
"""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 :							Dict       ,      UpperCAmelCase_ :							Optional[int]			)->  Optional[Any]:







              """simple docstring"""
              if isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                     # 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
                     UpperCamelCase					 =		deepcopy(UpperCAmelCase_			)
              elif os.path.exists(UpperCAmelCase_			):
                     with io.open(UpperCAmelCase_       ,      "r"       ,      encoding="utf-8"			) as f:
                            UpperCamelCase					 =		json.load(UpperCAmelCase_			)
              else:
                     try:
                            UpperCamelCase					 =		baseaa.urlsafe_baadecode(UpperCAmelCase_			).decode("utf-8"			)
                            UpperCamelCase					 =		json.loads(UpperCAmelCase_			)
                     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}"			)

              UpperCamelCase					 =		config

              self.set_stage_and_offload()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Union[str, Any]:







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

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      UpperCAmelCase_ :							Tuple			)->  Any:







              """simple docstring"""
              UpperCamelCase					 =		self.config

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

              return config, ds_key






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							Union[str, Any]       ,      UpperCAmelCase_ :							int=None			)->  List[Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							List[str]       ,      UpperCAmelCase_ :							str=False			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.config

              # find the config node of interest if it exists
              UpperCamelCase					 =		ds_key_long.split("."			)
              for node in nodes:
                     UpperCamelCase					 =		config
                     UpperCamelCase					 =		config.get(UpperCAmelCase_			)
                     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(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]       ,      UpperCAmelCase_ :							List[Any]			)->  str:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any       ,      UpperCAmelCase_ :							Any			)->  int:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Union[str, Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







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





class   __a  :






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







              """simple docstring"""
              UpperCamelCase					 =		engine






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							Dict       ,      **UpperCAmelCase_ :							List[Any]			)->  Optional[Any]:







              """simple docstring"""
              # runs backpropagation and handles mixed precision
              self.engine.backward(UpperCAmelCase_       ,      **UpperCAmelCase_			)

              # 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  (      _lowerCAmelCase	):






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







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]       ,      UpperCAmelCase_ :							Union[str, Any]=None			)->  Optional[Any]:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  int:







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






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[int]:







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





class   __a  (      _lowerCAmelCase	):






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







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Tuple:







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





class   __a  :






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







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





class   __a  :






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







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







"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[int]   =			'''upernet'''






       def __init__(  self :							int       ,      UpperCAmelCase_ :							Optional[Any]=None       ,      UpperCAmelCase_ :							Any=512       ,      UpperCAmelCase_ :							Union[str, Any]=0.02       ,      UpperCAmelCase_ :							List[Any]=[1, 2, 3, 6]       ,      UpperCAmelCase_ :							List[str]=True       ,      UpperCAmelCase_ :							Union[str, Any]=0.4       ,      UpperCAmelCase_ :							Union[str, Any]=384       ,      UpperCAmelCase_ :							Optional[Any]=256       ,      UpperCAmelCase_ :							Optional[int]=1       ,      UpperCAmelCase_ :							str=False       ,      UpperCAmelCase_ :							Tuple=255       ,      **UpperCAmelCase_ :							str       ,      )->  Tuple:







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

              if backbone_config is None:
                     logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."			)
                     UpperCamelCase					 =		CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]			)
              elif isinstance(UpperCAmelCase_       ,      UpperCAmelCase_			):
                     UpperCamelCase					 =		backbone_config.get("model_type"			)
                     UpperCamelCase					 =		CONFIG_MAPPING[backbone_model_type]
                     UpperCamelCase					 =		config_class.from_dict(UpperCAmelCase_			)

              UpperCamelCase					 =		backbone_config
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		pool_scales
              UpperCamelCase					 =		use_auxiliary_head
              UpperCamelCase					 =		auxiliary_loss_weight
              UpperCamelCase					 =		auxiliary_in_channels
              UpperCamelCase					 =		auxiliary_channels
              UpperCamelCase					 =		auxiliary_num_convs
              UpperCamelCase					 =		auxiliary_concat_input
              UpperCamelCase					 =		loss_ignore_index






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		copy.deepcopy(self.__dict__			)
              UpperCamelCase					 =		self.backbone_config.to_dict()
              UpperCamelCase					 =		self.__class__.model_type
              return output





