
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__							:							Optional[int]													=	logging.get_logger(__name__)
lowercase__							:							Any													=	{"vocab_file": "spiece.model"}

lowercase__							:							Union[str, Any]													=	{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

lowercase__							:							str													=	{
    "albert-base-v1": 5_1_2,
    "albert-large-v1": 5_1_2,
    "albert-xlarge-v1": 5_1_2,
    "albert-xxlarge-v1": 5_1_2,
    "albert-base-v2": 5_1_2,
    "albert-large-v2": 5_1_2,
    "albert-xlarge-v2": 5_1_2,
    "albert-xxlarge-v2": 5_1_2,
}

lowercase__							:							Optional[int]													=	"▁"






class 				SCREAMING_SNAKE_CASE__			(							SCREAMING_SNAKE_CASE_			):

							"""simple docstring"""






							_snake_case   						=     VOCAB_FILES_NAMES
							_snake_case   						=     PRETRAINED_VOCAB_FILES_MAP
							_snake_case   						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



							def __init__(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_=True   ,		SCREAMING_SNAKE_CASE_=True   ,		SCREAMING_SNAKE_CASE_=False   ,		SCREAMING_SNAKE_CASE_="[CLS]"   ,		SCREAMING_SNAKE_CASE_="[SEP]"   ,		SCREAMING_SNAKE_CASE_="<unk>"   ,		SCREAMING_SNAKE_CASE_="[SEP]"   ,		SCREAMING_SNAKE_CASE_="<pad>"   ,		SCREAMING_SNAKE_CASE_="[CLS]"   ,		SCREAMING_SNAKE_CASE_="[MASK]"   ,		SCREAMING_SNAKE_CASE_ = None   ,		**SCREAMING_SNAKE_CASE_   ,		)->      Any:





													'''simple docstring'''






													__UpperCamelCase        =	(
													    AddedToken(__UpperCAmelCase   ,		lstrip=__UpperCAmelCase   ,		rstrip=__UpperCAmelCase   ,		normalized=__UpperCAmelCase	)
													    if isinstance(__UpperCAmelCase   ,		__UpperCAmelCase	)
													    else mask_token
													)

													__UpperCamelCase        =	{} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    do_lower_case=__UpperCAmelCase   ,		remove_space=__UpperCAmelCase   ,		keep_accents=__UpperCAmelCase   ,		bos_token=__UpperCAmelCase   ,		eos_token=__UpperCAmelCase   ,		unk_token=__UpperCAmelCase   ,		sep_token=__UpperCAmelCase   ,		pad_token=__UpperCAmelCase   ,		cls_token=__UpperCAmelCase   ,		mask_token=__UpperCAmelCase   ,		sp_model_kwargs=self.sp_model_kwargs   ,		**__UpperCAmelCase   ,		)

													__UpperCamelCase        =	do_lower_case
													__UpperCamelCase        =	remove_space
													__UpperCamelCase        =	keep_accents
													__UpperCamelCase        =	vocab_file

													__UpperCamelCase        =	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
													self.sp_model.Load(__UpperCAmelCase	)



							@property
							def 			A__						(     self	)->      Tuple:





													'''simple docstring'''






													return len(self.sp_model	)



							def 			A__						(     self	)->      Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase        =	{self.convert_ids_to_tokens(__UpperCAmelCase	): i for i in range(self.vocab_size	)}
													vocab.update(self.added_tokens_encoder	)
													return vocab



							def __getstate__(     self	)->      int:





													'''simple docstring'''






													__UpperCamelCase        =	self.__dict__.copy()
													__UpperCamelCase        =	None
													return state



							def __setstate__(     self   ,		SCREAMING_SNAKE_CASE_	)->      Tuple:





													'''simple docstring'''






													__UpperCamelCase        =	d

													# for backward compatibility
													if not hasattr(self   ,		'''sp_model_kwargs'''	):
																			__UpperCamelCase        =	{}

													__UpperCamelCase        =	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
													self.sp_model.Load(self.vocab_file	)



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Optional[int]:





