

import torch

from accelerate import Accelerator, DistributedType



class    __snake_case	:

  def __enter__(       self):
      if self.enabled:
          SCREAMING_SNAKE_CASE_						=    self.model.no_sync()
          self.model_sync_obj.__enter__()

      return self

  def __exit__(       self    ,		_A    ,		_A    ,		_A):
      if self.enabled:
          # Average all models on exit
          self._sync_and_avg_model_params()
          self.model_sync_obj.__exit__(_A    ,		_A    ,		_A)

  def __init__(       self    ,		_A    ,		_A    ,		_A    ,		_A = True):
      if accelerator.distributed_type not in [
          DistributedType.NO,
          DistributedType.MULTI_CPU,
          DistributedType.MULTI_GPU,
      ]:
          raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)')
      SCREAMING_SNAKE_CASE_						=    enabled and accelerator.distributed_type != DistributedType.NO
      SCREAMING_SNAKE_CASE_						=    0
      if self.enabled:
          SCREAMING_SNAKE_CASE_						=    accelerator
          SCREAMING_SNAKE_CASE_						=    model
          SCREAMING_SNAKE_CASE_						=    local_sgd_steps

  def     lowerCAmelCase__  (       self):
      self.num_steps += 1
      if not self.enabled:
          return

      if self.num_steps % self.local_sgd_steps == 0:
          self._sync_and_avg_model_params()


  def     lowerCAmelCase__  (       self):
      self.accelerator.wait_for_everyone()
      with self.accelerator.autocast():
          for param in self.model.parameters():
              SCREAMING_SNAKE_CASE_						=    self.accelerator.reduce(param.data    ,		reduction='mean')





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

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


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       UpperCamelCase__	:   Optional[int]								=     None


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


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[Any]								=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_A    ,		**_A    ,		)

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


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = 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(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_						=    os.path.join(
          _A    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

      return (out_vocab_file,)


