





'''simple docstring'''






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__lowerCamelCase						: int		=   logging.get_logger(__name__)


__lowerCamelCase						: Union[str, Any]		=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase						: Optional[int]		=   {
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__lowerCamelCase						: Union[str, Any]		=   {
    'allenai/led-base-16384': 1_6384,
}







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				VOCAB_FILES_NAMES
  lowerCAmelCase_					=				PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_					=				LEDTokenizer
  lowerCAmelCase_					=				["""input_ids""", """attention_mask"""]


  def __init__(				self    :					Union[str, Any]							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					Tuple=None							,	UpperCamelCase__    :					Dict=None							,	UpperCamelCase__    :					Any="replace"							,	UpperCamelCase__    :					Any="<s>"							,	UpperCamelCase__    :					str="</s>"							,	UpperCamelCase__    :					Optional[int]="</s>"							,	UpperCamelCase__    :					str="<s>"							,	UpperCamelCase__    :					str="<unk>"							,	UpperCamelCase__    :					int="<pad>"							,	UpperCamelCase__    :					Optional[Any]="<mask>"							,	UpperCamelCase__    :					Optional[Any]=False							,	UpperCamelCase__    :					Any=True							,	**UpperCamelCase__    :					Optional[Any]							,	)     ->						Optional[int]:
    super().__init__(
        UpperCamelCase__							,	UpperCamelCase__							,	tokenizer_file=UpperCamelCase__							,	errors=UpperCamelCase__							,	bos_token=UpperCamelCase__							,	eos_token=UpperCamelCase__							,	sep_token=UpperCamelCase__							,	cls_token=UpperCamelCase__							,	unk_token=UpperCamelCase__							,	pad_token=UpperCamelCase__							,	mask_token=UpperCamelCase__							,	add_prefix_space=UpperCamelCase__							,	trim_offsets=UpperCamelCase__							,	**UpperCamelCase__							,	)

    _UpperCamelCase       			=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
    if pre_tok_state.get('''add_prefix_space'''							,	UpperCamelCase__  ) != add_prefix_space:
      _UpperCamelCase       			=getattr(UpperCamelCase__							,	pre_tok_state.pop('''type'''  )  )
      _UpperCamelCase       			=add_prefix_space
      _UpperCamelCase       			=pre_tok_class(**UpperCamelCase__  )

    _UpperCamelCase       			=add_prefix_space

    # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
    _UpperCamelCase       			='''post_processor'''
    _UpperCamelCase       			=getattr(self.backend_tokenizer							,	UpperCamelCase__							,	UpperCamelCase__  )
    if tokenizer_component_instance:
      _UpperCamelCase       			=json.loads(tokenizer_component_instance.__getstate__()  )

      # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
      if "sep" in state:
        _UpperCamelCase       			=tuple(state['''sep''']  )
      if "cls" in state:
        _UpperCamelCase       			=tuple(state['''cls''']  )

      _UpperCamelCase       			=False

      if state.get('''add_prefix_space'''							,	UpperCamelCase__  ) != add_prefix_space:
        _UpperCamelCase       			=add_prefix_space
        _UpperCamelCase       			=True

      if state.get('''trim_offsets'''							,	UpperCamelCase__  ) != trim_offsets:
        _UpperCamelCase       			=trim_offsets
        _UpperCamelCase       			=True

      if changes_to_apply:
        _UpperCamelCase       			=getattr(UpperCamelCase__							,	state.pop('''type'''  )  )
        _UpperCamelCase       			=component_class(**UpperCamelCase__  )
        setattr(self.backend_tokenizer							,	UpperCamelCase__							,	UpperCamelCase__  )


  @property
  # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						str:
    if self._mask_token is None:
      if self.verbose:
        logger.error('''Using mask_token, but it is not set yet.'''  )
      return None
    return str(self._mask_token  )


