'''simple docstring'''




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

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


class       UpperCAmelCase__  :






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

        lowercase_					:       Optional[Any]     				=					config

        self.set_stage_and_offload()
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # 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.
        lowercase_					:       Tuple     				=					self.get_value('zero_optimization.stage'       ,				-1				)

        # offload
        lowercase_					:       Optional[int]     				=					False
        if self.is_zeroa() or self.is_zeroa():
             lowercase_					:       Dict     				=					set(['cpu', 'nvme']				)
             lowercase_					:       List[Any]     				=					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:
                  lowercase_					:       Union[str, Any]     				=					True
   def 		_lowerCamelCase (self       ,				_a				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.config

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

        return config, ds_key
   def 		_lowerCamelCase (self       ,				_a       ,				_a=None				)    ->     Optional[Any]:
        lowercase_				,lowercase_					:       Any     				=					self.find_config_node(_a				)
        if config is None:
             return default
        return config.get(_a       ,				_a				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a=False				)    ->     List[Any]:
        lowercase_					:       List[str]     				=					self.config

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





   def 		_lowerCamelCase (self				)    ->     str:
        return self._offload



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a				)    ->     List[str]:
        lowercase_					:       Optional[Any]     				=					engine





   def 		_lowerCamelCase (self       ,				_a       ,				**_a				)    ->     Dict:
        # 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       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def __init__(self       ,				_a				)    ->     int:
        super().__init__(_a       ,				device_placement=_a       ,				scaler=_a				)
        lowercase_					:       Any     				=					hasattr(self.optimizer       ,				'overflow'				)
   def 		_lowerCamelCase (self       ,				_a=None				)    ->     Union[str, Any]:
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
   def 		_lowerCamelCase (self				)    ->     Tuple:
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        if self.__has_overflow__:
             return self.optimizer.overflow
        return False



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a				)    ->     str:
        super().__init__(_a       ,				_a				)





   def 		_lowerCamelCase (self				)    ->     str:
        pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=0.0_01       ,				_a=0       ,				**_a				)    ->     Dict:
        lowercase_					:       List[Any]     				=					params
        lowercase_					:       Dict     				=					lr
        lowercase_					:       List[str]     				=					weight_decay
        lowercase_					:       List[str]     				=					kwargs



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=None       ,				_a=0       ,				**_a				)    ->     int:
        lowercase_					:       List[Any]     				=					optimizer
        lowercase_					:       List[Any]     				=					total_num_steps
        lowercase_					:       Optional[int]     				=					warmup_num_steps
        lowercase_					:       List[str]     				=					kwargs





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A								=   {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A								=   [
     'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BigBirdPegasusForCausalLM',
     'BigBirdPegasusForConditionalGeneration',
     'BigBirdPegasusForQuestionAnswering',
     'BigBirdPegasusForSequenceClassification',
     'BigBirdPegasusModel',
     'BigBirdPegasusPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bigbird_pegasus import (
     BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BigBirdPegasusConfig,
     BigBirdPegasusOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
      BigBirdPegasusForCausalLM,
      BigBirdPegasusForConditionalGeneration,
      BigBirdPegasusForQuestionAnswering,
      BigBirdPegasusForSequenceClassification,
      BigBirdPegasusModel,
      BigBirdPegasusPreTrainedModel,
  )


else:
 import sys

 _A								=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




