



'''simple docstring'''






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


UpperCamelCase__:     int										=    logging.get_logger(__name__)
UpperCamelCase__:     Any										=    {"vocab_file": "spiece.model"}

UpperCamelCase__:     Optional[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",
    }
}

UpperCamelCase__:     List[str]										=    {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

UpperCamelCase__:     Dict										=    "▁"

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







	def __init__(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int=True	,    __snake_case    :  List[Any]=True	,    __snake_case    :  Optional[Any]=False	,    __snake_case    :  str="[CLS]"	,    __snake_case    :  str="[SEP]"	,    __snake_case    :  int="<unk>"	,    __snake_case    :  Any="[SEP]"	,    __snake_case    :  List[Any]="<pad>"	,    __snake_case    :  Tuple="[CLS]"	,    __snake_case    :  Tuple="[MASK]"	,    __snake_case    :  Optional[Dict[str, Any]] = None	,    **__snake_case    :  List[Any]	,    )    ->	None:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								UpperCAmelCase				: Tuple											=	(
								    AddedToken(__snake_case	,    lstrip=__snake_case	,    rstrip=__snake_case	,    normalized=__snake_case	)
								    if isinstance(__snake_case	,    __snake_case	)
								    else mask_token
								)

								UpperCAmelCase				: List[str]											=	{} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCAmelCase				: Union[str, Any]											=	do_lower_case
								UpperCAmelCase				: List[str]											=	remove_space
								UpperCAmelCase				: Tuple											=	keep_accents
								UpperCAmelCase				: List[str]											=	vocab_file

								UpperCAmelCase				: Union[str, Any]											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(__snake_case	)







	@property
	def   A			(		self    :  Dict	)    ->	int:
								return len(self.sp_model	)







	def   A			(		self    :  Tuple	)    ->	Optional[Any]:
								UpperCAmelCase				: int											=	{self.convert_ids_to_tokens(__snake_case	): i for i in range(self.vocab_size	)}
								vocab.update(self.added_tokens_encoder	)
								return vocab







	def __getstate__(		self    :  int	)    ->	Any:
								UpperCAmelCase				: Tuple											=	self.__dict__.copy()
								UpperCAmelCase				: Any											=	None
								return state







	def __setstate__(		self    :  Dict	,    __snake_case    :  Dict	)    ->	List[Any]:
								UpperCAmelCase				: Optional[Any]											=	d

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

								UpperCAmelCase				: str											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(self.vocab_file	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Dict	)    ->	Any:
								if self.remove_space:
															UpperCAmelCase				: Optional[int]											=	''' '''.join(inputs.strip().split()	)
								else:
															UpperCAmelCase				: Optional[int]											=	inputs
								UpperCAmelCase				: List[str]											=	outputs.replace('''``'''	,    '''"'''	).replace('''\'\''''	,    '''"'''	)

								if not self.keep_accents:
															UpperCAmelCase				: str											=	unicodedata.normalize('''NFKD'''	,    __snake_case	)
															UpperCAmelCase				: Optional[Any]											=	''''''.join([c for c in outputs if not unicodedata.combining(__snake_case	)]	)
								if self.do_lower_case:
															UpperCAmelCase				: Optional[int]											=	outputs.lower()

								return outputs







	def   A			(		self    :  List[Any]	,    __snake_case    :  str	)    ->	List[str]:
								UpperCAmelCase				: Optional[int]											=	self.preprocess_text(__snake_case	)
								UpperCAmelCase				: List[Any]											=	self.sp_model.encode(__snake_case	,    out_type=__snake_case	)
								UpperCAmelCase				: List[str]											=	[]
								for piece in pieces:
															if len(__snake_case	) > 1 and piece[-1] == str(''','''	) and piece[-2].isdigit():
																						UpperCAmelCase				: Tuple											=	self.sp_model.EncodeAsPieces(piece[:-1].replace(__snake_case	,    ''''''	)	)
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																													if len(cur_pieces[0]	) == 1:
																																				UpperCAmelCase				: List[Any]											=	cur_pieces[1:]
																													else:
																																				UpperCAmelCase				: List[Any]											=	cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]	)
																						new_pieces.extend(__snake_case	)
															else:
																						new_pieces.append(__snake_case	)

								return new_pieces







	def   A			(		self    :  Any	,    __snake_case    :  Dict	)    ->	Dict:
								return self.sp_model.PieceToId(__snake_case	)







	def   A			(		self    :  Tuple	,    __snake_case    :  str	)    ->	int:
								return self.sp_model.IdToPiece(__snake_case	)







	def   A			(		self    :  Any	,    __snake_case    :  int	)    ->	Any:
								UpperCAmelCase				: Tuple											=	[]
								UpperCAmelCase				: List[Any]											=	''''''
								UpperCAmelCase				: str											=	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(__snake_case	) + token
																						UpperCAmelCase				: Union[str, Any]											=	True
																						UpperCAmelCase				: Union[str, Any]											=	[]
															else:
																						current_sub_tokens.append(__snake_case	)
																						UpperCAmelCase				: List[Any]											=	False
								out_string += self.sp_model.decode(__snake_case	)
								return out_string.strip()







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	)    ->	List[int]:
								UpperCAmelCase				: Union[str, Any]											=	[self.sep_token_id]
								UpperCAmelCase				: Optional[int]											=	[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    :  int	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  bool = False	)    ->	List[int]:

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

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







	def   A			(		self    :  List[str]	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	)    ->	List[int]:
								UpperCAmelCase				: Union[str, Any]											=	[self.sep_token_id]
								UpperCAmelCase				: Union[str, Any]											=	[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    :  Dict	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	Tuple[str]:
								if not os.path.isdir(__snake_case	):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
															return
								UpperCAmelCase				: Optional[int]											=	os.path.join(
								    __snake_case	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

								if os.path.abspath(self.vocab_file	) != os.path.abspath(__snake_case	) and os.path.isfile(self.vocab_file	):
															copyfile(self.vocab_file	,    __snake_case	)
								elif not os.path.isfile(self.vocab_file	):
															with open(__snake_case	,    '''wb'''	) as fi:
																						UpperCAmelCase				: List[str]											=	self.sp_model.serialized_model_proto()
																						fi.write(__snake_case	)

								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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

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

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, 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(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

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

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

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

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





