

"""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						       =      logging.get_logger(__name__)





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Dict       				=  """linear"""
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  """cosine"""
				SCREAMING_SNAKE_CASE_       :				Any       				=  """cosine_with_restarts"""
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  """polynomial"""
				SCREAMING_SNAKE_CASE_       :				int       				=  """constant"""
				SCREAMING_SNAKE_CASE_       :				int       				=  """constant_with_warmup"""
				SCREAMING_SNAKE_CASE_       :				str       				=  """piecewise_constant"""






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = -1			)     ->     Dict:
		return LambdaLR(__SCREAMING_SNAKE_CASE ,			lambda __SCREAMING_SNAKE_CASE			: 1 ,			last_epoch=__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = -1			)     ->     int:

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

		return LambdaLR(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			last_epoch=__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = -1			)     ->     Optional[Any]:
		__lowerCAmelCase:	Union[str, Any]   				=      {}
		__lowerCAmelCase:	Dict   				=      step_rules.split(","			)
		for rule_str in rule_list[:-1]:
				__lowerCAmelCase:	Dict   				=      rule_str.split(":"			)
				__lowerCAmelCase:	List[str]   				=      int(__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Any   				=      float(__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	List[Any]   				=      value
		__lowerCAmelCase:	Optional[Any]   				=      float(rule_list[-1]			)

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

				return rule_func

		__lowerCAmelCase:	Tuple   				=      create_rules_function(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

		return LambdaLR(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			last_epoch=__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=-1			)     ->     Optional[Any]:

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

		return LambdaLR(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 0.5 ,			__SCREAMING_SNAKE_CASE = -1			)     ->     Dict:

		def lr_lambda(__SCREAMING_SNAKE_CASE			):
				if current_step < num_warmup_steps:
						return float(__SCREAMING_SNAKE_CASE			) / float(max(1 ,			__SCREAMING_SNAKE_CASE			)			)
				__lowerCAmelCase:	str   				=      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(__SCREAMING_SNAKE_CASE			) * 2.0 * progress			))			)

		return LambdaLR(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1 ,			__SCREAMING_SNAKE_CASE = -1			)     ->     Tuple:

		def lr_lambda(__SCREAMING_SNAKE_CASE			):
				if current_step < num_warmup_steps:
						return float(__SCREAMING_SNAKE_CASE			) / float(max(1 ,			__SCREAMING_SNAKE_CASE			)			)
				__lowerCAmelCase:	Optional[int]   				=      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(__SCREAMING_SNAKE_CASE			) * progress) % 1.0)			))			)

		return LambdaLR(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=1E-7 ,			__SCREAMING_SNAKE_CASE=1.0 ,			__SCREAMING_SNAKE_CASE=-1			)     ->     int:
		__lowerCAmelCase:	Union[str, Any]   				=      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(__SCREAMING_SNAKE_CASE			):
				if current_step < num_warmup_steps:
						return float(__SCREAMING_SNAKE_CASE			) / float(max(1 ,			__SCREAMING_SNAKE_CASE			)			)
				elif current_step > num_training_steps:
						return lr_end / lr_init  # as LambdaLR multiplies by lr_init
				else:
						__lowerCAmelCase:	Dict   				=      lr_init - lr_end
						__lowerCAmelCase:	str   				=      num_training_steps - num_warmup_steps
						__lowerCAmelCase:	str   				=      1 - (current_step - num_warmup_steps) / decay_steps
						__lowerCAmelCase:	int   				=      lr_range * pct_remaining**power + lr_end
						return decay / lr_init  # as LambdaLR multiplies by lr_init

		return LambdaLR(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)


__A						       =      {
    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__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = None ,			__SCREAMING_SNAKE_CASE = None ,			__SCREAMING_SNAKE_CASE = None ,			__SCREAMING_SNAKE_CASE = 1 ,			__SCREAMING_SNAKE_CASE = 1.0 ,			__SCREAMING_SNAKE_CASE = -1 ,			)     ->     Dict:
		__lowerCAmelCase:	Union[str, Any]   				=      SchedulerType(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[str]   				=      TYPE_TO_SCHEDULER_FUNCTION[name]
		if name == SchedulerType.CONSTANT:
				return schedule_func(__SCREAMING_SNAKE_CASE ,			last_epoch=__SCREAMING_SNAKE_CASE			)

		if name == SchedulerType.PIECEWISE_CONSTANT:
				return schedule_func(__SCREAMING_SNAKE_CASE ,			step_rules=__SCREAMING_SNAKE_CASE ,			last_epoch=__SCREAMING_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(__SCREAMING_SNAKE_CASE ,			num_warmup_steps=__SCREAMING_SNAKE_CASE ,			last_epoch=__SCREAMING_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(
				    __SCREAMING_SNAKE_CASE ,			num_warmup_steps=__SCREAMING_SNAKE_CASE ,			num_training_steps=__SCREAMING_SNAKE_CASE ,			num_cycles=__SCREAMING_SNAKE_CASE ,			last_epoch=__SCREAMING_SNAKE_CASE ,			)

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

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





"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Tuple   				=      list(range(0 ,			__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Optional[Any]   				=      [item for sublist in list(device_map.values()			) for item in sublist]

		# Duplicate check
		__lowerCAmelCase:	List[Any]   				=      []
		for i in device_map_blocks:
				if device_map_blocks.count(__SCREAMING_SNAKE_CASE			) > 1 and i not in duplicate_blocks:
						duplicate_blocks.append(__SCREAMING_SNAKE_CASE			)
    # Missing blocks
		__lowerCAmelCase:	Optional[Any]   				=      [i for i in blocks if i not in device_map_blocks]
		__lowerCAmelCase:	List[Any]   				=      [i for i in device_map_blocks if i not in blocks]

		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."
				    " These attention blocks were specified more than once: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "There are attention blocks for this model that are not specified in the device_map. Add these attention "
				    "blocks to a device on the device_map: " + str(__SCREAMING_SNAKE_CASE			)			)
		if len(__SCREAMING_SNAKE_CASE			) != 0:
				raise ValueError(
				    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
				    + str(__SCREAMING_SNAKE_CASE			)			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	List[Any]   				=      list(range(__SCREAMING_SNAKE_CASE			)			)
		__lowerCAmelCase:	Dict   				=      int(ceil(n_layers / len(__SCREAMING_SNAKE_CASE			)			)			)
		__lowerCAmelCase:	Union[str, Any]   				=      [layers[i : i + n_blocks] for i in range(0 ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)]

		return dict(zip(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)			)


