

"""simple docstring"""




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


A_				:      Optional[Any]           =			logging.get_logger(__name__)





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		List[Any]       		=				'linear'
			lowerCamelCase__ :		Optional[Any]       		=				'cosine'
			lowerCamelCase__ :		Union[str, Any]       		=				'cosine_with_restarts'
			lowerCamelCase__ :		Any       		=				'polynomial'
			lowerCamelCase__ :		Union[str, Any]       		=				'constant'
			lowerCamelCase__ :		int       		=				'constant_with_warmup'
			lowerCamelCase__ :		Union[str, Any]       		=				'piecewise_constant'

def 						A							(  snake_case__		,					snake_case__ = -1     ):




			'''simple docstring'''


			return LambdaLR(snake_case__		,					lambda snake_case__     : 1		,					last_epoch=snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__ = -1     ):




			'''simple docstring'''


			def lr_lambda(snake_case__     ):
						if current_step < num_warmup_steps:
									return float(snake_case__     ) / float(max(1.0		,					snake_case__     )     )
						return 1.0

			return LambdaLR(snake_case__		,					snake_case__		,					last_epoch=snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__ = -1     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   {}
			SCREAMING_SNAKE_CASE__										=   step_rules.split(""","""     )
			for rule_str in rule_list[:-1]:
						SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__										=   rule_str.split(""":"""     )
						SCREAMING_SNAKE_CASE__										=   int(snake_case__     )
						SCREAMING_SNAKE_CASE__										=   float(snake_case__     )
						SCREAMING_SNAKE_CASE__										=   value
			SCREAMING_SNAKE_CASE__										=   float(rule_list[-1]     )

			def create_rules_function(snake_case__		,					snake_case__     ):
						def rule_func(snake_case__     ) -> float:
									SCREAMING_SNAKE_CASE__										=   sorted(rules_dict.keys()     )
									for i, sorted_step in enumerate(snake_case__     ):
												if steps < sorted_step:
															return rules_dict[sorted_steps[i]]
									return last_lr_multiple

						return rule_func

			SCREAMING_SNAKE_CASE__										=   create_rules_function(snake_case__		,					snake_case__     )

			return LambdaLR(snake_case__		,					snake_case__		,					last_epoch=snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__=-1     ):




			'''simple docstring'''


			def lr_lambda(snake_case__     ):
						if current_step < num_warmup_steps:
									return float(snake_case__     ) / float(max(1		,					snake_case__     )     )
						return max(
						    0.0		,					float(num_training_steps - current_step     ) / float(max(1		,					num_training_steps - num_warmup_steps     )     )     )

			return LambdaLR(snake_case__		,					snake_case__		,					snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = 0.5		,					snake_case__ = -1     ):




			'''simple docstring'''


			def lr_lambda(snake_case__     ):
						if current_step < num_warmup_steps:
									return float(snake_case__     ) / float(max(1		,					snake_case__     )     )
						SCREAMING_SNAKE_CASE__										=   float(current_step - num_warmup_steps     ) / float(max(1		,					num_training_steps - num_warmup_steps     )     )
						return max(0.0		,					0.5 * (1.0 + math.cos(math.pi * float(snake_case__     ) * 2.0 * progress     ))     )

			return LambdaLR(snake_case__		,					snake_case__		,					snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = 1		,					snake_case__ = -1     ):




			'''simple docstring'''


			def lr_lambda(snake_case__     ):
						if current_step < num_warmup_steps:
									return float(snake_case__     ) / float(max(1		,					snake_case__     )     )
						SCREAMING_SNAKE_CASE__										=   float(current_step - num_warmup_steps     ) / float(max(1		,					num_training_steps - num_warmup_steps     )     )
						if progress >= 1.0:
									return 0.0
						return max(0.0		,					0.5 * (1.0 + math.cos(math.pi * ((float(snake_case__     ) * progress) % 1.0)     ))     )

			return LambdaLR(snake_case__		,					snake_case__		,					snake_case__     )

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__=1e-7		,					snake_case__=1.0		,					snake_case__=-1     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   optimizer.defaults["""lr"""]
			if not (lr_init > lr_end):
						raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""     )

			def lr_lambda(snake_case__     ):
						if current_step < num_warmup_steps:
									return float(snake_case__     ) / float(max(1		,					snake_case__     )     )
						elif current_step > num_training_steps:
									return lr_end / lr_init  # as LambdaLR multiplies by lr_init
						else:
									SCREAMING_SNAKE_CASE__										=   lr_init - lr_end
									SCREAMING_SNAKE_CASE__										=   num_training_steps - num_warmup_steps
									SCREAMING_SNAKE_CASE__										=   1 - (current_step - num_warmup_steps) / decay_steps
									SCREAMING_SNAKE_CASE__										=   lr_range * pct_remaining**power + lr_end
									return decay / lr_init  # as LambdaLR multiplies by lr_init

			return LambdaLR(snake_case__		,					snake_case__		,					snake_case__     )


A_				:      List[str]           =			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}




def 						A							(  snake_case__		,					snake_case__		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = None		,					snake_case__ = 1		,					snake_case__ = 1.0		,					snake_case__ = -1		,					):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   SchedulerType(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   TYPE_TO_SCHEDULER_FUNCTION[name]
			if name == SchedulerType.CONSTANT:
						return schedule_func(snake_case__		,					last_epoch=snake_case__     )

			if name == SchedulerType.PIECEWISE_CONSTANT:
						return schedule_func(snake_case__		,					step_rules=snake_case__		,					last_epoch=snake_case__     )

			# All other schedulers require `num_warmup_steps`
			if num_warmup_steps is None:
						raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""     )

			if name == SchedulerType.CONSTANT_WITH_WARMUP:
						return schedule_func(snake_case__		,					num_warmup_steps=snake_case__		,					last_epoch=snake_case__     )

			# All other schedulers require `num_training_steps`
			if num_training_steps is None:
						raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""     )

			if name == SchedulerType.COSINE_WITH_RESTARTS:
						return schedule_func(
						    snake_case__		,					num_warmup_steps=snake_case__		,					num_training_steps=snake_case__		,					num_cycles=snake_case__		,					last_epoch=snake_case__		,					)

			if name == SchedulerType.POLYNOMIAL:
						return schedule_func(
						    snake_case__		,					num_warmup_steps=snake_case__		,					num_training_steps=snake_case__		,					power=snake_case__		,					last_epoch=snake_case__		,					)

			return schedule_func(
			    snake_case__		,					num_warmup_steps=snake_case__		,					num_training_steps=snake_case__		,					last_epoch=snake_case__     )





"""simple docstring"""




A_				:      Any           =			"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich


