


"""simple docstring"""




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

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




class 			__snake_case   :




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

										__lowerCamelCase          =      config

										self.set_stage_and_offload()




							def   __a   (     self:    List[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.
										__lowerCamelCase          =      self.get_value("""zero_optimization.stage"""     ,							-1   )

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




							def   __a   (     self:    Any     ,							A_:    Any   ):
										__lowerCamelCase          =      self.config

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

										return config, ds_key




							def   __a   (     self:    Tuple     ,							A_:    List[Any]     ,							A_:    str=None   ):
										__lowerCamelCase     ,__lowerCamelCase          =      self.find_config_node(A_   )
										if config is None:
													return default
										return config.get(A_     ,							A_   )




							def   __a   (     self:    str     ,							A_:    List[Any]     ,							A_:    Optional[int]=False   ):
										__lowerCamelCase          =      self.config

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




							def   __a   (     self:    Optional[int]     ,							A_:    int   ):
										__lowerCamelCase          =      self.get_value(A_   )
										return False if value is None else not bool(A_   )




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




							def   __a   (     self:    Optional[Any]   ):
										return self._stage == 3




							def   __a   (     self:    Union[str, Any]   ):
										return self._offload




class 			__snake_case   :




							def __init__(     self:    int     ,							A_:    Union[str, Any]   ):
										__lowerCamelCase          =      engine




							def   __a   (     self:    Dict     ,							A_:    int     ,							**A_:    Any   ):
										# 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  ):




							def __init__(     self:    Dict     ,							A_:    Tuple   ):
										super().__init__(A_     ,							device_placement=A_     ,							scaler=A_   )
										__lowerCamelCase          =      hasattr(self.optimizer     ,							"""overflow"""   )




							def   __a   (     self:    Optional[Any]     ,							A_:    List[Any]=None   ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




							def   __a   (     self:    Union[str, Any]   ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




							@property
							def   __a   (     self:    Optional[Any]   ):
										if self.__has_overflow__:
													return self.optimizer.overflow
										return False




class 			__snake_case   (lowerCamelCase  ):




							def __init__(     self:    str     ,							A_:    Any     ,							A_:    Tuple   ):
										super().__init__(A_     ,							A_   )




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




class 			__snake_case   :




							def __init__(     self:    Any     ,							A_:    Union[str, Any]     ,							A_:    Union[str, Any]=0.001     ,							A_:    Optional[int]=0     ,							**A_:    Union[str, Any]   ):
										__lowerCamelCase          =      params
										__lowerCamelCase          =      lr
										__lowerCamelCase          =      weight_decay
										__lowerCamelCase          =      kwargs




class 			__snake_case   :




							def __init__(     self:    Optional[int]     ,							A_:    List[Any]     ,							A_:    Optional[int]=None     ,							A_:    Dict=0     ,							**A_:    Optional[int]   ):
										__lowerCamelCase          =      optimizer
										__lowerCamelCase          =      total_num_steps
										__lowerCamelCase          =      warmup_num_steps
										__lowerCamelCase          =      kwargs








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




