




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_					    = logging.get_logger(__name__)

lowerCAmelCase_					    = {"""vocab_file""": """sentencepiece.model"""}

lowerCAmelCase_					    = {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCAmelCase_					    = {
    """google/rembert""": 2_56,
}






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		VOCAB_FILES_NAMES
      A__		     =		PRETRAINED_VOCAB_FILES_MAP
      A__		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase="[CLS]"      ,				__UpperCAmelCase="[SEP]"      ,				__UpperCAmelCase="[UNK]"      ,				__UpperCAmelCase="[SEP]"      ,				__UpperCAmelCase="[PAD]"      ,				__UpperCAmelCase="[CLS]"      ,				__UpperCAmelCase="[MASK]"      ,				**__UpperCAmelCase      ,				):
        super().__init__(
            do_lower_case=__UpperCAmelCase      ,				remove_space=__UpperCAmelCase      ,				keep_accents=__UpperCAmelCase      ,				bos_token=__UpperCAmelCase      ,				eos_token=__UpperCAmelCase      ,				unk_token=__UpperCAmelCase      ,				sep_token=__UpperCAmelCase      ,				pad_token=__UpperCAmelCase      ,				cls_token=__UpperCAmelCase      ,				mask_token=__UpperCAmelCase      ,				**__UpperCAmelCase      ,				)

        lowerCAmelCase__				:  Any		       =     do_lower_case
        lowerCAmelCase__				:  int		       =     remove_space
        lowerCAmelCase__				:  Optional[Any]		       =     keep_accents
        lowerCAmelCase__				:  Optional[Any]		       =     vocab_file

        lowerCAmelCase__				:  Any		       =     spm.SentencePieceProcessor()
        self.sp_model.Load(__UpperCAmelCase		)

      @property
      def 	__magic_name__(   self		):
        return len(self.sp_model		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     {self.convert_ids_to_tokens(__UpperCAmelCase		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab

      def __getstate__(   self		):
        lowerCAmelCase__				:  Any		       =     self.__dict__.copy()
        lowerCAmelCase__				:  Dict		       =     None
        return state

      def __setstate__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     d
        lowerCAmelCase__				:  List[str]		       =     spm.SentencePieceProcessor()
        self.sp_model.Load(self.vocab_file		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False		):
        lowerCAmelCase__				:  List[Any]		       =     self.sp_model.EncodeAsPieces(__UpperCAmelCase		)
        return pieces

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return self.sp_model.PieceToId(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return self.sp_model.IdToPiece(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     self.sp_model.decode_pieces(__UpperCAmelCase		)
        return out_string

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None		):
        lowerCAmelCase__				:  int		       =     [self.sep_token_id]
        lowerCAmelCase__				:  List[Any]		       =     [self.cls_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = False		):

        if already_has_special_tokens:
          if token_ids_a is not None:
            raise ValueError(
                '''You should not supply a second sequence if the provided sequence of '''
                '''ids is already formatted with special tokens for the model.'''		)
          return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None		):
        lowerCAmelCase__				:  Tuple		       =     [self.sep_token_id]
        lowerCAmelCase__				:  int		       =     [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		) * [0] + len(token_ids_a + sep		) * [1]







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None		):
        if not os.path.isdir(__UpperCAmelCase		):
          logger.error('''Vocabulary path ({}) should be a directory'''.format(__UpperCAmelCase		)		)
          return
        lowerCAmelCase__				:  List[Any]		       =     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,)







