


"""simple docstring"""


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

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



class 	_a   :
    def __init__(			self				:       Any				,       SCREAMING_SNAKE_CASE__				:       Union[str, Any]					):
          if isinstance(_a				,       _a					):
                # 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__							     =		deepcopy(_a					)
          elif os.path.exists(_a					):
                with io.open(_a				,       'r'				,       encoding='utf-8'					) as f:
                      lowerCamelCase__							     =		json.load(_a					)
          else:
                try:
                      lowerCamelCase__							     =		baseaa.urlsafe_baadecode(_a					).decode('utf-8'					)
                      lowerCamelCase__							     =		json.loads(_a					)
                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__							     =		config

          self.set_stage_and_offload()
    def   _UpperCamelCase							(			self				:       str					):
          # 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__							     =		self.get_value('zero_optimization.stage'				,       -1					)

          # offload
          lowerCamelCase__							     =		False
          if self.is_zeroa() or self.is_zeroa():
                lowerCamelCase__							     =		set(['cpu', 'nvme']					)
                lowerCamelCase__							     =		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__							     =		True
    def   _UpperCamelCase							(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Optional[int]					):
          lowerCamelCase__							     =		self.config

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

          return config, ds_key
    def   _UpperCamelCase							(			self				:       Dict				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       Any=None					):
          lowerCamelCase__							     =		self.find_config_node(_a					)
          if config is None:
                return default
          return config.get(_a				,       _a					)
    def   _UpperCamelCase							(			self				:       Union[str, Any]				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=False					):
          lowerCamelCase__							     =		self.config

          # find the config node of interest if it exists
          lowerCamelCase__							     =		ds_key_long.split('.'					)
          for node in nodes:
                lowerCamelCase__							     =		config
                lowerCamelCase__							     =		config.get(_a					)
                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(_a					)
    def   _UpperCamelCase							(			self				:       int				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]					):
          lowerCamelCase__							     =		self.get_value(_a					)
          return False if value is None else bool(_a					)
    def   _UpperCamelCase							(			self				:       Tuple				,       SCREAMING_SNAKE_CASE__				:       Optional[int]					):
          lowerCamelCase__							     =		self.get_value(_a					)
          return False if value is None else not bool(_a					)
    def   _UpperCamelCase							(			self				:       List[str]					):
          return self._stage == 2
    def   _UpperCamelCase							(			self				:       str					):
          return self._stage == 3


    def   _UpperCamelCase							(			self				:       List[Any]					):
          return self._offload





class 	_a   :
    def __init__(			self				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Any					):
          lowerCamelCase__							     =		engine


    def   _UpperCamelCase							(			self				:       str				,       SCREAMING_SNAKE_CASE__				:       str				,       **SCREAMING_SNAKE_CASE__				:       Tuple					):
          # runs backpropagation and handles mixed precision
          self.engine.backward(_a				,       **_a					)

          # 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 	_a   (   SCREAMING_SNAKE_CASE_			):
    def __init__(			self				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       List[Any]					):
          super().__init__(_a				,       device_placement=_a				,       scaler=_a					)
          lowerCamelCase__							     =		hasattr(self.optimizer				,       'overflow'					)
    def   _UpperCamelCase							(			self				:       int				,       SCREAMING_SNAKE_CASE__				:       int=None					):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
    def   _UpperCamelCase							(			self				:       Tuple					):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


    @property
    def   _UpperCamelCase							(			self				:       Optional[Any]					):
          if self.__has_overflow__:
                return self.optimizer.overflow
          return False





class 	_a   (   SCREAMING_SNAKE_CASE_			):
    def __init__(			self				:       int				,       SCREAMING_SNAKE_CASE__				:       int				,       SCREAMING_SNAKE_CASE__				:       int					):
          super().__init__(_a				,       _a					)


    def   _UpperCamelCase							(			self				:       Optional[int]					):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class 	_a   :
    def __init__(			self				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       List[Any]				,       SCREAMING_SNAKE_CASE__				:       Optional[int]=0.0_01				,       SCREAMING_SNAKE_CASE__				:       List[str]=0				,       **SCREAMING_SNAKE_CASE__				:       Tuple					):
          lowerCamelCase__							     =		params
          lowerCamelCase__							     =		lr
          lowerCamelCase__							     =		weight_decay
          lowerCamelCase__							     =		kwargs





class 	_a   :
    def __init__(			self				:       List[str]				,       SCREAMING_SNAKE_CASE__				:       Optional[Any]				,       SCREAMING_SNAKE_CASE__				:       Dict=None				,       SCREAMING_SNAKE_CASE__				:       Dict=0				,       **SCREAMING_SNAKE_CASE__				:       Optional[int]					):
          lowerCamelCase__							     =		optimizer
          lowerCamelCase__							     =		total_num_steps
          lowerCamelCase__							     =		warmup_num_steps
          lowerCamelCase__							     =		kwargs








'''simple docstring'''





import json
import os
from typing import Optional, Tuple

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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    return dict(self.vocab     ,      **self.added_tokens_encoder  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

    with open(_a     ,      'w'     ,      encoding='utf-8'  ) as f:
      f.write(json.dumps(self.vocab     ,      indent=2     ,      sort_keys=_a     ,      ensure_ascii=_a  ) + '\n'  )

    return (vocab_file,)






