




'''simple docstring'''



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

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







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Dict  ,				__lowerCamelCase :     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
         _SCREAMING_SNAKE_CASE	=				deepcopy(__lowerCamelCase   )
      elif os.path.exists(__lowerCamelCase   ):
         with io.open(__lowerCamelCase  ,				"r"  ,				encoding="utf-8"   ) as f:
            _SCREAMING_SNAKE_CASE	=				json.load(__lowerCamelCase   )
      else:
         try:
            _SCREAMING_SNAKE_CASE	=				baseaa.urlsafe_baadecode(__lowerCamelCase   ).decode("utf-8"   )
            _SCREAMING_SNAKE_CASE	=				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}"""   )

      _SCREAMING_SNAKE_CASE	=				config

      self.set_stage_and_offload()



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_value("zero_optimization.stage"  ,				-1   )

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.config

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

      return config, ds_key



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     int=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.find_config_node(__lowerCamelCase   )
      if config is None:
         return default
      return config.get(__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[Any]=False   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.config

      # find the config node of interest if it exists
      _SCREAMING_SNAKE_CASE	=				ds_key_long.split("."   )
      for node in nodes:
         _SCREAMING_SNAKE_CASE	=				config
         _SCREAMING_SNAKE_CASE	=				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 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_value(__lowerCamelCase   )
      return False if value is None else bool(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_value(__lowerCamelCase   )
      return False if value is None else not bool(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self._stage == 2



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      return self._stage == 3



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self._offload







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     List[Any]  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				engine



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				**__lowerCamelCase :     Union[str, 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 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase  ,				device_placement=__lowerCamelCase  ,				scaler=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				hasattr(self.optimizer  ,				"overflow"   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Dict=None   ):

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



   def 							lowerCAmelCase_      (   self :     Any   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      if self.__has_overflow__:
         return self.optimizer.overflow
      return False







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Any  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

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







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Optional[int]=0.0_0_1  ,				__lowerCamelCase :     Dict=0  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				params
      _SCREAMING_SNAKE_CASE	=				lr
      _SCREAMING_SNAKE_CASE	=				weight_decay
      _SCREAMING_SNAKE_CASE	=				kwargs







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple=None  ,				__lowerCamelCase :     List[Any]=0  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				optimizer
      _SCREAMING_SNAKE_CASE	=				total_num_steps
      _SCREAMING_SNAKE_CASE	=				warmup_num_steps
      _SCREAMING_SNAKE_CASE	=				kwargs







'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]=1_2_8_1_0_0  ,				__lowerCamelCase :     Optional[int]=1_5_3_6  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     List[str]="gelu"  ,				__lowerCamelCase :     int=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Union[str, Any]=5_1_2  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Any=-1  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[int]=0  ,				__lowerCamelCase :     Any="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

      # Backwards compatibility
      if type(__lowerCamelCase   ) == str:
         _SCREAMING_SNAKE_CASE	=				[x.strip() for x in pos_att_type.lower().split("|"   )]

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "sequence"}
      if self._config.type_vocab_size > 0:
         return OrderedDict(
             [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]   )
      else:
         return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=__lowerCamelCase  ,				framework=__lowerCamelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

