

import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class _UpperCamelCase							(							__A							,		unittest.TestCase				):
			'''simple docstring'''







			lowerCamelCase__          =BertweetTokenizer
			lowerCamelCase__          =False
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Dict:

				"""simple docstring"""

				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				SCREAMING_SNAKE_CASE				: Any			       =    ["I", "m", "V@@", "R@@", "r", "e@@"]
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    dict(zip(a , range(len(a		)		)		)		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    ["#version: 0.2", "a m</w>"]
				SCREAMING_SNAKE_CASE				: Dict			       =    {"unk_token": "<unk>"}

				SCREAMING_SNAKE_CASE				: List[Any]			       =    os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"]		)
				SCREAMING_SNAKE_CASE				: str			       =    os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"]		)
				with open(self.vocab_file , "w" , encoding="utf-8"		) as fp:
					for token in vocab_tokens:
						fp.write(F"{token} {vocab_tokens[token]}\n"		)
				with open(self.merges_file , "w" , encoding="utf-8"		) as fp:
					fp.write("\n".join(a		)		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , **a							:	Any		)							->    Optional[Any]:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map		)
				return BertweetTokenizer.from_pretrained(self.tmpdirname , **a		)
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	Any		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    "I am VinAI Research"
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
				return input_text, output_text






			def 							__UpperCamelCase	(     self							:	Any		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    BertweetTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map		)
				SCREAMING_SNAKE_CASE				: Dict			       =    "I am VinAI Research"
				SCREAMING_SNAKE_CASE				: int			       =    "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
				SCREAMING_SNAKE_CASE				: Dict			       =    tokenizer.tokenize(a		)
				self.assertListEqual(a , a		)

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    tokens + [tokenizer.unk_token]

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(a		) , a		)


import re

def 	lowerCamelCase__	(   _a):
	def docstring_decorator(_a):
		SCREAMING_SNAKE_CASE				: List[Any]			       =    fn.__doc__
		SCREAMING_SNAKE_CASE				: List[str]			       =    func_doc.split("\n")
		SCREAMING_SNAKE_CASE				: Dict			       =    0
		while i < len(_a) and re.search(r"^\s*Examples?:\s*$"							,     lines[i]) is None:
			i += 1
		if i < len(_a):
			SCREAMING_SNAKE_CASE				: Tuple			       =    example_docstring
			SCREAMING_SNAKE_CASE				: List[Any]			       =    "\n".join(_a)
		else:
			raise ValueError(
			    f"The function {fn} should have an empty 'Examples:' in its docstring as placeholder, "
			    f"current docstring is:\n{func_doc}")
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    func_doc
		return fn

	return docstring_decorator