





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


snake_case								=							get_logger(__name__)


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

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str = None  )   ->    int:
  _lowercase						    =			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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:str = None      ,				snake_case__	:Optional[Any] = None  )   ->    List[str]:
  if token is None:
    _lowercase						    =			HfFolder.get_token()
  if organization is None:
    _lowercase						    =			whoami(UpperCAmelCase_  )['name']
    return F"""{username}/{model_id}"""
  else:
    return F"""{organization}/{model_id}"""

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:List[Any]  )   ->    Optional[int]:
  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

  _lowercase						    =			args.hub_token if hasattr(UpperCAmelCase_      ,				'hub_token'  ) else None
  _lowercase						    =			get_full_repo_name(UpperCAmelCase_      ,				token=UpperCAmelCase_  )

  _lowercase						    =			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      ,				)

  _lowercase						    =			os.path.join(args.output_dir      ,				'README.md'  )
  model_card.save(UpperCAmelCase_  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:List[str] = None  )   ->    Dict:
  if resolved_file is None or commit_hash is not None:
    return commit_hash
  _lowercase						    =			str(Path(UpperCAmelCase_  ).as_posix()  )
  _lowercase						    =			re.search(R'snapshots/([^/]+)/'      ,				UpperCAmelCase_  )
  if search is None:
    return None
  _lowercase						    =			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.
snake_case								=							os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
snake_case								=							os.path.join(hf_cache_home, """diffusers""")

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any = None      ,				snake_case__	:int = None  )   ->    Dict:
  if new_cache_dir is None:
    _lowercase						    =			DIFFUSERS_CACHE
  if old_cache_dir is None:
    _lowercase						    =			old_diffusers_cache

  _lowercase						    =			Path(UpperCAmelCase_  ).expanduser()
  _lowercase						    =			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():
      _lowercase						    =			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).


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

if cache_version < 1:
      snake_case								=							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:
                  snake_case								=							'\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 				SCREAMING_SNAKE_CASE__							(	snake_case__	:str      ,				snake_case__	:Optional[Any] = None  )   ->    Union[str, Any]:
  if variant is not None:
    _lowercase						    =			weights_name.split('.'  )
    _lowercase						    =			splits[:-1] + [variant] + splits[-1:]
    _lowercase						    =			'.'.join(UpperCAmelCase_  )

  return weights_name







def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Union[str, Any]      ,				*,
    snake_case__	:str      ,				snake_case__	:List[str]      ,				snake_case__	:Any      ,				snake_case__	:Any      ,				snake_case__	:Union[str, Any]      ,				snake_case__	:Tuple      ,				snake_case__	:Optional[int]      ,				snake_case__	:str      ,				snake_case__	:str      ,				snake_case__	:List[str]      ,				snake_case__	:Union[str, Any]=None      ,				)   ->    str:
  _lowercase						    =			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
      _lowercase						    =			os.path.join(UpperCAmelCase_      ,				UpperCAmelCase_  )
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(UpperCAmelCase_      ,				UpperCAmelCase_      ,				UpperCAmelCase_  )  ):
      _lowercase						    =			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:
        _lowercase						    =			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
      _lowercase						    =			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 json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


snake_case_					:      Union[str, Any]    			=    logging.get_logger(__name__)

snake_case_					:      Tuple    			=    '▁'

snake_case_					:      List[str]    			=    {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case_					:      Tuple    			=    {
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

snake_case_					:      Union[str, Any]    			=    {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
snake_case_					:      List[Any]    			=    {
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     VOCAB_FILES_NAMES
	lowercase__    							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__    							=     PRETRAINED_VOCAB_FILES_MAP
	lowercase__    							=     ["""input_ids""", """attention_mask"""]

	lowercase__    							=     []
	lowercase__    							=     []





	def __init__(					self   :							Union[str, Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Dict=None					,lowerCamelCase__   :							Optional[int]=None					,lowerCamelCase__   :							Optional[int]="<s>"					,lowerCamelCase__   :							Optional[int]="</s>"					,lowerCamelCase__   :							str="</s>"					,lowerCamelCase__   :							Union[str, Any]="<pad>"					,lowerCamelCase__   :							Optional[int]="<unk>"					,lowerCamelCase__   :							str="m2m100"					,lowerCamelCase__   :							Optional[Dict[str, Any]] = None					,lowerCamelCase__   :							Optional[Any]=8					,**lowerCamelCase__   :							str					,):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  {} if sp_model_kwargs is None else sp_model_kwargs

							_UpperCamelCase   :						Optional[int]												=  language_codes
							_UpperCamelCase   :						Union[str, Any]												=  FAIRSEQ_LANGUAGE_CODES[language_codes]
							_UpperCamelCase   :						str												=  {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

							_UpperCamelCase   :						Optional[Any]												=  kwargs.get('additional_special_tokens'					,[]					)
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(lowerCamelCase__					)
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(lowerCamelCase__					) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=lowerCamelCase__					,tgt_lang=lowerCamelCase__					,bos_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,sep_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,language_codes=lowerCamelCase__					,sp_model_kwargs=self.sp_model_kwargs					,num_madeup_words=lowerCamelCase__					,**lowerCamelCase__					,)

							_UpperCamelCase   :						Any												=  vocab_file
							_UpperCamelCase   :						int												=  load_json(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  {v: k for k, v in self.encoder.items()}
							_UpperCamelCase   :						Any												=  spm_file
							_UpperCamelCase   :						Optional[Any]												=  load_spm(lowerCamelCase__					,self.sp_model_kwargs					)

