





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

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







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    List[str]							,				A :    Tuple		):
         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
           __snake_case:						Optional[Any]   =						deepcopy(A		)
         elif os.path.exists(A		):
           with io.open(A							,				"""r"""							,				encoding="""utf-8"""		) as f:
             __snake_case:						str   =						json.load(A		)
         else:
           try:
             __snake_case:						Optional[Any]   =						baseaa.urlsafe_baadecode(A		).decode("""utf-8"""		)
             __snake_case:						Dict   =						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}'''		)

         __snake_case:						Optional[Any]   =						config

         self.set_stage_and_offload()




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         # 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.
         __snake_case:						Union[str, Any]   =						self.get_value("""zero_optimization.stage"""							,				-1		)

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




       def 							UpperCAmelCase__ (  self :    Any							,				A :    Optional[int]		):
         __snake_case:						Tuple   =						self.config

         # find the config node of interest if it exists
         __snake_case:						Union[str, Any]   =						ds_key_long.split("""."""		)
         __snake_case:						List[str]   =						nodes.pop()
         for node in nodes:
           __snake_case:						Dict   =						config.get(A		)
           if config is None:
             return None, ds_key

         return config, ds_key




       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    int							,				A :    List[Any]=None		):
         __snake_case  ,   __snake_case:						Optional[int]   =						self.find_config_node(A		)
         if config is None:
           return default
         return config.get(A							,				A		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Tuple							,				A :    Optional[int]=False		):
         __snake_case:						Optional[int]   =						self.config

         # find the config node of interest if it exists
         __snake_case:						int   =						ds_key_long.split("""."""		)
         for node in nodes:
           __snake_case:						Optional[int]   =						config
           __snake_case:						List[str]   =						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							,				A :    List[Any]		):
         __snake_case:						Tuple   =						self.get_value(A		)
         return False if value is None else bool(A		)




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    Any		):
         __snake_case:						Dict   =						self.get_value(A		)
         return False if value is None else not bool(A		)




       def 							UpperCAmelCase__ (  self :    str		):
         return self._stage == 2




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         return self._stage == 3






       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         return self._offload







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    str							,				A :    int		):
         __snake_case:						Optional[Any]   =						engine






       def 							UpperCAmelCase__ (  self :    Tuple							,				A :    int							,				**A :    List[str]		):
         # 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     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Union[str, Any]							,				A :    str		):
         super().__init__(A							,				device_placement=A							,				scaler=A		)
         __snake_case:						Dict   =						hasattr(self.optimizer							,				"""overflow"""		)




       def 							UpperCAmelCase__ (  self :    Dict							,				A :    Union[str, Any]=None		):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




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






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







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Tuple							,				A :    Any							,				A :    Tuple		):
         super().__init__(A							,				A		)






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







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    Optional[Any]							,				A :    Tuple							,				A :    int=0.001							,				A :    int=0							,				**A :    Dict		):
         __snake_case:						Union[str, Any]   =						params
         __snake_case:						Any   =						lr
         __snake_case:						Any   =						weight_decay
         __snake_case:						Optional[Any]   =						kwargs







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    str							,				A :    Tuple							,				A :    Union[str, Any]=None							,				A :    Union[str, Any]=0							,				**A :    Any		):
         __snake_case:						Union[str, Any]   =						optimizer
         __snake_case:						Optional[int]   =						total_num_steps
         __snake_case:						Any   =						warmup_num_steps
         __snake_case:						List[str]   =						kwargs













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       :   Optional[int]				       =							logging.get_logger(__name__)

__UpperCAmelCase       :   Optional[int]				       =							{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     """ibert"""




       def __init__(  self :    Dict							,				A :    Union[str, Any]=30_522							,				A :    List[Any]=768							,				A :    List[Any]=12							,				A :    Optional[int]=12							,				A :    Optional[Any]=3_072							,				A :    int="gelu"							,				A :    str=0.1							,				A :    List[Any]=0.1							,				A :    Optional[Any]=512							,				A :    int=2							,				A :    Union[str, Any]=0.02							,				A :    List[str]=1E-12							,				A :    Optional[int]=1							,				A :    Optional[int]=0							,				A :    List[str]=2							,				A :    str="absolute"							,				A :    Any=False							,				A :    Optional[Any]="none"							,				**A :    Any							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)

         __snake_case:						List[Any]   =						vocab_size
         __snake_case:						Optional[Any]   =						hidden_size
         __snake_case:						List[str]   =						num_hidden_layers
         __snake_case:						Tuple   =						num_attention_heads
         __snake_case:						List[str]   =						hidden_act
         __snake_case:						Optional[Any]   =						intermediate_size
         __snake_case:						Tuple   =						hidden_dropout_prob
         __snake_case:						List[str]   =						attention_probs_dropout_prob
         __snake_case:						Any   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						List[str]   =						initializer_range
         __snake_case:						List[Any]   =						layer_norm_eps
         __snake_case:						Optional[int]   =						position_embedding_type
         __snake_case:						str   =						quant_mode
         __snake_case:						Optional[int]   =						force_dequant







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       @property
       def 							UpperCAmelCase__ (  self :    Tuple		):
         if self.task == "multiple-choice":
           __snake_case:						List[Any]   =						{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           __snake_case:						List[str]   =						{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]		)






