'''simple docstring'''





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

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




class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										if isinstance(lowercase__       ,		lowercase__					):
																# 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			:			Union[str, Any]   			=						deepcopy(lowercase__					)
										elif os.path.exists(lowercase__					):
																with io.open(lowercase__       ,		"r"       ,		encoding="utf-8"					) as f:
																						_UpperCamelCase			:			Dict   			=						json.load(lowercase__					)
										else:
																try:
																						_UpperCamelCase			:			List[str]   			=						baseaa.urlsafe_baadecode(lowercase__					).decode("utf-8"					)
																						_UpperCamelCase			:			Union[str, Any]   			=						json.loads(lowercase__					)
																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			:			Union[str, Any]   			=						config

										self.set_stage_and_offload()





				def  snake_case__				(						self					:						Optional[Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						self.get_value("zero_optimization.stage"       ,		-1					)

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





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						List[Any]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.config

										# find the config node of interest if it exists
										_UpperCamelCase			:			Optional[int]   			=						ds_key_long.split("."					)
										_UpperCamelCase			:			List[str]   			=						nodes.pop()
										for node in nodes:
																_UpperCamelCase			:			Optional[int]   			=						config.get(lowercase__					)
																if config is None:
																						return None, ds_key

										return config, ds_key





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


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			Dict   			=						self.find_config_node(lowercase__					)
										if config is None:
																return default
										return config.get(lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						str       ,		lowercase__					:						Any       ,		lowercase__					:						Tuple=False					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						self.config

										# find the config node of interest if it exists
										_UpperCamelCase			:			Union[str, Any]   			=						ds_key_long.split("."					)
										for node in nodes:
																_UpperCamelCase			:			Union[str, Any]   			=						config
																_UpperCamelCase			:			Optional[int]   			=						config.get(lowercase__					)
																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(lowercase__					)





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						int					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.get_value(lowercase__					)
										return False if value is None else bool(lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Optional[Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						self.get_value(lowercase__					)
										return False if value is None else not bool(lowercase__					)





				def  snake_case__				(						self					:						Dict					)							->Tuple:


										'''simple docstring'''




										return self._stage == 2





				def  snake_case__				(						self					:						Any					)							->int:


										'''simple docstring'''




										return self._stage == 3







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


										'''simple docstring'''




										return self._offload





class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						List[Any]       ,		lowercase__					:						Dict					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						engine







				def  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						int       ,		**lowercase__					:						Dict					)							->List[Any]:


										'''simple docstring'''




										self.engine.backward(lowercase__       ,		**lowercase__					)

										# 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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						Optional[Any]					)							->Tuple:


										'''simple docstring'''




										super().__init__(lowercase__       ,		device_placement=lowercase__       ,		scaler=lowercase__					)
										_UpperCamelCase			:			Dict   			=						hasattr(self.optimizer       ,		"overflow"					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						Union[str, Any]=None					)							->List[str]:


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Tuple					)							->List[str]:


										'''simple docstring'''




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







				@property
				def  snake_case__				(						self					:						Tuple					)							->List[str]:


										'''simple docstring'''




										if self.__has_overflow__:
																return self.optimizer.overflow
										return False





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





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


										'''simple docstring'''




										super().__init__(lowercase__       ,		lowercase__					)







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


										'''simple docstring'''




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





class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Union[str, Any]=0.0_0_1       ,		lowercase__					:						Tuple=0       ,		**lowercase__					:						Union[str, Any]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						params
										_UpperCamelCase			:			Any   			=						lr
										_UpperCamelCase			:			List[Any]   			=						weight_decay
										_UpperCamelCase			:			Optional[int]   			=						kwargs





class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Optional[Any]=None       ,		lowercase__					:						Tuple=0       ,		**lowercase__					:						List[Any]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						optimizer
										_UpperCamelCase			:			List[Any]   			=						total_num_steps
										_UpperCamelCase			:			int   			=						warmup_num_steps
										_UpperCamelCase			:			str   			=						kwargs



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_	:		Union[str, Any]			=     {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		Tuple			=     [
			    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """OPTForCausalLM""",
			    """OPTModel""",
			    """OPTPreTrainedModel""",
			    """OPTForSequenceClassification""",
			    """OPTForQuestionAnswering""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		str			=     ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_	:		List[Any]			=     [
			    """FlaxOPTForCausalLM""",
			    """FlaxOPTModel""",
			    """FlaxOPTPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_opt import (
						    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OPTForCausalLM,
						    OPTForQuestionAnswering,
						    OPTForSequenceClassification,
						    OPTModel,
						    OPTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
			import sys

			lowerCAmelCase_	:		Any			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


