
"""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


UpperCAmelCase__        =					get_logger(__name__)


UpperCAmelCase__        =					Path(__file__).parent / """model_card_template.md"""
UpperCAmelCase__        =					uuida().hex
UpperCAmelCase__        =					os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__        =					os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__        =					HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def    _UpperCAmelCase (					__lowerCamelCase  :			str = None						)	->   str:
  _snake_case   	=			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(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						):
    ua += "; " + "; ".join(f'''{k}/{v}''' for k, v in user_agent.items()						)
  elif isinstance(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						):
    ua += "; " + user_agent
  return ua
def    _UpperCAmelCase (					__lowerCamelCase  :			Optional[Any]					,   __lowerCamelCase  :			Dict = None					,   __lowerCamelCase  :			Any = None						)	->   List[str]:






  if token is None:
    _snake_case   	=			HfFolder.get_token()
  if organization is None:
    _snake_case   	=			whoami(SCREAMING_SNAKE_CASE_						)['''name''']
    return f'''{username}/{model_id}'''
  else:
    return f'''{organization}/{model_id}'''
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			List[str]						)	->   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(SCREAMING_SNAKE_CASE_					,   '''local_rank'''						) and args.local_rank not in [-1, 0]:
    return

  _snake_case   	=			args.hub_token if hasattr(SCREAMING_SNAKE_CASE_					,   '''hub_token'''						) else None
  _snake_case   	=			get_full_repo_name(SCREAMING_SNAKE_CASE_					,   token=SCREAMING_SNAKE_CASE_						)

  _snake_case   	=			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=SCREAMING_SNAKE_CASE_					,   model_name=SCREAMING_SNAKE_CASE_					,   repo_name=SCREAMING_SNAKE_CASE_					,   dataset_name=args.dataset_name if hasattr(SCREAMING_SNAKE_CASE_					,   '''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(SCREAMING_SNAKE_CASE_					,   '''gradient_accumulation_steps'''						) else None
      )					,   adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_					,   '''adam_beta1'''						) else None					,   adam_betaa=args.adam_betaa if hasattr(SCREAMING_SNAKE_CASE_					,   '''adam_beta2'''						) else None					,   adam_weight_decay=args.adam_weight_decay if hasattr(SCREAMING_SNAKE_CASE_					,   '''adam_weight_decay'''						) else None					,   adam_epsilon=args.adam_epsilon if hasattr(SCREAMING_SNAKE_CASE_					,   '''adam_epsilon'''						) else None					,   lr_scheduler=args.lr_scheduler if hasattr(SCREAMING_SNAKE_CASE_					,   '''lr_scheduler'''						) else None					,   lr_warmup_steps=args.lr_warmup_steps if hasattr(SCREAMING_SNAKE_CASE_					,   '''lr_warmup_steps'''						) else None					,   ema_inv_gamma=args.ema_inv_gamma if hasattr(SCREAMING_SNAKE_CASE_					,   '''ema_inv_gamma'''						) else None					,   ema_power=args.ema_power if hasattr(SCREAMING_SNAKE_CASE_					,   '''ema_power'''						) else None					,   ema_max_decay=args.ema_max_decay if hasattr(SCREAMING_SNAKE_CASE_					,   '''ema_max_decay'''						) else None					,   mixed_precision=args.mixed_precision					,   )

  _snake_case   	=			os.path.join(args.output_dir					,   '''README.md'''						)
  model_card.save(SCREAMING_SNAKE_CASE_						)
def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   __lowerCamelCase  :			str = None						)	->   Optional[Any]:






  if resolved_file is None or commit_hash is not None:
    return commit_hash
  _snake_case   	=			str(Path(SCREAMING_SNAKE_CASE_						).as_posix()						)
  _snake_case   	=			re.search(R'''snapshots/([^/]+)/'''					,   SCREAMING_SNAKE_CASE_						)
  if search is None:
    return None
  _snake_case   	=			search.groups()[0]
  return commit_hash if REGEX_COMMIT_HASH.match(SCREAMING_SNAKE_CASE_						) 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.
UpperCAmelCase__        =					os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCAmelCase__        =					os.path.join(hf_cache_home, 'diffusers')
def    _UpperCAmelCase (					__lowerCamelCase  :			Dict = None					,   __lowerCamelCase  :			int = None						)	->   None:






  if new_cache_dir is None:
    _snake_case   	=			DIFFUSERS_CACHE
  if old_cache_dir is None:
    _snake_case   	=			old_diffusers_cache

  _snake_case   	=			Path(SCREAMING_SNAKE_CASE_						).expanduser()
  _snake_case   	=			Path(SCREAMING_SNAKE_CASE_						).expanduser()
  for old_blob_path in old_cache_dir.glob('''**/blobs/*'''						):
    if old_blob_path.is_file() and not old_blob_path.is_symlink():
      _snake_case   	=			new_cache_dir / old_blob_path.relative_to(SCREAMING_SNAKE_CASE_						)
      new_blob_path.parent.mkdir(parents=SCREAMING_SNAKE_CASE_					,   exist_ok=SCREAMING_SNAKE_CASE_						)
      os.replace(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)
      try:
        os.symlink(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)
      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).