							_UpperCamelCase   :						Union[str, Any]												=  len(self.encoder					)

							_UpperCamelCase   :						Dict												=  {
							    self.get_lang_token(lowerCamelCase__					): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__					)
							}
							_UpperCamelCase   :						Any												=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__					)}
							_UpperCamelCase   :						Dict												=  {v: k for k, v in self.lang_token_to_id.items()}

							_UpperCamelCase   :						Any												=  src_lang if src_lang is not None else 'en'
							_UpperCamelCase   :						int												=  tgt_lang
							_UpperCamelCase   :						Optional[int]												=  self.get_lang_id(self._src_lang					)
							self.set_src_lang_special_tokens(self._src_lang					)

							_UpperCamelCase   :						List[Any]												=  num_madeup_words





	@property
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							return len(self.encoder					) + len(self.lang_token_to_id					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self._src_lang





	@src_lang.setter
	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							return self.sp_model.encode(lowerCamelCase__					,out_type=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							if token in self.lang_token_to_id:
													return self.lang_token_to_id[token]
							return self.encoder.get(lowerCamelCase__					,self.encoder[self.unk_token]					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							if index in self.id_to_lang_token:
													return self.id_to_lang_token[index]
							return self.decoder.get(lowerCamelCase__					,self.unk_token					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  []
							_UpperCamelCase   :						Union[str, Any]												=  ''
							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(lowerCamelCase__					) + token
																			_UpperCamelCase   :						Optional[int]												=  []
													else:
																			current_sub_tokens.append(lowerCamelCase__					)
							out_string += self.sp_model.decode(lowerCamelCase__					)
							return out_string.strip()





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							List[int]					,lowerCamelCase__   :							Optional[List[int]] = None					,lowerCamelCase__   :							bool = False					):







							'''simple docstring'''

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

							_UpperCamelCase   :						Tuple												=  [1] * len(self.prefix_tokens					)
							_UpperCamelCase   :						str												=  [1] * len(self.suffix_tokens					)
							if token_ids_a is None:
													return prefix_ones + ([0] * len(lowerCamelCase__					)) + suffix_ones
							return prefix_ones + ([0] * len(lowerCamelCase__					)) + ([0] * len(lowerCamelCase__					)) + suffix_ones





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							List[int]					,lowerCamelCase__   :							Optional[List[int]] = None					):







							'''simple docstring'''
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						List[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   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  self.__dict__.copy()
							_UpperCamelCase   :						int												=  None
							return state





	def __setstate__(					self   :							Optional[int]					,lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  d

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

							_UpperCamelCase   :						Dict												=  load_spm(self.spm_file					,self.sp_model_kwargs					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[str] = None					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  Path(lowerCamelCase__					)
							if not save_dir.is_dir():
													raise OSError(F'{save_directory} should be a directory'					)
							_UpperCamelCase   :						List[str]												=  save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
							)
							_UpperCamelCase   :						Optional[Any]												=  save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
							)

							save_json(self.encoder					,lowerCamelCase__					)

							if os.path.abspath(self.spm_file					) != os.path.abspath(lowerCamelCase__					) and os.path.isfile(self.spm_file					):
													copyfile(self.spm_file					,lowerCamelCase__					)
							elif not os.path.isfile(self.spm_file					):
													with open(lowerCamelCase__					,'wb'					) as fi:
																			_UpperCamelCase   :						Union[str, Any]												=  self.sp_model.serialized_model_proto()
																			fi.write(lowerCamelCase__					)

							return (str(lowerCamelCase__					), str(lowerCamelCase__					))





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							str = "en"					,lowerCamelCase__   :							Optional[List[str]] = None					,lowerCamelCase__   :							str = "ro"					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  src_lang
							_UpperCamelCase   :						Any												=  tgt_lang
							self.set_src_lang_special_tokens(self.src_lang					)
							return super().prepare_seqaseq_batch(lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[str]					,lowerCamelCase__   :							Optional[str]					,**lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
							_UpperCamelCase   :						int												=  src_lang
							_UpperCamelCase   :						Any												=  self(lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,**lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  self.get_lang_id(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  tgt_lang_id
							return inputs





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							self.set_src_lang_special_tokens(self.src_lang					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							self.set_tgt_lang_special_tokens(self.tgt_lang					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  self.get_lang_token(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  self.lang_token_to_id[lang_token]
							_UpperCamelCase   :						Any												=  [self.cur_lang_id]
							_UpperCamelCase   :						Union[str, Any]												=  [self.eos_token_id]





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.get_lang_token(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  self.lang_token_to_id[lang_token]
							_UpperCamelCase   :						Union[str, Any]												=  [self.cur_lang_id]
							_UpperCamelCase   :						List[str]												=  [self.eos_token_id]





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							return self.lang_code_to_token[lang]





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  self.get_lang_token(lowerCamelCase__					)
							return self.lang_token_to_id[lang_token]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Union[str, Any]												=  sentencepiece.SentencePieceProcessor(**UpperCAmelCase_				)
						spm.Load(str(UpperCAmelCase_				)				)
						return spm






def A__       (	UpperCAmelCase_				):
						with open(UpperCAmelCase_    ,    'r'				) as f:
												return json.load(UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						with open(UpperCAmelCase_    ,    'w'				) as f:
												json.dump(UpperCAmelCase_    ,    UpperCAmelCase_    ,    indent=2				)


