




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    BartTokenizer
      lowerCAmelCase__	:       List[Any]			=    BartTokenizerFast
      lowerCAmelCase__	:       str			=    True
      lowerCAmelCase__	:       List[str]			=    filter_roberta_detectors







      def 					snake_case		(   self    :       Optional[int] ):
          super().setUp()
          __UpperCamelCase     						=  [
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          __UpperCamelCase     						=  dict(zip(snake_case       ,				range(len(snake_case ) ) ) )
          __UpperCamelCase     						=  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          __UpperCamelCase     						=  {'''unk_token''': '''<unk>'''}

          __UpperCamelCase     						=  os.path.join(self.tmpdirname       ,				VOCAB_FILES_NAMES['''vocab_file'''] )
          __UpperCamelCase     						=  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 					snake_case		(   self    :       Tuple       ,				**snake_case    :       Optional[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       str       ,				**snake_case    :       Optional[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,				**snake_case )







      def 					snake_case		(   self    :       Any       ,				snake_case    :       Dict ):
          return "lower newer", "lower newer"







      @cached_property
      def 					snake_case		(   self    :       Optional[Any] ):
          return BartTokenizer.from_pretrained('''facebook/bart-large''' )







      @cached_property
      def 					snake_case		(   self    :       Tuple ):
          return BartTokenizerFast.from_pretrained('''facebook/bart-large''' )







      @require_torch
      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          __UpperCamelCase     						=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  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 )
              __UpperCamelCase     						=  batch.input_ids.tolist()[0]
              self.assertListEqual(snake_case       ,				snake_case )
              # Test that special tokens are reset







      @require_torch
      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  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 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase     						=  [
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  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 					snake_case		(   self    :       Union[str, Any] ):
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(
                  ['''I am a small frog''' * 1024, '''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, 1024) )







      @require_torch
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  ['''A long paragraph for summarization.''']
          __UpperCamelCase     						=  [
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              __UpperCamelCase     						=  tokenizer(snake_case       ,				return_tensors='''pt''' )
              __UpperCamelCase     						=  tokenizer(text_target=snake_case       ,				return_tensors='''pt''' )
              __UpperCamelCase     						=  inputs['''input_ids''']
              __UpperCamelCase     						=  targets['''input_ids''']
              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 					snake_case		(   self    :       str ):
          pass







      def 					snake_case		(   self    :       Dict ):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
                  __UpperCamelCase     						=  self.rust_tokenizer_class.from_pretrained(snake_case       ,				**snake_case )
                  __UpperCamelCase     						=  self.tokenizer_class.from_pretrained(snake_case       ,				**snake_case )
                  __UpperCamelCase     						=  '''A, <mask> AllenNLP sentence.'''
                  __UpperCamelCase     						=  tokenizer_r.encode_plus(snake_case       ,				add_special_tokens=snake_case       ,				return_token_type_ids=snake_case )
                  __UpperCamelCase     						=  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'''] )       ,				)

                  __UpperCamelCase     						=  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  __UpperCamelCase     						=  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(
                      snake_case       ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                  self.assertSequenceEqual(
                      snake_case       ,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )








"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
			__A       						=       None


__A       						=       logging.get_logger(__name__)

__A       						=       {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__A       						=       {
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
        ),
    },
}

__A       						=       {
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		VOCAB_FILES_NAMES
			lowerCamelCase  					=		PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase  					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase  					=		['''input_ids''', '''attention_mask''']
			lowerCamelCase  					=		XLMRobertaTokenizer



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="</s>"     ,     __UpperCAmelCase="<s>"     ,     __UpperCAmelCase="<unk>"     ,     __UpperCAmelCase="<pad>"     ,     __UpperCAmelCase="<mask>"     ,     **__UpperCAmelCase     ,     ) ->     str:
				# Mask token behave like a normal word, i.e. include the space before it
				_lowerCAmelCase								=AddedToken(__UpperCAmelCase     ,     lstrip=__UpperCAmelCase     ,     rstrip=__UpperCAmelCase  ) if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ) else mask_token

				super().__init__(
				    __UpperCAmelCase     ,     tokenizer_file=__UpperCAmelCase     ,     bos_token=__UpperCAmelCase     ,     eos_token=__UpperCAmelCase     ,     sep_token=__UpperCAmelCase     ,     cls_token=__UpperCAmelCase     ,     unk_token=__UpperCAmelCase     ,     pad_token=__UpperCAmelCase     ,     mask_token=__UpperCAmelCase     ,     **__UpperCAmelCase     ,     )

				_lowerCAmelCase								=vocab_file
				_lowerCAmelCase								=False if not self.vocab_file else True



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				_lowerCAmelCase								=[self.cls_token_id]
				_lowerCAmelCase								=[self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     List[int]:
				_lowerCAmelCase								=[self.sep_token_id]
				_lowerCAmelCase								=[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]



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase = None  ) ->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
					raise ValueError(
					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
					    """tokenizer."""  )

				if not os.path.isdir(__UpperCAmelCase  ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''  )
					return
				_lowerCAmelCase								=os.path.join(
				    __UpperCAmelCase     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

				if os.path.abspath(self.vocab_file  ) != os.path.abspath(__UpperCAmelCase  ):
					copyfile(self.vocab_file     ,     __UpperCAmelCase  )

				return (out_vocab_file,)



