






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase  :							Optional[Any]			  =    logging.get_logger(__name__)


lowercase  :							Optional[int]			  =    '''▁'''

lowercase  :							int			  =    {'''vocab_file''': '''spiece.model'''}

lowercase  :							List[str]			  =    {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowercase  :							Dict			  =    {
    '''google/reformer-crime-and-punishment''': 52_42_88,
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Union[str, Any]						=					VOCAB_FILES_NAMES
						A     :   List[str]						=					PRETRAINED_VOCAB_FILES_MAP
						A     :   int						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A     :   str						=					['input_ids', 'attention_mask']






						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE="</s>"		,				_SCREAMING_SNAKE_CASE="<unk>"		,				_SCREAMING_SNAKE_CASE=[]		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				None:
								snake_case_				:							Union[str, Any]						 =       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=_SCREAMING_SNAKE_CASE		,				unk_token=_SCREAMING_SNAKE_CASE		,				additional_special_tokens=_SCREAMING_SNAKE_CASE		,				sp_model_kwargs=self.sp_model_kwargs		,				**_SCREAMING_SNAKE_CASE		,				)

								snake_case_				:							Dict						 =       vocab_file
								snake_case_				:							List[str]						 =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								return self.sp_model.get_piece_size()






						def       _lowerCAmelCase       (	self				) ->				Dict[str, int]:
								snake_case_				:							Dict						 =       {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab






						def __getstate__(	self				) ->				List[str]:
								snake_case_				:							Dict						 =       self.__dict__.copy()
								snake_case_				:							Tuple						 =       None
								return state






						def __setstate__(	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								snake_case_				:							Optional[Any]						 =       d

								# for backward compatibility
								if not hasattr(self		,				"sp_model_kwargs"				):
										snake_case_				:							List[str]						 =       {}

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






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.sp_model.encode(_SCREAMING_SNAKE_CASE		,				out_type=_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								if index < self.sp_model.get_piece_size():
										snake_case_				:							Optional[Any]						 =       self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)
								return token






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								snake_case_				:							int						 =       []
								snake_case_				:							Tuple						 =       ""
								for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
												out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				) + token
												snake_case_				:							Optional[Any]						 =       []
										else:
												current_sub_tokens.append(_SCREAMING_SNAKE_CASE				)
								out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE				)
								return out_string.strip()






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				Tuple[str]:
								if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
										return
								snake_case_				:							List[Any]						 =       os.path.join(
								    _SCREAMING_SNAKE_CASE		,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(_SCREAMING_SNAKE_CASE				) and os.path.isfile(self.vocab_file				):
										copyfile(self.vocab_file		,				_SCREAMING_SNAKE_CASE				)
								elif not os.path.isfile(self.vocab_file				):
										with open(_SCREAMING_SNAKE_CASE		,				"wb"				) as fi:
												snake_case_				:							Optional[int]						 =       self.sp_model.serialized_model_proto()
												fi.write(_SCREAMING_SNAKE_CASE				)

								return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
