






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


UpperCamelCase    							=      logging.get_logger(__name__)

UpperCamelCase    							=      {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase    							=      {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

UpperCamelCase    							=      {
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


UpperCamelCase    							=      {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}







class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Union[str, Any]						=					VOCAB_FILES_NAMES
  lowercase_						:			str						=					PRETRAINED_VOCAB_FILES_MAP
  lowercase_						:			List[str]						=					PRETRAINED_INIT_CONFIGURATION
  lowercase_						:			int						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase_						:			Optional[int]						=					SqueezeBertTokenizer

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Tuple=None   ,				lowerCamelCase__						:	Tuple=None   ,				lowerCamelCase__						:	Tuple=True   ,				lowerCamelCase__						:	Tuple="[UNK]"   ,				lowerCamelCase__						:	Dict="[SEP]"   ,				lowerCamelCase__						:	Optional[Any]="[PAD]"   ,				lowerCamelCase__						:	Optional[Any]="[CLS]"   ,				lowerCamelCase__						:	Union[str, Any]="[MASK]"   ,				lowerCamelCase__						:	Any=True   ,				lowerCamelCase__						:	Optional[int]=None   ,				**lowerCamelCase__						:	Optional[int]   ,				):
     """simple docstring"""

     super().__init__(
         lowerCamelCase__   ,				tokenizer_file=lowerCamelCase__   ,				do_lower_case=lowerCamelCase__   ,				unk_token=lowerCamelCase__   ,				sep_token=lowerCamelCase__   ,				pad_token=lowerCamelCase__   ,				cls_token=lowerCamelCase__   ,				mask_token=lowerCamelCase__   ,				tokenize_chinese_chars=lowerCamelCase__   ,				strip_accents=lowerCamelCase__   ,				**lowerCamelCase__   ,				)

     __UpperCamelCase	:			Dict					=					json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
     if (
         normalizer_state.get("""lowercase"""   ,				lowerCamelCase__			) != do_lower_case
         or normalizer_state.get("""strip_accents"""   ,				lowerCamelCase__			) != strip_accents
         or normalizer_state.get("""handle_chinese_chars"""   ,				lowerCamelCase__			) != tokenize_chinese_chars
     ):
        __UpperCamelCase	:			Any					=					getattr(lowerCamelCase__   ,				normalizer_state.pop("""type"""			)			)
        __UpperCamelCase	:			Optional[Any]					=					do_lower_case
        __UpperCamelCase	:			List[str]					=					strip_accents
        __UpperCamelCase	:			Dict					=					tokenize_chinese_chars
        __UpperCamelCase	:			List[str]					=					normalizer_class(**lowerCamelCase__			)

     __UpperCamelCase	:			int					=					do_lower_case

  def    a					(    self						:	Dict   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	Dict=None			):
     """simple docstring"""

     __UpperCamelCase	:			Any					=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

     return output

  def    a					(    self						:	str   ,				lowerCamelCase__						:	List[int]   ,				lowerCamelCase__						:	Optional[List[int]] = None			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					[self.sep_token_id]
     __UpperCamelCase	:			List[Any]					=					[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			) * [0] + len(token_ids_a + sep			) * [1]

  def    a					(    self						:	Union[str, Any]   ,				lowerCamelCase__						:	str   ,				lowerCamelCase__						:	Optional[str] = None			):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					self._tokenizer.model.save(lowerCamelCase__   ,				name=lowerCamelCase__			)
     return tuple(lowerCamelCase__			)











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase    							=      namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any							)     ->  Optional[int]:
   return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					_TestCommandArgs(dataset=__lowerCAmelCase					,							all_configs=__lowerCAmelCase					,							save_infos=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					TestCommand(*__lowerCAmelCase							)
   test_command.run()
   __UpperCamelCase	:			Any					=					os.path.join(__lowerCAmelCase					,							"""README.md"""							)
   assert os.path.exists(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					DatasetInfosDict.from_directory(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DatasetInfosDict(
       {
           """default""": DatasetInfo(
               features=Features(
                   {
                       """tokens""": Sequence(Value("""string"""							)							),
                       """ner_tags""": Sequence(
                           ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
                       """langs""": Sequence(Value("""string"""							)							),
                       """spans""": Sequence(Value("""string"""							)							),
                   }							)					,							splits=[
                   {
                       """name""": """train""",
                       """num_bytes""": 2351563,
                       """num_examples""": 10000,
                   },
                   {
                       """name""": """validation""",
                       """num_bytes""": 238418,
                       """num_examples""": 1000,
                   },
               ]					,							download_size=3940680					,							dataset_size=2589981					,							)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					getattr(dataset_infos["""default"""]					,							__lowerCAmelCase							), getattr(expected_dataset_infos["""default"""]					,							__lowerCAmelCase							)
      if key == "num_bytes":
         assert is_apercent_close(__lowerCAmelCase					,							__lowerCAmelCase							)
      elif key == "splits":
         assert list(__lowerCAmelCase							) == list(__lowerCAmelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes					,							expected[split].num_bytes							)
      else:
         result == expected



