


'''simple docstring'''



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


__A		=get_logger(__name__)


__A		=Path(__file__).parent / 'model_card_template.md'
__A		=uuida().hex
__A		=os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
__A		=os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
__A		=HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'







def       _UpperCamelCase (						UpperCamelCase__ = None   ):
			UpperCAmelCase__					:   List[Any]       				=       f'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
			if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
						return ua + "; telemetry/off"
			if is_torch_available():
						ua += f'''; torch/{_torch_version}'''
			if is_flax_available():
						ua += f'''; jax/{_jax_version}'''
						ua += f'''; flax/{_flax_version}'''
			if is_onnx_available():
						ua += f'''; onnxruntime/{_onnxruntime_version}'''
			# CI will set this value to True
			if os.environ.get("""DIFFUSERS_IS_CI"""	, """"""   ).upper() in ENV_VARS_TRUE_VALUES:
						ua += "; is_ci/true"
			if isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()   )
			elif isinstance(UpperCamelCase__	, UpperCamelCase__   ):
						ua += "; " + user_agent
			return ua







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None	, UpperCamelCase__ = None   ):
			if token is None:
						UpperCAmelCase__					:   List[str]       				=       HfFolder.get_token()
			if organization is None:
						UpperCAmelCase__					:   Dict       				=       whoami(UpperCamelCase__   )["""name"""]
						return f'''{username}/{model_id}'''
			else:
						return f'''{organization}/{model_id}'''







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			if not is_jinja_available():
						raise ValueError(
						    """Modelcard rendering is based on Jinja templates."""
						    """ Please make sure to have `jinja` installed before using `create_model_card`."""
						    """ To install it, please run `pip install Jinja2`."""   )

			if hasattr(UpperCamelCase__	, """local_rank"""   ) and args.local_rank not in [-1, 0]:
						return

			UpperCAmelCase__					:   int       				=       args.hub_token if hasattr(UpperCamelCase__	, """hub_token"""   ) else None
			UpperCAmelCase__					:   Optional[int]       				=       get_full_repo_name(UpperCamelCase__	, token=UpperCamelCase__   )

			UpperCAmelCase__					:   Dict       				=       ModelCard.from_template(
			    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
			        language="""en"""	, license="""apache-2.0"""	, library_name="""diffusers"""	, tags=[]	, datasets=args.dataset_name	, metrics=[]	, )	, template_path=UpperCamelCase__	, model_name=UpperCamelCase__	, repo_name=UpperCamelCase__	, dataset_name=args.dataset_name if hasattr(UpperCamelCase__	, """dataset_name"""   ) else None	, learning_rate=args.learning_rate	, train_batch_size=args.train_batch_size	, eval_batch_size=args.eval_batch_size	, gradient_accumulation_steps=(
			        args.gradient_accumulation_steps if hasattr(UpperCamelCase__	, """gradient_accumulation_steps"""   ) else None
			    )	, adam_betaa=args.adam_betaa if hasattr(UpperCamelCase__	, """adam_beta1"""   ) else None	, adam_betaa=args.adam_betaa if hasattr(UpperCamelCase__	, """adam_beta2"""   ) else None	, adam_weight_decay=args.adam_weight_decay if hasattr(UpperCamelCase__	, """adam_weight_decay"""   ) else None	, adam_epsilon=args.adam_epsilon if hasattr(UpperCamelCase__	, """adam_epsilon"""   ) else None	, lr_scheduler=args.lr_scheduler if hasattr(UpperCamelCase__	, """lr_scheduler"""   ) else None	, lr_warmup_steps=args.lr_warmup_steps if hasattr(UpperCamelCase__	, """lr_warmup_steps"""   ) else None	, ema_inv_gamma=args.ema_inv_gamma if hasattr(UpperCamelCase__	, """ema_inv_gamma"""   ) else None	, ema_power=args.ema_power if hasattr(UpperCamelCase__	, """ema_power"""   ) else None	, ema_max_decay=args.ema_max_decay if hasattr(UpperCamelCase__	, """ema_max_decay"""   ) else None	, mixed_precision=args.mixed_precision	, )

			UpperCAmelCase__					:   str       				=       os.path.join(args.output_dir	, """README.md"""   )
			model_card.save(UpperCamelCase__   )







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None   ):
			if resolved_file is None or commit_hash is not None:
						return commit_hash
			UpperCAmelCase__					:   Union[str, Any]       				=       str(Path(UpperCamelCase__   ).as_posix()   )
			UpperCAmelCase__					:   List[Any]       				=       re.search(R"""snapshots/([^/]+)/"""	, UpperCamelCase__   )
			if search is None:
						return None
			UpperCAmelCase__					:   Tuple       				=       search.groups()[0]
			return commit_hash if REGEX_COMMIT_HASH.match(UpperCamelCase__   ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__A		=os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
__A		=os.path.join(hf_cache_home, 'diffusers')







def       _UpperCamelCase (						UpperCamelCase__ = None	, UpperCamelCase__ = None   ):
			if new_cache_dir is None:
						UpperCAmelCase__					:   Union[str, Any]       				=       DIFFUSERS_CACHE
			if old_cache_dir is None:
						UpperCAmelCase__					:   Optional[int]       				=       old_diffusers_cache

