





'''simple docstring'''






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:
     snake_case_     =	None

snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case_     =	{
    """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"""
        ),
    },
}

snake_case_     =	{
    """moussaKam/mbarthez""": 10_24,
    """moussaKam/barthez""": 10_24,
    """moussaKam/barthez-orangesum-title""": 10_24,
}

snake_case_     =	"""▁"""


class   _lowercase						(							a				):
   _UpperCamelCase       		=   VOCAB_FILES_NAMES
   _UpperCamelCase       		=   PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase       		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase       		=   ["""input_ids""", """attention_mask"""]
   _UpperCamelCase       		=   BarthezTokenizer


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

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

       A       :       Optional[int]							=						vocab_file
       A       :       Any							=						False if not self.vocab_file else True


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):

       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A       :       Union[str, Any]							=						[self.cls_token_id]
       A       :       List[str]							=						[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       int							=						[self.sep_token_id]
       A       :       List[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 + sep + token_ids_a + sep					) * [0]


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       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(_UpperCAmelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
       A       :       Dict							=						os.path.join(
           _UpperCAmelCase    ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

       return (out_vocab_file,)







'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  List[str]:
    return EnvironmentCommand()
def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]		)		->  Tuple:
    return EnvironmentCommand(args.accelerate_config_file		)





class   _lowercase						(							a				):


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       A       :       Optional[Any]							=						parser.add_parser('''env'''					)
       download_parser.set_defaults(func=_UpperCAmelCase					)
       download_parser.add_argument(
           '''--accelerate-config_file'''    ,   default=_UpperCAmelCase    ,   help='''The accelerate config file to use for the default values in the launching script.'''    ,   )
       download_parser.set_defaults(func=_UpperCAmelCase					)


   def __init__( self    ,   _UpperCAmelCase    ,   *_UpperCAmelCase					):
       A       :       Any							=						accelerate_config_file


   def 					snake_case				( self					):
       A       :       Any							=						'''not installed'''
       if is_safetensors_available():
           import safetensors

           A       :       Dict							=						safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''					) is not None:
           import safetensors

           A       :       int							=						f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

       A       :       Tuple							=						'''not installed'''
       A       :       Tuple							=						'''not found'''
       if is_accelerate_available():
           import accelerate
           from accelerate.commands.config import default_config_file, load_config_from_file

           A       :       Any							=						accelerate.__version__
           # Get the default from the config file.
           if self._accelerate_config_file is not None or os.path.isfile(_UpperCAmelCase					):
               A       :       int							=						load_config_from_file(self._accelerate_config_file					).to_dict()

           A       :       Tuple							=						(
               '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]					)
               if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)
               else f'''\t{accelerate_config}'''
           )

       A       :       str							=						'''not installed'''
       A       :       Optional[Any]							=						'''NA'''
       if is_torch_available():
           import torch

           A       :       Optional[int]							=						torch.__version__
           A       :       List[Any]							=						torch.cuda.is_available()

       A       :       Dict							=						'''not installed'''
       A       :       Any							=						'''NA'''
       if is_tf_available():
           import tensorflow as tf

           A       :       Optional[int]							=						tf.__version__
           try:
               # deprecated in v2.1
               A       :       int							=						tf.test.is_gpu_available()
           except AttributeError:
               # returns list of devices, convert to bool
               A       :       Optional[int]							=						bool(tf.config.list_physical_devices('''GPU'''					)					)

       A       :       str							=						'''not installed'''
       A       :       Dict							=						'''not installed'''
       A       :       int							=						'''not installed'''
       A       :       Tuple							=						'''NA'''
       if is_flax_available():
           import flax
           import jax
           import jaxlib

           A       :       Any							=						flax.__version__
           A       :       Tuple							=						jax.__version__
           A       :       int							=						jaxlib.__version__
           A       :       List[Any]							=						jax.lib.xla_bridge.get_backend().platform

       A       :       Union[str, Any]							=						{
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': f'''{safetensors_version}''',
           '''Accelerate version''': f'''{accelerate_version}''',
           '''Accelerate config''': f'''{accelerate_config_str}''',
           '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
           '''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
           '''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
           '''Jax version''': f'''{jax_version}''',
           '''JaxLib version''': f'''{jaxlib_version}''',
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''					)
       print(self.format_dict(_UpperCAmelCase					)					)

       return info


   @staticmethod
   def 					snake_case				( _UpperCAmelCase					):
       return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"