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        if isinstance(__UpperCAmelCase      ,				__UpperCAmelCase		):
          # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
          # modified it, it will not be accepted here again, since `auto` values would have been overridden
          lowerCAmelCase__				:  Union[str, Any]		       =     deepcopy(__UpperCAmelCase		)
        elif os.path.exists(__UpperCAmelCase		):
          with io.open(__UpperCAmelCase      ,				'''r'''      ,				encoding='''utf-8'''		) as f:
            lowerCAmelCase__				:  int		       =     json.load(__UpperCAmelCase		)
        else:
          try:
            lowerCAmelCase__				:  Union[str, Any]		       =     baseaa.urlsafe_baadecode(__UpperCAmelCase		).decode('''utf-8'''		)
            lowerCAmelCase__				:  Optional[int]		       =     json.loads(__UpperCAmelCase		)
          except (UnicodeDecodeError, AttributeError, ValueError):
            raise ValueError(
                f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""		)

        lowerCAmelCase__				:  Optional[Any]		       =     config

        self.set_stage_and_offload()

      def 	__magic_name__(   self		):
        # zero stage - this is done as early as possible, before model is created, to allow
        # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
        # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value('''zero_optimization.stage'''      ,				-1		)

        # offload
        lowerCAmelCase__				:  Dict		       =     False
        if self.is_zeroa() or self.is_zeroa():
          lowerCAmelCase__				:  Dict		       =     set(['''cpu''', '''nvme''']		)
          lowerCAmelCase__				:  Optional[Any]		       =     set(
              [
                  self.get_value('''zero_optimization.offload_optimizer.device'''		),
                  self.get_value('''zero_optimization.offload_param.device'''		),
              ]		)
          if len(offload_devices & offload_devices_valid		) > 0:
            lowerCAmelCase__				:  List[Any]		       =     True

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  Tuple		       =     ds_key_long.split('''.'''		)
        lowerCAmelCase__				:  int		       =     nodes.pop()
        for node in nodes:
          lowerCAmelCase__				:  int		       =     config.get(__UpperCAmelCase		)
          if config is None:
            return None, ds_key

        return config, ds_key

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Optional[int]		       =     self.find_config_node(__UpperCAmelCase		)
        if config is None:
          return default
        return config.get(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=False		):
        lowerCAmelCase__				:  Dict		       =     self.config

        # find the config node of interest if it exists
        lowerCAmelCase__				:  str		       =     ds_key_long.split('''.'''		)
        for node in nodes:
          lowerCAmelCase__				:  Dict		       =     config
          lowerCAmelCase__				:  List[str]		       =     config.get(__UpperCAmelCase		)
          if config is None:
            if must_exist:
              raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""		)
            else:
              return

        # if found remove it
        if parent_config is not None:
          parent_config.pop(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else bool(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     self.get_value(__UpperCAmelCase		)
        return False if value is None else not bool(__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        return self._stage == 2

      def 	__magic_name__(   self		):
        return self._stage == 3







      def 	__magic_name__(   self		):
        return self._offload






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  int		       =     engine







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		):
        # runs backpropagation and handles mixed precision
        self.engine.backward(__UpperCAmelCase      ,				**__UpperCAmelCase		)

        # Deepspeed's `engine.step` performs the following operations:
        # - gradient accumulation check
        # - gradient clipping
        # - optimizer step
        # - zero grad
        # - checking overflow
        # - lr_scheduler step (only if engine.lr_scheduler is not None)
        self.engine.step()
        # and this plugin overrides the above calls with no-ops when Accelerate runs under
        # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
        # training loop that works transparently under many training regimes.






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				device_placement=__UpperCAmelCase      ,				scaler=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     hasattr(self.optimizer      ,				'''overflow'''		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase=None		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







      @property
      def 	__magic_name__(   self		):
        if self.__has_overflow__:
          return self.optimizer.overflow
        return False






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        super().__init__(__UpperCAmelCase      ,				__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=0.001      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     params
        lowerCAmelCase__				:  str		       =     lr
        lowerCAmelCase__				:  List[str]		       =     weight_decay
        lowerCAmelCase__				:  Dict		       =     kwargs






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=0      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     optimizer
        lowerCAmelCase__				:  Optional[Any]		       =     total_num_steps
        lowerCAmelCase__				:  str		       =     warmup_num_steps
        lowerCAmelCase__				:  Dict		       =     kwargs