			UpperCAmelCase__					:   str       				=       Path(UpperCamelCase__   ).expanduser()
			UpperCAmelCase__					:   Union[str, Any]       				=       Path(UpperCamelCase__   ).expanduser()
			for old_blob_path in old_cache_dir.glob("""**/blobs/*"""   ):
						if old_blob_path.is_file() and not old_blob_path.is_symlink():
									UpperCAmelCase__					:   List[Any]       				=       new_cache_dir / old_blob_path.relative_to(UpperCamelCase__   )
									new_blob_path.parent.mkdir(parents=UpperCamelCase__	, exist_ok=UpperCamelCase__   )
									os.replace(UpperCamelCase__	, UpperCamelCase__   )
									try:
												os.symlink(UpperCamelCase__	, UpperCamelCase__   )
									except OSError:
												logger.warning(
												    """Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."""   )
			# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


__A		=os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
		__A		=0
else:
		with open(cache_version_file) as f:
				try:
						__A		=int(f.read())
				except ValueError:
						__A		=0

if cache_version < 1:
		__A		=os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
		if old_cache_is_not_empty:
				logger.warning(
				    'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
				    'existing cached models. This is a one-time operation, you can interrupt it or run it '
				    'later by calling `diffusers.utils.hub_utils.move_cache()`.'
				)
				try:
						move_cache()
				except Exception as e:
						__A		='\n'.join(traceback.format_tb(e.__traceback__))
						logger.error(
						    f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
						    'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
						    'message and we will do our best to help.'
						)

if cache_version < 1:
		try:
				os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
				with open(cache_version_file, 'w') as f:
						f.write('1')
		except Exception:
				logger.warning(
				    f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
				    'the directory exists and can be written to.'
				)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__ = None   ):
			if variant is not None:
						UpperCAmelCase__					:   int       				=       weights_name.split("""."""   )
						UpperCAmelCase__					:   str       				=       splits[:-1] + [variant] + splits[-1:]
						UpperCAmelCase__					:   Dict       				=       """.""".join(UpperCamelCase__   )

			return weights_name







def       _UpperCamelCase (						UpperCamelCase__	, *,
    UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__=None	, ):
			UpperCAmelCase__					:   List[str]       				=       str(UpperCamelCase__   )
			if os.path.isfile(UpperCamelCase__   ):
						return pretrained_model_name_or_path
			elif os.path.isdir(UpperCamelCase__   ):
						if os.path.isfile(os.path.join(UpperCamelCase__	, UpperCamelCase__   )   ):
									# Load from a PyTorch checkpoint
									UpperCAmelCase__					:   str       				=       os.path.join(UpperCamelCase__	, UpperCamelCase__   )
									return model_file
						elif subfolder is not None and os.path.isfile(
						    os.path.join(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )   ):
									UpperCAmelCase__					:   Dict       				=       os.path.join(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
									return model_file
						else:
									raise EnvironmentError(
									    f'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'''   )
			else:
						# 1. First check if deprecated way of loading from branches is used
						if (
						    revision in DEPRECATED_REVISION_ARGS
						    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
						    and version.parse(version.parse(UpperCamelCase__   ).base_version   ) >= version.parse("""0.20.0"""   )
						):
									try:
												UpperCAmelCase__					:   Optional[Any]       				=       hf_hub_download(
												    UpperCamelCase__	, filename=_add_variant(UpperCamelCase__	, UpperCamelCase__   )	, cache_dir=UpperCamelCase__	, force_download=UpperCamelCase__	, proxies=UpperCamelCase__	, resume_download=UpperCamelCase__	, local_files_only=UpperCamelCase__	, use_auth_token=UpperCamelCase__	, user_agent=UpperCamelCase__	, subfolder=UpperCamelCase__	, revision=revision or commit_hash	, )
												warnings.warn(
												    f'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'''	, UpperCamelCase__	, )
												return model_file
									except:  # noqa: E722
												warnings.warn(
												    f'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(UpperCamelCase__	, UpperCamelCase__   )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(UpperCamelCase__	, UpperCamelCase__   )}\' so that the correct variant file can be added.'''	, UpperCamelCase__	, )
						try:
									# 2. Load model file as usual
									UpperCAmelCase__					:   Optional[int]       				=       hf_hub_download(
									    UpperCamelCase__	, filename=UpperCamelCase__	, cache_dir=UpperCamelCase__	, force_download=UpperCamelCase__	, proxies=UpperCamelCase__	, resume_download=UpperCamelCase__	, local_files_only=UpperCamelCase__	, use_auth_token=UpperCamelCase__	, user_agent=UpperCamelCase__	, subfolder=UpperCamelCase__	, revision=revision or commit_hash	, )
									return model_file

