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

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


if is_sentencepiece_available():
   from .tokenization_xlnet import XLNetTokenizer
else:
   _A						      =				None


_A						      =				logging.get_logger(__name__)

_A						      =				{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_A						      =				{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

_A						      =				{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

_A						      =				"▁"

# Segments (not really needed)
_A						      =				0
_A						      =				1
_A						      =				2
_A						      =				3
_A						      =				4







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							str     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							Tuple     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							List[str]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							Any     =	"left"
     UpperCAmelCase__       :							Union[str, Any]     =	XLNetTokenizer





     def __init__(self   :					List[str]       ,					_A   :					Dict=None       ,					_A   :					str=None       ,					_A   :					str=False       ,					_A   :					Dict=True       ,					_A   :					str=False       ,					_A   :					str="<s>"       ,					_A   :					str="</s>"       ,					_A   :					Tuple="<unk>"       ,					_A   :					Dict="<sep>"       ,					_A   :					Optional[Any]="<pad>"       ,					_A   :					Any="<cls>"       ,					_A   :					Optional[int]="<mask>"       ,					_A   :					Optional[int]=["<eop>", "<eod>"]       ,					**_A   :					Dict       ,					)		->		int:
         # Mask token behave like a normal word, i.e. include the space before it
         snake_case				  =      AddedToken(_A       ,					lstrip=_A       ,					rstrip=_A     ) if isinstance(_A       ,					_A     ) else mask_token

         super().__init__(
             vocab_file=_A       ,					tokenizer_file=_A       ,					do_lower_case=_A       ,					remove_space=_A       ,					keep_accents=_A       ,					bos_token=_A       ,					eos_token=_A       ,					unk_token=_A       ,					sep_token=_A       ,					pad_token=_A       ,					cls_token=_A       ,					mask_token=_A       ,					additional_special_tokens=_A       ,					**_A       ,					)

         snake_case				  =      3
         snake_case				  =      do_lower_case
         snake_case				  =      remove_space
         snake_case				  =      keep_accents
         snake_case				  =      vocab_file
         snake_case				  =      False if not self.vocab_file else True





     def 	UpperCAmelCase(self   :					str       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None     )		->		List[int]:
         snake_case				  =      [self.sep_token_id]
         snake_case				  =      [self.cls_token_id]
         if token_ids_a is None:
             return token_ids_a + sep + cls
         return token_ids_a + sep + token_ids_a + sep + cls





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					List[int]       ,					_A   :					Optional[List[int]] = None     )		->		List[int]:
         snake_case				  =      [self.sep_token_id]
         snake_case				  =      [2]

         if token_ids_a is None:
             return len(token_ids_a + sep     ) * [0] + cls_segment_id
         return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id





     def 	UpperCAmelCase(self   :					int       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         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
         snake_case				  =      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,)



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

