=A__		)
						_A	:				Optional[Any]   =      tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
						self.assertNotEqual(A__       ,A__		)

						_A	:				str   =      tokenizer.encode(A__       ,add_special_tokens=A__       ,add_prefix_space=A__		)
						_A	:				Optional[int]   =      tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
						self.assertEqual(A__       ,A__		)

						tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}		)
						_A	:				Dict   =      tokenizer.encode(A__       ,add_special_tokens=A__		)
						_A	:				str   =      tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
						self.assertNotEqual(A__       ,A__		)

						# Testing spaces after special tokens
						_A	:				Dict   =      '''<mask>'''
						tokenizer.add_special_tokens(
						    {'''mask_token''': AddedToken(A__       ,lstrip=A__       ,rstrip=A__		)}		)  # mask token has a left space
						_A	:				Any   =      tokenizer.convert_tokens_to_ids(A__		)

						_A	:				int   =      '''Encode <mask> sequence'''
						_A	:				List[str]   =      '''Encode <mask>sequence'''

						_A	:				str   =      tokenizer.encode(A__		)
						_A	:				str   =      encoded.index(A__		)
						_A	:				Dict   =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
						self.assertEqual(A__       ,A__		)

						_A	:				Optional[Any]   =      tokenizer.encode(A__		)
						_A	:				str   =      encoded.index(A__		)
						_A	:				List[Any]   =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
						self.assertNotEqual(A__       ,A__		)







		def  A__      (					self		):
						pass







		def  A__      (					self		):
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
														_A	:				Optional[int]   =      self.rust_tokenizer_class.from_pretrained(A__       ,**A__		)
														_A	:				str   =      self.tokenizer_class.from_pretrained(A__       ,**A__		)
														_A	:				Dict   =      '''A, <mask> AllenNLP sentence.'''
														_A	:				Optional[Any]   =      tokenizer_r.encode_plus(A__       ,add_special_tokens=A__       ,return_token_type_ids=A__		)
														_A	:				List[Any]   =      tokenizer_p.encode_plus(A__       ,add_special_tokens=A__       ,return_token_type_ids=A__		)

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['''token_type_ids''']		)       ,sum(tokens_p['''token_type_ids''']		)		)

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		)       ,sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		)       ,)

														_A	:				Optional[int]   =      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
														_A	:				List[str]   =      tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['''input_ids''']       ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
														self.assertSequenceEqual(tokens_r['''input_ids''']       ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

														self.assertSequenceEqual(
														    A__       ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
														self.assertSequenceEqual(
														    A__       ,['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)







		def  A__      (					self		):
						for trim_offsets, add_prefix_space in itertools.product([True, False]       ,repeat=2		):
										_A	:				Optional[Any]   =      self.rust_tokenizer_class.from_pretrained(
										    self.tmpdirname       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)

										_A	:				List[str]   =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
										_A	:				List[str]   =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

										self.assertEqual(pre_tokenizer_state['''add_prefix_space''']       ,A__		)

										self.assertEqual(post_processor_state['''add_prefix_space''']       ,A__		)
										self.assertEqual(post_processor_state['''trim_offsets''']       ,A__		)







		def  A__      (					self		):
						# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
						# `trim_offsets`
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
														_A	:				Any   =      '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
														_A	:				Dict   =      f"""{text_of_1_token} {text_of_1_token}"""

														_A	:				Dict   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				str   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(0, len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(len(A__		) + 1, len(A__		) + 1 + len(A__		))       ,)

														_A	:				Tuple   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				str   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(0, len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(len(A__		) + 1, len(A__		) + 1 + len(A__		))       ,)

														_A	:				List[str]   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				Any   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(0, len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(len(A__		), len(A__		) + 1 + len(A__		))       ,)

														_A	:				Tuple   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				Tuple   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(0, len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(len(A__		), len(A__		) + 1 + len(A__		))       ,)

														_A	:				Union[str, Any]   =      f""" {text}"""

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														_A	:				Union[str, Any]   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				Optional[Any]   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(1, 1 + len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(1 + len(A__		) + 1, 1 + len(A__		) + 1 + len(A__		))       ,)

														_A	:				Dict   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				Optional[Any]   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(0, 1 + len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(1 + len(A__		), 1 + len(A__		) + 1 + len(A__		))       ,)

														_A	:				List[Any]   =      self.rust_tokenizer_class.from_pretrained(
														    A__       ,use_fast=A__       ,add_prefix_space=A__       ,trim_offsets=A__		)
														_A	:				Optional[int]   =      tokenizer_r(A__       ,return_offsets_mapping=A__       ,add_special_tokens=A__		)
														self.assertEqual(encoding.offset_mapping[0]       ,(0, 1 + len(A__		))		)
														self.assertEqual(
														    encoding.offset_mapping[1]       ,(1 + len(A__		), 1 + len(A__		) + 1 + len(A__		))       ,)











from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				if not -255.0 <= level <= 255.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
				return img.point(__lowercase   )


if __name__ == "__main__":
	# Load image
	with Image.open('image_data/lena.jpg') as img:
		# Change brightness to 100
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