						except RepositoryNotFoundError:
									raise EnvironmentError(
									    f'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
									    """listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
									    """token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
									    """login`."""   )
						except RevisionNotFoundError:
									raise EnvironmentError(
									    f'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
									    """this model name. Check the model page at """
									    f'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'''   )
						except EntryNotFoundError:
									raise EnvironmentError(
									    f'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'''   )
						except HTTPError as err:
									raise EnvironmentError(
									    f'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'''   )
						except ValueError:
									raise EnvironmentError(
									    f'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
									    f''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
									    f''' directory containing a file named {weights_name} or'''
									    """ \nCheckout your internet connection or see how to run the library in"""
									    """ offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."""   )
						except EnvironmentError:
									raise EnvironmentError(
									    f'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
									    """'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
									    f'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
									    f'''containing a file named {weights_name}'''   )



'''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 SPIECE_UNDERLINE, logging


__A		=logging.get_logger(__name__)

__A		={'vocab_file': 'spiece.model'}

__A		={
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}


class 						_snake_case   (       a__		):




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase=False							,							_lowerCamelCase=True							,							_lowerCamelCase=False							,							_lowerCamelCase="<s>"							,							_lowerCamelCase="</s>"							,							_lowerCamelCase="<unk>"							,							_lowerCamelCase="<sep>"							,							_lowerCamelCase="<pad>"							,							_lowerCamelCase="<cls>"							,							_lowerCamelCase="<mask>"							,							_lowerCamelCase=["<eop>", "<eod>"]							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Dict       				=       AddedToken(_lowerCamelCase							,							lstrip=_lowerCamelCase							,							rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else mask_token

								UpperCAmelCase__					:   Any       				=       {} if sp_model_kwargs is None else sp_model_kwargs

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

								UpperCAmelCase__					:   List[str]       				=       3

								UpperCAmelCase__					:   str       				=       do_lower_case
								UpperCAmelCase__					:   Tuple       				=       remove_space
								UpperCAmelCase__					:   List[str]       				=       keep_accents
								UpperCAmelCase__					:   Any       				=       vocab_file

								UpperCAmelCase__					:   str       				=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(_lowerCamelCase)

								try:
											import jieba
								except ModuleNotFoundError as error:
											raise error.__class__(
											    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
											    """See https://pypi.org/project/jieba/ for installation.""")
								UpperCAmelCase__					:   Union[str, Any]       				=       jieba
								UpperCAmelCase__					:   int       				=       str.maketrans(""" \n"""							,							"""\u2582\u2583""")




					@property
					# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
					def 			snake_case__  ( self):
								return len(self.sp_model)




					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[Any]       				=       {self.convert_ids_to_tokens(_lowerCamelCase): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab




					def __getstate__( self):
								UpperCAmelCase__					:   List[str]       				=       self.__dict__.copy()
								UpperCAmelCase__					:   Optional[int]       				=       None
								return state




					def __setstate__( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   Optional[Any]       				=       d

								# for backward compatibility
								if not hasattr(self							,							"""sp_model_kwargs"""):
											UpperCAmelCase__					:   Tuple       				=       {}

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




					def 			snake_case__  ( self							,							_lowerCamelCase):
								if self.remove_space:
											UpperCAmelCase__					:   Tuple       				=       """ """.join(inputs.strip().split())
								else:
											UpperCAmelCase__					:   str       				=       inputs
								UpperCAmelCase__					:   List[Any]       				=       outputs.replace("""``"""							,							"""\"""").replace("""''"""							,							"""\"""")

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

								return outputs




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

								return new_pieces




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.PieceToId(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								return self.sp_model.IdToPiece(_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase):
								UpperCAmelCase__					:   str       				=       """""".join(_lowerCamelCase).replace(_lowerCamelCase							,							""" """).strip()
								return out_string




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   Optional[Any]       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Optional[int]       				=       [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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = False):

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

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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__					:   int       				=       [self.sep_token_id]
								UpperCAmelCase__					:   Tuple       				=       [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 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								if not os.path.isdir(_lowerCamelCase):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
											return
								UpperCAmelCase__					:   str       				=       os.path.join(
								    _lowerCamelCase							,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

								if os.path.abspath(self.vocab_file) != os.path.abspath(_lowerCamelCase) and os.path.isfile(self.vocab_file):
											copyfile(self.vocab_file							,							_lowerCamelCase)
								elif not os.path.isfile(self.vocab_file):
											with open(_lowerCamelCase							,							"""wb""") as fi:
														UpperCAmelCase__					:   str       				=       self.sp_model.serialized_model_proto()
														fi.write(_lowerCamelCase)

								return (out_vocab_file,)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								UpperCAmelCase__					:   Union[str, Any]       				=       super()._decode(*_lowerCamelCase							,							**_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       text.replace(""" """							,							"""""").replace("""\u2582"""							,							""" """).replace("""\u2583"""							,							"""\n""")
								return text