




"""simple docstring"""


import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A_						=				50_00_00

A_	,    A_						=				os.path.split(__file__)
A_						=				os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 					UpperCAmelCase__       (snake_case__							:					datasets.Dataset		,   **snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  dataset.map(**snake_case__    )


@get_duration
def 					UpperCAmelCase__       (snake_case__							:					datasets.Dataset		,   **snake_case__							:					Any    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  dataset.filter(**snake_case__    )


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Dict								=  {"""num examples""": SPEED_TEST_N_EXAMPLES}
  with tempfile.TemporaryDirectory() as tmp_dir:
    _snake_case							:		Dict								=  datasets.Features({"""text""": datasets.Value("""string"""    ), """numbers""": datasets.Value("""float32"""    )}    )
    _snake_case							:		List[Any]								=  generate_example_dataset(
        os.path.join(snake_case__		,   """dataset.arrow"""    )		,   snake_case__		,   num_examples=snake_case__    )

    _snake_case							:		List[Any]								=  transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""		,   use_fast=snake_case__    )

    def tokenize(snake_case__							:					Optional[int]    ):
      return tokenizer(examples["""text"""]    )

    _snake_case							:		str								=  map(snake_case__    )

    _snake_case							:		Optional[int]								=  map(snake_case__		,   batched=snake_case__    )

    _snake_case							:		int								=  map(snake_case__		,   function=lambda snake_case__    : None		,   batched=snake_case__    )

    with dataset.formatted_as(type="""numpy"""    ):
      _snake_case							:		Dict								=  map(snake_case__		,   function=lambda snake_case__    : None		,   batched=snake_case__    )

    with dataset.formatted_as(type="""pandas"""    ):
      _snake_case							:		List[str]								=  map(snake_case__		,   function=lambda snake_case__    : None		,   batched=snake_case__    )

    with dataset.formatted_as(type="""torch"""		,   columns="""numbers"""    ):
      _snake_case							:		Union[str, Any]								=  map(snake_case__		,   function=lambda snake_case__    : None		,   batched=snake_case__    )

    with dataset.formatted_as(type="""tensorflow"""		,   columns="""numbers"""    ):
      _snake_case							:		List[str]								=  map(snake_case__		,   function=lambda snake_case__    : None		,   batched=snake_case__    )

    _snake_case							:		Dict								=  map(snake_case__		,   function=snake_case__		,   batched=snake_case__    )

    _snake_case							:		List[str]								=  filter(snake_case__    )

    # Activate later when tokenizer support batched inputs
    # with dataset.formatted_as(type='numpy'):
    #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

  with open(snake_case__		,   """wb"""    ) as f:
    f.write(json.dumps(snake_case__    ).encode("""utf-8"""    )    )


if __name__ == "__main__":  # useful to run the profiler
     benchmark_map_filter()








"""simple docstring"""


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


A_						=				logging.get_logger(__name__)

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

A_						=				{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

A_						=				{
    '''yjernite/retribert-base-uncased''': 5_12,
}


A_						=				{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					PRETRAINED_INIT_CONFIGURATION
 lowercase__ 					=					RetriBertTokenizer
 lowercase__ 					=					["input_ids", "attention_mask"]


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




   '''simple docstring'''



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

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

   _snake_case							:		List[str]								=  do_lower_case


 def  UpperCamelCase_   (				self:		Any,      a_:		str,      a_:		Optional[int]=None		):




   '''simple docstring'''



   _snake_case							:		Optional[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  UpperCamelCase_   (				self:		List[str],      a_:		List[int],      a_:		Optional[List[int]] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  [self.sep_token_id]
   _snake_case							:		List[str]								=  [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  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


