

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

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



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Any	,							lowerCAmelCase			:   List[Any]   )							->     str:






     """simple docstring"""







     if isinstance(lowerCAmelCase	,							lowerCAmelCase   ):
        # 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(lowerCAmelCase   )
     elif os.path.exists(lowerCAmelCase   ):
        with io.open(lowerCAmelCase	,							"""r"""	,							encoding="""utf-8"""   ) as f:
           __lowerCAmelCase  :      str    =						json.load(lowerCAmelCase   )
     else:
        try:
           __lowerCAmelCase  :      List[Any]    =						baseaa.urlsafe_baadecode(lowerCAmelCase   ).decode("""utf-8"""   )
           __lowerCAmelCase  :      str    =						json.loads(lowerCAmelCase   )
        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  :      Tuple    =						config

     self.set_stage_and_offload()







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.get_value("""zero_optimization.stage"""	,							-1   )

     # offload
     __lowerCAmelCase  :      int    =						False
     if self.is_zeroa() or self.is_zeroa():
        __lowerCAmelCase  :      Optional[int]    =						set(["""cpu""", """nvme"""]   )
        __lowerCAmelCase  :      int    =						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  :      Any    =						True







  def 	SCREAMING_SNAKE_CASE   (      self			:   str	,							lowerCAmelCase			:   str   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.config

     # find the config node of interest if it exists
     __lowerCAmelCase  :      int    =						ds_key_long.split("""."""   )
     __lowerCAmelCase  :      Optional[int]    =						nodes.pop()
     for node in nodes:
        __lowerCAmelCase  :      int    =						config.get(lowerCAmelCase   )
        if config is None:
           return None, ds_key

     return config, ds_key







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Union[str, Any]=None   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						self.find_config_node(lowerCAmelCase   )
     if config is None:
        return default
     return config.get(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int]=False   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.config

     # find the config node of interest if it exists
     __lowerCAmelCase  :      Dict    =						ds_key_long.split("""."""   )
     for node in nodes:
        __lowerCAmelCase  :      Tuple    =						config
        __lowerCAmelCase  :      Dict    =						config.get(lowerCAmelCase   )
        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(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.get_value(lowerCAmelCase   )
     return False if value is None else bool(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.get_value(lowerCAmelCase   )
     return False if value is None else not bool(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     return self._stage == 2







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     return self._stage == 3







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Any:






     """simple docstring"""







     return self._offload






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int	,							lowerCAmelCase			:   Optional[int]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						engine







  def 	SCREAMING_SNAKE_CASE   (      self			:   int	,							lowerCAmelCase			:   List[Any]	,							**lowerCAmelCase			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     self.engine.backward(lowerCAmelCase	,							**lowerCAmelCase   )

     # 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 SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   List[str]	,							lowerCAmelCase			:   Tuple   )							->     Tuple:






     """simple docstring"""







     super().__init__(lowerCAmelCase	,							device_placement=lowerCAmelCase	,							scaler=lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						hasattr(self.optimizer	,							"""overflow"""   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   List[Any]=None   )							->     List[str]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







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







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     if self.__has_overflow__:
        return self.optimizer.overflow
     return False






class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[Any]   )							->     List[str]:






     """simple docstring"""







     super().__init__(lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Any:






     """simple docstring"""







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






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Tuple	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[int]=0.001	,							lowerCAmelCase			:   Optional[int]=0	,							**lowerCAmelCase			:   Any   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						params
     __lowerCAmelCase  :      List[Any]    =						lr
     __lowerCAmelCase  :      Any    =						weight_decay
     __lowerCAmelCase  :      List[str]    =						kwargs






class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]=None	,							lowerCAmelCase			:   List[Any]=0	,							**lowerCAmelCase			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						optimizer
     __lowerCAmelCase  :      Optional[Any]    =						total_num_steps
     __lowerCAmelCase  :      Tuple    =						warmup_num_steps
     __lowerCAmelCase  :      Union[str, Any]    =						kwargs





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=a_			)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(default="audio-classification"       ,							metadata={"include_in_asdict_even_if_is_default": True}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"audio": Audio()}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"labels": ClassLabel}			)
  lowerCamelCase   :      str										="audio"
  lowerCamelCase   :      str										="labels"







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Dict   )							->     Dict:






     """simple docstring"""







     if self.label_column not in features:
        raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
     if not isinstance(features[self.label_column]	,							lowerCAmelCase   ):
        raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
     __lowerCAmelCase  :      Tuple    =						copy.deepcopy(self   )
     __lowerCAmelCase  :      Tuple    =						self.label_schema.copy()
     __lowerCAmelCase  :      Union[str, Any]    =						features[self.label_column]
     __lowerCAmelCase  :      Any    =						label_schema
     return task_template







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Dict[str, str]:






     """simple docstring"""







     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }


