


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


snake_case							:	List[Any]					      =				logging.get_logger(__name__)

snake_case							:	Dict					      =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:	Union[str, Any]					      =				{
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:	Union[str, Any]					      =				{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Any   				=      VOCAB_FILES_NAMES
					UpperCAmelCase__ :   Tuple   				=      PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__ :   Optional[int]   				=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__ :   int   				=      ['''input_ids''', '''attention_mask''']
					UpperCAmelCase__ :   Union[str, Any]   				=      RobertaTokenizer
					def __init__(				self       :Tuple      ,__snake_case       :Optional[Any]=None      ,__snake_case       :List[Any]=None      ,__snake_case       :Optional[Any]=None      ,__snake_case       :List[Any]="replace"      ,__snake_case       :Union[str, Any]="<s>"      ,__snake_case       :Tuple="</s>"      ,__snake_case       :Dict="</s>"      ,__snake_case       :Optional[Any]="<s>"      ,__snake_case       :List[str]="<unk>"      ,__snake_case       :Tuple="<pad>"      ,__snake_case       :Dict="<mask>"      ,__snake_case       :int=False      ,__snake_case       :Optional[Any]=True      ,**__snake_case       :List[Any]      ,) ->   Optional[int]:
												super().__init__(
												    __snake_case      ,__snake_case      ,tokenizer_file=__snake_case      ,errors=__snake_case      ,bos_token=__snake_case      ,eos_token=__snake_case      ,sep_token=__snake_case      ,cls_token=__snake_case      ,unk_token=__snake_case      ,pad_token=__snake_case      ,mask_token=__snake_case      ,add_prefix_space=__snake_case      ,trim_offsets=__snake_case      ,**__snake_case      ,)

												a__					=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
												if pre_tok_state.get('add_prefix_space'      ,__snake_case				) != add_prefix_space:
																			a__					=					getattr(__snake_case      ,pre_tok_state.pop('type'				)				)
																			a__					=					add_prefix_space
																			a__					=					pre_tok_class(**__snake_case				)

												a__					=					add_prefix_space

												a__					=					'post_processor'
												a__					=					getattr(self.backend_tokenizer      ,__snake_case      ,__snake_case				)
												if tokenizer_component_instance:
																			a__					=					json.loads(tokenizer_component_instance.__getstate__()				)

																			# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																			if "sep" in state:
																										a__					=					tuple(state['sep']				)
																			if "cls" in state:
																										a__					=					tuple(state['cls']				)

																			a__					=					False

																			if state.get('add_prefix_space'      ,__snake_case				) != add_prefix_space:
																										a__					=					add_prefix_space
																										a__					=					True

																			if state.get('trim_offsets'      ,__snake_case				) != trim_offsets:
																										a__					=					trim_offsets
																										a__					=					True

																			if changes_to_apply:
																										a__					=					getattr(__snake_case      ,state.pop('type'				)				)
																										a__					=					component_class(**__snake_case				)
																										setattr(self.backend_tokenizer      ,__snake_case      ,__snake_case				)
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												if self._mask_token is None:
																			if self.verbose:
																										logger.error('Using mask_token, but it is not set yet.'				)
																			return None
												return str(self._mask_token				)
					@mask_token.setter
					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :Dict				) ->   int:
												a__					=					AddedToken(__snake_case      ,lstrip=__snake_case      ,rstrip=__snake_case				) if isinstance(__snake_case      ,__snake_case				) else value
												a__					=					value
					def 			lowerCamelCase__(				self       :int      ,*__snake_case       :List[Any]      ,**__snake_case       :List[str]				) ->   BatchEncoding:
												a__					=					kwargs.get('is_split_into_words'      ,__snake_case				)
												assert self.add_prefix_space or not is_split_into_words, (
												    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :str      ,*__snake_case       :Tuple      ,**__snake_case       :str				) ->   BatchEncoding:
												a__					=					kwargs.get('is_split_into_words'      ,__snake_case				)

												assert self.add_prefix_space or not is_split_into_words, (
												    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :str      ,__snake_case       :Optional[str] = None				) ->   Tuple[str]:
												a__					=					self._tokenizer.model.save(__snake_case      ,name=__snake_case				)
												return tuple(__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :int      ,__snake_case       :Any=None				) ->   str:
												a__					=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																			return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