													'''simple docstring'''






													if self.remove_space:
																			__UpperCamelCase        =	' '.join(inputs.strip().split()	)
													else:
																			__UpperCamelCase        =	inputs
													__UpperCamelCase        =	outputs.replace('''``'''   ,		'''"'''	).replace('''\'\''''   ,		'''"'''	)

													if not self.keep_accents:
																			__UpperCamelCase        =	unicodedata.normalize('''NFKD'''   ,		__UpperCAmelCase	)
																			__UpperCamelCase        =	''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase	)]	)
													if self.do_lower_case:
																			__UpperCamelCase        =	outputs.lower()

													return outputs



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase        =	self.preprocess_text(__UpperCAmelCase	)
													__UpperCamelCase        =	self.sp_model.encode(__UpperCAmelCase   ,		out_type=__UpperCAmelCase	)
													__UpperCamelCase        =	[]
													for piece in pieces:
																			if len(__UpperCAmelCase	) > 1 and piece[-1] == str(''','''	) and piece[-2].isdigit():
																									__UpperCamelCase        =	self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase   ,		''''''	)	)
																									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																															if len(cur_pieces[0]	) == 1:
																																					__UpperCamelCase        =	cur_pieces[1:]
																															else:
																																					__UpperCamelCase        =	cur_pieces[0][1:]
																									cur_pieces.append(piece[-1]	)
																									new_pieces.extend(__UpperCAmelCase	)
																			else:
																									new_pieces.append(__UpperCAmelCase	)

													return new_pieces



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Tuple:





													'''simple docstring'''






													return self.sp_model.PieceToId(__UpperCAmelCase	)



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      Dict:





													'''simple docstring'''






													return self.sp_model.IdToPiece(__UpperCAmelCase	)



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_	)->      str:





													'''simple docstring'''






													__UpperCamelCase        =	[]
													__UpperCamelCase        =	''
													__UpperCamelCase        =	False
													for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																									if not prev_is_special:
																															out_string += " "
																									out_string += self.sp_model.decode(__UpperCAmelCase	) + token
																									__UpperCamelCase        =	True
																									__UpperCamelCase        =	[]
																			else:
																									current_sub_tokens.append(__UpperCAmelCase	)
																									__UpperCamelCase        =	False
													out_string += self.sp_model.decode(__UpperCAmelCase	)
													return out_string.strip()



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None	)->      Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase        =	[self.sep_token_id]
													__UpperCamelCase        =	[self.cls_token_id]
													if token_ids_a is None:
																			return cls + token_ids_a + sep
													return cls + token_ids_a + sep + token_ids_a + sep



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None   ,		SCREAMING_SNAKE_CASE_ = False	)->      Any:





													'''simple docstring'''






													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=__UpperCAmelCase   ,		token_ids_a=__UpperCAmelCase   ,		already_has_special_tokens=__UpperCAmelCase	)

													if token_ids_a is not None:
																			return [1] + ([0] * len(__UpperCAmelCase	)) + [1] + ([0] * len(__UpperCAmelCase	)) + [1]
													return [1] + ([0] * len(__UpperCAmelCase	)) + [1]



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None	)->      Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase        =	[self.sep_token_id]
													__UpperCamelCase        =	[self.cls_token_id]

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



							def 			A__						(     self   ,		SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_ = None	)->      str:





													'''simple docstring'''






													if not os.path.isdir(__UpperCAmelCase	):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
																			return
													__UpperCamelCase        =	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	) and os.path.isfile(self.vocab_file	):
																			copyfile(self.vocab_file   ,		__UpperCAmelCase	)
													elif not os.path.isfile(self.vocab_file	):
																			with open(__UpperCAmelCase   ,		'''wb'''	) as fi:
																									__UpperCamelCase        =	self.sp_model.serialized_model_proto()
																									fi.write(__UpperCAmelCase	)

													return (out_vocab_file,)











"""simple docstring"""





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
  import torch






@require_torch
@require_optimum
@slow
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

         self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							model.reverse_bettertransformer()

         self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

           self.assertFalse(
               any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

         with tempfile.TemporaryDirectory() as tmpdirname:
           with self.assertRaises(__UpperCAmelCase      ):
             model.save_pretrained(__UpperCAmelCase      )

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
