



'''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_							: str						 =	logging.get_logger(__name__)



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """linear"""
 UpperCAmelCase							=  """cosine"""
 UpperCAmelCase							=  """cosine_with_restarts"""
 UpperCAmelCase							=  """polynomial"""
 UpperCAmelCase							=  """constant"""
 UpperCAmelCase							=  """constant_with_warmup"""
 UpperCAmelCase							=  """piecewise_constant"""







def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_ = -1		)->		List[Any]:





       '''simple docstring'''


       return LambdaLR(__lowerCamelCase       ,   lambda lowerCAmelCase_		: 1       ,   last_epoch=__lowerCamelCase		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = -1		)->		Tuple:





       '''simple docstring'''


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

       return LambdaLR(__lowerCamelCase       ,   __lowerCamelCase       ,   last_epoch=__lowerCamelCase		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = -1		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = {}
       _UpperCAmelCase  :						Any			     = step_rules.split(""","""		)
       for rule_str in rule_list[:-1]:
              _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = rule_str.split(""":"""		)
              _UpperCAmelCase  :						List[Any]			     = int(__lowerCamelCase		)
              _UpperCAmelCase  :						Optional[Any]			     = float(__lowerCamelCase		)
              _UpperCAmelCase  :						Any			     = value
       _UpperCAmelCase  :						str			     = float(rule_list[-1]		)

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

              return rule_func

       _UpperCAmelCase  :						str			     = create_rules_function(__lowerCamelCase       ,   __lowerCamelCase		)

       return LambdaLR(__lowerCamelCase       ,   __lowerCamelCase       ,   last_epoch=__lowerCamelCase		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=-1		)->		List[str]:





       '''simple docstring'''


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

       return LambdaLR(__lowerCamelCase       ,   __lowerCamelCase       ,   __lowerCamelCase		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = 0.5       ,   lowerCAmelCase_ = -1		)->		List[Any]:





       '''simple docstring'''


       def lr_lambda(lowerCAmelCase_		):
              if current_step < num_warmup_steps:
                     return float(__lowerCamelCase		) / float(max(1       ,   __lowerCamelCase		)		)
              _UpperCAmelCase  :						Dict			     = 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(__lowerCamelCase		) * 2.0 * progress		))		)

       return LambdaLR(__lowerCamelCase       ,   __lowerCamelCase       ,   __lowerCamelCase		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = 1       ,   lowerCAmelCase_ = -1		)->		Optional[int]:





       '''simple docstring'''


       def lr_lambda(lowerCAmelCase_		):
              if current_step < num_warmup_steps:
                     return float(__lowerCamelCase		) / float(max(1       ,   __lowerCamelCase		)		)
              _UpperCAmelCase  :						Dict			     = 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(__lowerCamelCase		) * progress) % 1.0)		))		)

       return LambdaLR(__lowerCamelCase       ,   __lowerCamelCase       ,   __lowerCamelCase		)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=1e-7       ,   lowerCAmelCase_=1.0       ,   lowerCAmelCase_=-1		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = 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(lowerCAmelCase_		):
              if current_step < num_warmup_steps:
                     return float(__lowerCamelCase		) / float(max(1       ,   __lowerCamelCase		)		)
              elif current_step > num_training_steps:
                     return lr_end / lr_init  # as LambdaLR multiplies by lr_init
              else:
                     _UpperCAmelCase  :						Optional[Any]			     = lr_init - lr_end
                     _UpperCAmelCase  :						Union[str, Any]			     = num_training_steps - num_warmup_steps
                     _UpperCAmelCase  :						Union[str, Any]			     = 1 - (current_step - num_warmup_steps) / decay_steps
                     _UpperCAmelCase  :						List[str]			     = lr_range * pct_remaining**power + lr_end
                     return decay / lr_init  # as LambdaLR multiplies by lr_init

       return LambdaLR(__lowerCamelCase       ,   __lowerCamelCase       ,   __lowerCamelCase		)


A_							: List[Any]						 =	{
    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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_ = None       ,   lowerCAmelCase_ = None       ,   lowerCAmelCase_ = None       ,   lowerCAmelCase_ = 1       ,   lowerCAmelCase_ = 1.0       ,   lowerCAmelCase_ = -1       ,   )->		Dict:





       '''simple docstring'''


       _UpperCAmelCase  :						List[Any]			     = SchedulerType(__lowerCamelCase		)
       _UpperCAmelCase  :						List[Any]			     = TYPE_TO_SCHEDULER_FUNCTION[name]
       if name == SchedulerType.CONSTANT:
              return schedule_func(__lowerCamelCase       ,   last_epoch=__lowerCamelCase		)

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

       # 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(__lowerCamelCase       ,   num_warmup_steps=__lowerCamelCase       ,   last_epoch=__lowerCamelCase		)

       # 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(
                  __lowerCamelCase       ,   num_warmup_steps=__lowerCamelCase       ,   num_training_steps=__lowerCamelCase       ,   num_cycles=__lowerCamelCase       ,   last_epoch=__lowerCamelCase       ,   )

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

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









import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp





def 		__A		(	)      ->       Any:
	a										=							ArgumentParser(
	    description=(
	        """PyTorch TPU distributed training launch """
	        """helper utility that will spawn up """
	        """multiple distributed processes"""
	    )    )

	# Optional arguments for the launch helper
	parser.add_argument("""--num_cores"""							,			type=__lowerCamelCase							,			default=1							,			help="""Number of TPU cores to use (1 or 8)."""    )

	# positional
	parser.add_argument(
	    """training_script"""							,			type=__lowerCamelCase							,			help=(
	        """The full path to the single TPU training """
	        """program/script to be launched in parallel, """
	        """followed by all the arguments for the """
	        """training script"""
	    )							,			)

	# rest from the training program
	parser.add_argument("""training_script_args"""							,			nargs=__lowerCamelCase    )

	return parser.parse_args()





def 		__A		(	)      ->       Union[str, Any]:
	a										=							parse_args()

	# Import training_script as a module.
	a										=							Path(args.training_script    )
	sys.path.append(str(script_fpath.parent.resolve()    )    )
	a										=							script_fpath.stem
	a										=							importlib.import_module(__lowerCamelCase    )

	# Patch sys.argv
	a										=							[args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores    )]
	xmp.spawn(mod._mp_fn							,			args=()							,			nprocs=args.num_cores    )


if __name__ == "__main__":
		main()