					def 			lowerCamelCase__(				self       :Optional[int]      ,__snake_case       :List[int]      ,__snake_case       :Optional[List[int]] = None				) ->   List[int]:
												a__					=					[self.sep_token_id]
												a__					=					[self.cls_token_id]

												if token_ids_a is None:
																			return len(cls + token_ids_a + sep				) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]








import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 	snake_case_       (lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      MvpTokenizer
					UpperCAmelCase__ :   List[Any]   				=      MvpTokenizerFast
					UpperCAmelCase__ :   Optional[int]   				=      True
					UpperCAmelCase__ :   Tuple   				=      filter_roberta_detectors
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Union[str, Any]:
												super().setUp()
												a__					=					[
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    '\u0120',
												    '\u0120l',
												    '\u0120n',
												    '\u0120lo',
												    '\u0120low',
												    'er',
												    '\u0120lowest',
												    '\u0120newer',
												    '\u0120wider',
												    '<unk>',
												]
												a__					=					dict(zip(__snake_case      ,range(len(__snake_case				)				)				)				)
												a__					=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
												a__					=					{'unk_token': '<unk>'}

												a__					=					os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES['vocab_file']				)
												a__					=					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(__snake_case				) + '\n'				)
												with open(self.merges_file      ,'w'      ,encoding='utf-8'				) as fp:
																			fp.write('\n'.join(__snake_case				)				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Tuple				) ->   int:
												kwargs.update(self.special_tokens_map				)
												return self.tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Optional[Any]      ,**__snake_case       :Dict				) ->   Dict:
												kwargs.update(self.special_tokens_map				)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Union[str, Any]				) ->   List[Any]:
												return "lower newer", "lower newer"
					@cached_property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return MvpTokenizer.from_pretrained('RUCAIBox/mvp'				)
					@cached_property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'				)
					@require_torch
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   str:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												a__					=					[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,max_length=len(__snake_case				)      ,padding=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)

																			self.assertEqual((2, 9)      ,batch.input_ids.shape				)
																			self.assertEqual((2, 9)      ,batch.attention_mask.shape				)
																			a__					=					batch.input_ids.tolist()[0]
																			self.assertListEqual(__snake_case      ,__snake_case				)
																			# Test that special tokens are reset
					@require_torch
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   List[Any]:
												a__					=					['A long paragraph for summarization.', 'Another paragraph for summarization.']
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,padding=__snake_case      ,return_tensors='pt'				)
																			# check if input_ids are returned and no labels
																			self.assertIn('input_ids'      ,__snake_case				)
																			self.assertIn('attention_mask'      ,__snake_case				)
																			self.assertNotIn('labels'      ,__snake_case				)
																			self.assertNotIn('decoder_attention_mask'      ,__snake_case				)
					@require_torch
					def 			lowerCamelCase__(				self       :Any				) ->   int:
												a__					=					[
												    'Summary of the text.',
												    'Another summary.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(text_target=__snake_case      ,max_length=32      ,padding='max_length'      ,return_tensors='pt'				)
																			self.assertEqual(32      ,targets['input_ids'].shape[1]				)
					@require_torch
					def 			lowerCamelCase__(				self       :int				) ->   Union[str, Any]:
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(
																			    ['I am a small frog' * 10_24, 'I am a small frog']      ,padding=__snake_case      ,truncation=__snake_case      ,return_tensors='pt'				)
																			self.assertIsInstance(__snake_case      ,__snake_case				)
																			self.assertEqual(batch.input_ids.shape      ,(2, 10_24)				)
					@require_torch
					def 			lowerCamelCase__(				self       :List[Any]				) ->   Any:
												a__					=					['A long paragraph for summarization.']
												a__					=					[
												    'Summary of the text.',
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a__					=					tokenizer(__snake_case      ,text_target=__snake_case      ,return_tensors='pt'				)
																			a__					=					inputs['input_ids']
																			a__					=					inputs['labels']
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()				)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()				)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Tuple:
												pass



					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[Any]:
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
																										a__					=					self.rust_tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					self.tokenizer_class.from_pretrained(__snake_case      ,**__snake_case				)
																										a__					=					'A, <mask> AllenNLP sentence.'
																										a__					=					tokenizer_r.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)
																										a__					=					tokenizer_p.encode_plus(__snake_case      ,add_special_tokens=__snake_case      ,return_token_type_ids=__snake_case				)

																										# 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__					=					tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']				)
																										a__					=					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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)
																										self.assertSequenceEqual(tokens_r['input_ids']      ,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]				)

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




