


import string
from math import logaa


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		document.translate(
           str.maketrans(''''''							,			''''''							,			string.punctuation       )       ).replace('''\n'''							,			''''''       )
       UpperCamelCase    :	Optional[int]   	=		document_without_punctuation.split(''' '''       )  # word tokenization
       return len([word for word in tokenize_document if word.lower() == term.lower()]       )


def      a	(							SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		corpus.lower().translate(
           str.maketrans(''''''							,			''''''							,			string.punctuation       )       )  # strip all punctuation and replace it with ''
       UpperCamelCase    :	Optional[int]   	=		corpus_without_punctuation.split('''\n'''       )
       UpperCamelCase    :	Dict   	=		term.lower()
       return (len([doc for doc in docs if term in doc]       ), len(SCREAMING_SNAKE_CASE_       ))


def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int=False       ):






       """simple docstring"""





       if smoothing:
              if n == 0:
                     raise ValueError('''log10(0) is undefined.'''       )
              return round(1 + logaa(n / (1 + df)       )							,			3       )

       if df == 0:
              raise ZeroDivisionError('''df must be > 0'''       )
       elif n == 0:
              raise ValueError('''log10(0) is undefined.'''       )
       return round(logaa(n / df       )							,			3       )


def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       return round(tf * idf							,			3       )





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_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(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


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

