
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   __lowerCAmelCase		(		lowerCAmelCase_       ,       unittest.TestCase					):



      """simple docstring"""







      A__		:	Union[str, Any]         =			RobertaTokenizer
      A__		:	Any         =			RobertaTokenizerFast
      A__		:	Optional[Any]         =			True
      A__		:	Dict         =			{'''cls_token''': '''<s>'''}




      def 						snake_case_	(					self						:				Any ):
          super().setUp()

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

          __lowercase						:	int					=				os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES['''vocab_file'''] )
          __lowercase						:	int					=				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						:				str ,				**_snake_case						:				List[Any] ):
          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname ,				**_snake_case )




      def 						snake_case_	(					self						:				Union[str, Any] ,				**_snake_case						:				str ):
          kwargs.update(self.special_tokens_map )
          return RobertaTokenizerFast.from_pretrained(self.tmpdirname ,				**_snake_case )




      def 						snake_case_	(					self						:				str ,				_snake_case						:				str ):
          __lowercase						:	List[Any]					=				'''lower newer'''
          __lowercase						:	Optional[Any]					=				'''lower newer'''
          return input_text, output_text




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

          __lowercase						:	Tuple					=				tokens + [tokenizer.unk_token]
          __lowercase						:	List[Any]					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ) ,				_snake_case )




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	Dict					=				self.get_tokenizer()

          self.assertListEqual(tokenizer.encode('''Hello world!''' ,				add_special_tokens=_snake_case ) ,				[0, 3_1414, 232, 328, 2] )
          self.assertListEqual(
              tokenizer.encode('''Hello world! cécé herlolip 418''' ,				add_special_tokens=_snake_case ) ,				[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] ,				)




      @slow
      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	Optional[int]					=				self.tokenizer_class.from_pretrained('''roberta-base''' )

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

          __lowercase						:	str					=				tokenizer.encode(
              '''sequence builders''' ,				add_special_tokens=_snake_case ,				add_prefix_space=_snake_case )
          __lowercase						:	Optional[Any]					=				tokenizer.encode(
              '''sequence builders''' ,				'''multi-sequence build''' ,				add_special_tokens=_snake_case ,				add_prefix_space=_snake_case )

          __lowercase						:	str					=				tokenizer.build_inputs_with_special_tokens(_snake_case )
          __lowercase						:	int					=				tokenizer.build_inputs_with_special_tokens(_snake_case ,				_snake_case )

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	Dict					=				self.get_tokenizer()

          __lowercase						:	List[str]					=				'''Encode this sequence.'''
          __lowercase						:	Optional[int]					=				tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]

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

          __lowercase						:	Tuple					=				tokenizer.encode(_snake_case ,				add_special_tokens=_snake_case ,				add_prefix_space=_snake_case )
          __lowercase						:	Optional[Any]					=				tokenizer.convert_ids_to_tokens(encoded[0] )[0]
          self.assertEqual(_snake_case ,				_snake_case )

          tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
          __lowercase						:	Any					=				tokenizer.encode(_snake_case ,				add_special_tokens=_snake_case )
          __lowercase						:	int					=				tokenizer.convert_ids_to_tokens(encoded[1] )[0]
          self.assertNotEqual(_snake_case ,				_snake_case )

          # Testing spaces after special tokens
          __lowercase						:	int					=				'''<mask>'''
          tokenizer.add_special_tokens(
              {'''mask_token''': AddedToken(_snake_case ,				lstrip=_snake_case ,				rstrip=_snake_case )} )  # mask token has a left space
          __lowercase						:	str					=				tokenizer.convert_tokens_to_ids(_snake_case )

          __lowercase						:	Union[str, Any]					=				'''Encode <mask> sequence'''
          __lowercase						:	str					=				'''Encode <mask>sequence'''

          __lowercase						:	List[Any]					=				tokenizer.encode(_snake_case )
          __lowercase						:	Tuple					=				encoded.index(_snake_case )
          __lowercase						:	List[str]					=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
          self.assertEqual(_snake_case ,				_snake_case )

          __lowercase						:	Union[str, Any]					=				tokenizer.encode(_snake_case )
          __lowercase						:	int					=				encoded.index(_snake_case )
          __lowercase						:	str					=				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
          self.assertNotEqual(_snake_case ,				_snake_case )




      def 						snake_case_	(					self						:				List[Any] ):
          pass




      def 						snake_case_	(					self						:				Union[str, Any] ):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ):
                  __lowercase						:	List[str]					=				self.rust_tokenizer_class.from_pretrained(_snake_case ,				**_snake_case )
                  __lowercase						:	List[Any]					=				self.tokenizer_class.from_pretrained(_snake_case ,				**_snake_case )
                  __lowercase						:	str					=				'''A, <mask> AllenNLP sentence.'''
                  __lowercase						:	int					=				tokenizer_r.encode_plus(_snake_case ,				add_special_tokens=_snake_case ,				return_token_type_ids=_snake_case )
                  __lowercase						:	Optional[int]					=				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'''] ) ,				)

                  __lowercase						:	int					=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  __lowercase						:	List[Any]					=				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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
                  self.assertSequenceEqual(tokens_r['''input_ids'''] ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 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>'''] )




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

              __lowercase						:	Dict					=				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
              __lowercase						:	Tuple					=				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

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

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




      def 						snake_case_	(					self						:				Any ):
          # 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})' ):
                  __lowercase						:	Tuple					=				'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                  __lowercase						:	List[str]					=				F'{text_of_1_token} {text_of_1_token}'

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

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

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

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

                  __lowercase						:	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)),
                  # )

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

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

                  __lowercase						:	Optional[int]					=				self.rust_tokenizer_class.from_pretrained(
                      _snake_case ,				use_fast=_snake_case ,				add_prefix_space=_snake_case ,				trim_offsets=_snake_case )
                  __lowercase						:	str					=				tokenizer_r(_snake_case ,				return_offsets_mapping=_snake_case ,				add_special_tokens=_snake_case )
                  self.assertEqual(encoding.offset_mapping[0] ,				(0, 1 + len(_snake_case )) )
                  self.assertEqual(
                      encoding.offset_mapping[1] ,				(1 + len(_snake_case ), 1 + len(_snake_case ) + 1 + len(_snake_case )) ,				)







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SqueezeBertForMaskedLM',
      'SqueezeBertForMultipleChoice',
      'SqueezeBertForQuestionAnswering',
      'SqueezeBertForSequenceClassification',
      'SqueezeBertForTokenClassification',
      'SqueezeBertModel',
      'SqueezeBertModule',
      'SqueezeBertPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_squeezebert import (
      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SqueezeBertConfig,
      SqueezeBertOnnxConfig,
  )
  from .tokenization_squeezebert import SqueezeBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_squeezebert import (
        SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        SqueezeBertForMaskedLM,
        SqueezeBertForMultipleChoice,
        SqueezeBertForQuestionAnswering,
        SqueezeBertForSequenceClassification,
        SqueezeBertForTokenClassification,
        SqueezeBertModel,
        SqueezeBertModule,
        SqueezeBertPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


