



"""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_						(       _SCREAMING_SNAKE_CASE ,    unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				BertweetTokenizer
      __SCREAMING_SNAKE_CASE		:     List[str]				  =				False



      def 		snake_case_      (  self  :			int		):
           super().setUp()

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

           _UpperCAmelCase    :			Tuple              =					os.path.join(self.tmpdirname  ,			VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase    :			Optional[int]              =					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 		snake_case_      (  self  :			Any  ,			**A  :			Optional[Any]		):
           kwargs.update(self.special_tokens_map		)
           return BertweetTokenizer.from_pretrained(self.tmpdirname  ,			**__A		)



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



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

           _UpperCAmelCase    :			int              =					tokens + [tokenizer.unk_token]

           _UpperCAmelCase    :			Union[str, Any]              =					[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		)





"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
 from .tokenization_xglm import XGLMTokenizer
else:
 _lowerCAmelCase       :      Tuple							=							None


_lowerCAmelCase       :      List[str]							=							logging.get_logger(__name__)

_lowerCAmelCase       :      str							=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase       :      Optional[Any]							=							{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase       :      Union[str, Any]							=							{
    "facebook/xglm-564M": 20_48,
}






class 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     str				  =				VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:     List[str]				  =				PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:     str				  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:     int				  =				['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:     str				  =				XGLMTokenizer



      def __init__(  self  :			List[Any]  ,			A  :			Union[str, Any]=None  ,			A  :			Optional[Any]=None  ,			A  :			Optional[Any]="<s>"  ,			A  :			Optional[int]="</s>"  ,			A  :			str="</s>"  ,			A  :			int="<s>"  ,			A  :			int="<unk>"  ,			A  :			List[str]="<pad>"  ,			**A  :			Tuple  ,			):
           # Compatibility with the original tokenizer
           _UpperCAmelCase    :			Union[str, Any]              =					7
           _UpperCAmelCase    :			Optional[int]              =					[f'<madeupword{i}>' for i in range(self.num_madeup_words		)]

           _UpperCAmelCase    :			Any              =					kwargs.get("additional_special_tokens"  ,			[]		)
           kwargs["additional_special_tokens"] += [
               word for word in madeup_words if word not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               A  ,			tokenizer_file=A  ,			bos_token=A  ,			eos_token=A  ,			sep_token=A  ,			cls_token=A  ,			unk_token=A  ,			pad_token=A  ,			**A  ,			)

           _UpperCAmelCase    :			Any              =					vocab_file
           _UpperCAmelCase    :			Union[str, Any]              =					False if not self.vocab_file else True



      def 		snake_case_      (  self  :			List[str]  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):

           if token_ids_a is None:
                return [self.sep_token_id] + token_ids_a
           _UpperCAmelCase    :			Tuple              =					[self.sep_token_id]
           return sep + token_ids_a + sep + sep + token_ids_a



      def 		snake_case_      (  self  :			str  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
           _UpperCAmelCase    :			Optional[int]              =					[self.sep_token_id]

           if token_ids_a is None:
                return len(sep + token_ids_a		) * [0]
           return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]



      def 		snake_case_      (  self  :			Dict  ,			A  :			str  ,			A  :			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(A		):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory.'		)
                return
           _UpperCAmelCase    :			str              =					os.path.join(
               A  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

           return (out_vocab_file,)
