




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_mvp import MvpTokenizer


UpperCamelCase__ :   str						=							logging.get_logger(__name__)


UpperCamelCase__ :   Optional[Any]						=							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__ :   Optional[int]						=							{
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__ :   Tuple						=							{
    '''RUCAIBox/mvp''': 10_24,
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Optional[int]						=     VOCAB_FILES_NAMES
				__a     :       Tuple						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       str						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[Any]						=     ["input_ids", "attention_mask"]
				__a     :       Dict						=     MvpTokenizer






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=None      ,snake_case__="replace"      ,snake_case__="<s>"      ,snake_case__="</s>"      ,snake_case__="</s>"      ,snake_case__="<s>"      ,snake_case__="<unk>"      ,snake_case__="<pad>"      ,snake_case__="<mask>"      ,snake_case__=False      ,snake_case__=True      ,**snake_case__      ,):
							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__      ,)

							SCREAMING_SNAKE_CASE_   : str					  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
							if pre_tok_state.get('add_prefix_space'      ,snake_case__ ) != add_prefix_space:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      getattr(snake_case__      ,pre_tok_state.pop('type' ) )
										SCREAMING_SNAKE_CASE_   : Tuple					  =      add_prefix_space
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      pre_tok_class(**snake_case__ )

							SCREAMING_SNAKE_CASE_   : int					  =      add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      'post_processor'
							SCREAMING_SNAKE_CASE_   : str					  =      getattr(self.backend_tokenizer      ,snake_case__      ,snake_case__ )
							if tokenizer_component_instance:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      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:
													SCREAMING_SNAKE_CASE_   : List[str]					  =      tuple(state['sep'] )
										if "cls" in state:
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tuple(state['cls'] )

										SCREAMING_SNAKE_CASE_   : List[Any]					  =      False

										if state.get('add_prefix_space'      ,snake_case__ ) != add_prefix_space:
													SCREAMING_SNAKE_CASE_   : Any					  =      add_prefix_space
													SCREAMING_SNAKE_CASE_   : str					  =      True

										if state.get('trim_offsets'      ,snake_case__ ) != trim_offsets:
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      trim_offsets
													SCREAMING_SNAKE_CASE_   : str					  =      True

										if changes_to_apply:
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      getattr(snake_case__      ,state.pop('type' ) )
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      component_class(**snake_case__ )
													setattr(self.backend_tokenizer      ,snake_case__      ,snake_case__ )






				@property
				def 		snake_case	(			self ):
							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 		snake_case	(			self      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      AddedToken(snake_case__      ,lstrip=snake_case__      ,rstrip=snake_case__ ) if isinstance(snake_case__      ,snake_case__ ) else value
							SCREAMING_SNAKE_CASE_   : Tuple					  =      value






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      kwargs.get('is_split_into_words'      ,snake_case__ )

							if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    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 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      kwargs.get('is_split_into_words'      ,snake_case__ )

							if is_split_into_words and not self.add_prefix_space:
										raise ValueError(
										    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 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self._tokenizer.model.save(snake_case__      ,name=snake_case__ )
							return tuple(snake_case__ )






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_   : int					  =      [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 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : str					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : 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 + sep + token_ids_a + sep ) * [0]






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # 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(A__ ,  'wb'	) as f:
            f.write(json.dumps(A__	).encode('utf-8'	)	)


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


