



"""simple docstring"""






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class     A__     (						_snake_case						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			FunnelTokenizer
	SCREAMING_SNAKE_CASE					   =			FunnelTokenizerFast
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			True

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Optional[int]:

				"""simple docstring"""

				super().setUp()

				__lowerCAmelCase				= [
				    "<unk>",
				    "<cls>",
				    "<sep>",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				__lowerCAmelCase				= os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES["vocab_file"])
				with open(self.vocab_file ,    "w" ,    encoding="utf-8") as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	str)       -> Union[str, Any]:

				"""simple docstring"""

				return FunnelTokenizer.from_pretrained(self.tmpdirname ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    **_SCREAMING_SNAKE_CASE:	Union[str, Any])       -> Optional[Any]:

				"""simple docstring"""

				return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	int)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase				= "UNwant\u00E9d,running"
				__lowerCAmelCase				= "unwanted, running"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase				= self.tokenizer_class(self.vocab_file)

				__lowerCAmelCase				= tokenizer.tokenize("UNwant\u00E9d,running")
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    ["un", "##want", "##ed", ",", "runn", "##ing"])
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE) ,    [7, 4, 5, 10, 8, 9])







	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> int:

				"""simple docstring"""

				__lowerCAmelCase				= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE)
				for tokenizer in tokenizers:
							__lowerCAmelCase				= tokenizer("UNwant\u00E9d,running")
							__lowerCAmelCase				= len(inputs["input_ids"]) - 1
							self.assertListEqual(inputs["token_type_ids"] ,    [2] + [0] * sentence_len)

							__lowerCAmelCase				= tokenizer("UNwant\u00E9d,running" ,    "UNwant\u00E9d,running")
							self.assertListEqual(inputs["token_type_ids"] ,    [2] + [0] * sentence_len + [1] * sentence_len)




"""simple docstring"""






import string
from math import logaa


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						int				= document.translate(
			    str.maketrans(""      ,""      ,string.punctuation			)			).replace("\n"      ,""			)
			__lowerCAmelCase   :						Dict				= document_without_punctuation.split(" "			)  # word tokenization
			return len([word for word in tokenize_document if word.lower() == term.lower()]			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     tuple[int, int]:
			__lowerCAmelCase   :						Optional[Any]				= corpus.lower().translate(
			    str.maketrans(""      ,""      ,string.punctuation			)			)  # strip all punctuation and replace it with ''
			__lowerCAmelCase   :						List[str]				= corpus_without_punctuation.split("\n"			)
			__lowerCAmelCase   :						str				= term.lower()
			return (len([doc for doc in docs if term in doc]			), len(__snake_case			))


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=False			)		->     float:
			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     _lowercase       (						__snake_case      ,__snake_case			)		->     float:
			return round(tf * idf      ,3			)