





"""simple docstring"""
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin






class  _UpperCAmelCase       (     lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							BertweetTokenizer
					UpperCamelCase								=							False


					def      lowerCamelCase	( self      :List[str]		):
												super().setUp()

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

												A		=  os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES["vocab_file"]		)
												A		=  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(__UpperCamelCase		)		)


					def      lowerCamelCase	( self      :Tuple					,	**__UpperCamelCase      :str		):
												kwargs.update(self.special_tokens_map		)
												return BertweetTokenizer.from_pretrained(self.tmpdirname					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :List[Any]		):
												A		=  "I am VinAI Research"
												A		=  "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
												return input_text, output_text


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  BertweetTokenizer(self.vocab_file					,	self.merges_file					,	**self.special_tokens_map		)
												A		=  "I am VinAI Research"
												A		=  "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase					,	__UpperCamelCase		)

												A		=  tokens + [tokenizer.unk_token]

												A		=  [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)					,	__UpperCamelCase		)









"""simple docstring"""
import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_snake_case					:		Dict							=			None


_snake_case					:		int							=			logging.get_logger(__name__)


_snake_case					:		List[Any]							=			'▁'

_snake_case					:		Tuple							=			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case					:		Tuple							=			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case					:		List[Any]							=			{
    'google/reformer-crime-and-punishment': 52_4288,
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							VOCAB_FILES_NAMES
					UpperCamelCase								=							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase								=							['''input_ids''', '''attention_mask''']
					UpperCamelCase								=							ReformerTokenizer


					def __init__( self      :Dict					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Optional[Any]=None					,	__UpperCamelCase      :Dict="</s>"					,	__UpperCamelCase      :Optional[Any]="<unk>"					,	__UpperCamelCase      :Union[str, Any]=[]					,	**__UpperCamelCase      :str					,	):
												super().__init__(
												    __UpperCamelCase					,	tokenizer_file=__UpperCamelCase					,	eos_token=__UpperCamelCase					,	unk_token=__UpperCamelCase					,	additional_special_tokens=__UpperCamelCase					,	**__UpperCamelCase					,	)

												A		=  vocab_file
												A		=  False if not self.vocab_file else True


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[str] = None		):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."		)

												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(__UpperCamelCase		):
																			copyfile(self.vocab_file					,	__UpperCamelCase		)

												return (out_vocab_file,)