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

if cache_version < 1:
 UpperCAmelCase__        =					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:
   UpperCAmelCase__        =					"""\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 (					__lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			List[str] = None						)	->   str:






  if variant is not None:
    _snake_case   	=			weights_name.split('''.'''						)
    _snake_case   	=			splits[:-1] + [variant] + splits[-1:]
    _snake_case   	=			'''.'''.join(SCREAMING_SNAKE_CASE_						)

  return weights_name

def    _UpperCAmelCase (					__lowerCamelCase  :			str					,   *,
    __lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			Optional[Any]					,   __lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			Union[str, Any]					,   __lowerCamelCase  :			Optional[Any]					,   __lowerCamelCase  :			int					,   __lowerCamelCase  :			List[Any]					,   __lowerCamelCase  :			int					,   __lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			Optional[int]					,   __lowerCamelCase  :			Any=None					,   )	->   Any:
  _snake_case   	=			str(SCREAMING_SNAKE_CASE_						)
  if os.path.isfile(SCREAMING_SNAKE_CASE_						):
    return pretrained_model_name_or_path
  elif os.path.isdir(SCREAMING_SNAKE_CASE_						):
    if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)						):
      # Load from a PyTorch checkpoint
      _snake_case   	=			os.path.join(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)
      return model_file
    elif subfolder is not None and os.path.isfile(
        os.path.join(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)						):
      _snake_case   	=			os.path.join(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)
      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(SCREAMING_SNAKE_CASE_						).base_version						) >= version.parse('''0.20.0'''						)
    ):
      try:
        _snake_case   	=			hf_hub_download(
            SCREAMING_SNAKE_CASE_					,   filename=_add_variant(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)					,   cache_dir=SCREAMING_SNAKE_CASE_					,   force_download=SCREAMING_SNAKE_CASE_					,   proxies=SCREAMING_SNAKE_CASE_					,   resume_download=SCREAMING_SNAKE_CASE_					,   local_files_only=SCREAMING_SNAKE_CASE_					,   use_auth_token=SCREAMING_SNAKE_CASE_					,   user_agent=SCREAMING_SNAKE_CASE_					,   subfolder=SCREAMING_SNAKE_CASE_					,   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.'''					,   SCREAMING_SNAKE_CASE_					,   )
        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(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)} 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(SCREAMING_SNAKE_CASE_					,   SCREAMING_SNAKE_CASE_						)}\' so that the correct variant file can be added.'''					,   SCREAMING_SNAKE_CASE_					,   )
    try:
      # 2. Load model file as usual
      _snake_case   	=			hf_hub_download(
          SCREAMING_SNAKE_CASE_					,   filename=SCREAMING_SNAKE_CASE_					,   cache_dir=SCREAMING_SNAKE_CASE_					,   force_download=SCREAMING_SNAKE_CASE_					,   proxies=SCREAMING_SNAKE_CASE_					,   resume_download=SCREAMING_SNAKE_CASE_					,   local_files_only=SCREAMING_SNAKE_CASE_					,   use_auth_token=SCREAMING_SNAKE_CASE_					,   user_agent=SCREAMING_SNAKE_CASE_					,   subfolder=SCREAMING_SNAKE_CASE_					,   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}'''						)






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_barthez import BarthezTokenizer
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     None

SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Optional[Any]						=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE		:int						=     {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE		:List[str]						=     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

SCREAMING_SNAKE_CASE		:Optional[Any]						=     """▁"""






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      VOCAB_FILES_NAMES
     UpperCamelCase_	:Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_	:Any										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_	:Any										=      ["""input_ids""", """attention_mask"""]
     UpperCamelCase_	:List[Any]										=      BarthezTokenizer






     def __init__(				self		,						_lowercase=None		,						_lowercase=None		,						_lowercase="<s>"		,						_lowercase="</s>"		,						_lowercase="</s>"		,						_lowercase="<s>"		,						_lowercase="<unk>"		,						_lowercase="<pad>"		,						_lowercase="<mask>"		,						**_lowercase		,						)->     Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_			  =						AddedToken(_lowercase		,						lstrip=_lowercase		,						rstrip=_lowercase	) if isinstance(_lowercase		,						_lowercase	) else mask_token

         super().__init__(
             _lowercase		,						tokenizer_file=_lowercase		,						bos_token=_lowercase		,						eos_token=_lowercase		,						unk_token=_lowercase		,						sep_token=_lowercase		,						cls_token=_lowercase		,						pad_token=_lowercase		,						mask_token=_lowercase		,						**_lowercase		,						)

         UpperCamelCase_			  =						vocab_file
         UpperCamelCase_			  =						False if not self.vocab_file else True






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:

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






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     List[int]:
         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 + sep + token_ids_a + sep	) * [0]






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase = None	)->     Tuple[str]:
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                 "tokenizer."	)

         if not os.path.isdir(_lowercase	):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
             return
         UpperCamelCase_			  =						os.path.join(
             _lowercase		,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(_lowercase	):
             copyfile(self.vocab_file		,						_lowercase	)

         return (out_vocab_file,)