  @mask_token.setter
  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					int  )     ->						List[str]:
    _UpperCamelCase       			=AddedToken(UpperCamelCase__							,	lstrip=UpperCamelCase__							,	rstrip=UpperCamelCase__  ) if isinstance(UpperCamelCase__							,	UpperCamelCase__  ) else value
    _UpperCamelCase       			=value


  def   UpperCamelCase__      (				self    :					Optional[Any]							,	*UpperCamelCase__    :					Optional[Any]							,	**UpperCamelCase__    :					Any  )     ->						BatchEncoding:
    _UpperCamelCase       			=kwargs.get('''is_split_into_words'''							,	UpperCamelCase__  )

    if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          '''to use it with pretokenized inputs.'''  )

    return super()._batch_encode_plus(*UpperCamelCase__							,	**UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Any							,	*UpperCamelCase__    :					Optional[int]							,	**UpperCamelCase__    :					Any  )     ->						BatchEncoding:
    _UpperCamelCase       			=kwargs.get('''is_split_into_words'''							,	UpperCamelCase__  )

    if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          '''to use it with pretokenized inputs.'''  )

    return super()._encode_plus(*UpperCamelCase__							,	**UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					Optional[str] = None  )     ->						Tuple[str]:
    _UpperCamelCase       			=self._tokenizer.model.save(UpperCamelCase__							,	name=UpperCamelCase__  )
    return tuple(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Tuple=None  )     ->						int:
    _UpperCamelCase       			=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
      return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					List[int]							,	UpperCamelCase__    :					Optional[List[int]] = 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    :					Tuple							,	UpperCamelCase__    :					Union[Dict[str, EncodedInput], BatchEncoding]							,	UpperCamelCase__    :					Optional[int] = None							,	UpperCamelCase__    :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,	UpperCamelCase__    :					Optional[int] = None							,	UpperCamelCase__    :					Optional[bool] = None							,	)     ->						dict:
    _UpperCamelCase       			=super()._pad(
        encoded_inputs=UpperCamelCase__							,	max_length=UpperCamelCase__							,	padding_strategy=UpperCamelCase__							,	pad_to_multiple_of=UpperCamelCase__							,	return_attention_mask=UpperCamelCase__							,	)

    # Load from model defaults
    if return_attention_mask is None:
      _UpperCamelCase       			='''attention_mask''' in self.model_input_names

    if return_attention_mask and "global_attention_mask" in encoded_inputs:
      _UpperCamelCase       			=encoded_inputs[self.model_input_names[0]]
      # `global_attention_mask` need to have the same length as other (sequential) inputs.
      _UpperCamelCase       			=len(encoded_inputs['''global_attention_mask''']  ) != len(UpperCamelCase__  )

      if needs_to_be_padded:
        _UpperCamelCase       			=len(UpperCamelCase__  ) - len(encoded_inputs['''global_attention_mask''']  )

        if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          _UpperCamelCase       			=(
              encoded_inputs['''global_attention_mask'''] + [-1] * difference
          )
        elif self.padding_side == "left":
          _UpperCamelCase       			=[-1] * difference + encoded_inputs[
              '''global_attention_mask'''
          ]
        else:
          raise ValueError('''Invalid padding strategy:''' + str(self.padding_side  )  )

    return encoded_inputs













'''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 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand()


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand(args.accelerate_config_file   )







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					ArgumentParser  )     ->						Union[str, Any]:
    _UpperCamelCase       			=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    :					Tuple							,	UpperCamelCase__    :					Dict							,	*UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=accelerate_config_file


  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			='''not installed'''
    if is_safetensors_available():
      import safetensors

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

      _UpperCamelCase       			=F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

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

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

      _UpperCamelCase       			=(
          '''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
          if isinstance(UpperCamelCase__							,	UpperCamelCase__  )
          else F'''\t{accelerate_config}'''
      )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_torch_available():
      import torch

      _UpperCamelCase       			=torch.__version__
      _UpperCamelCase       			=torch.cuda.is_available()

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_tf_available():
      import tensorflow as tf

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

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_flax_available():
      import flax
      import jax
      import jaxlib

      _UpperCamelCase       			=flax.__version__
      _UpperCamelCase       			=jax.__version__
      _UpperCamelCase       			=jaxlib.__version__
      _UpperCamelCase       			=jax.lib.xla_bridge.get_backend().platform

    _UpperCamelCase       			={
        '''`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   UpperCamelCase__      (				UpperCamelCase__    :					Optional[Any]  )     ->						Dict:
    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]  ) + "\n"






