



"""simple docstring"""

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


lowercase__			   =  logging.get_logger(__name__)




@dataclass
class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					[
						    "no_inference",
						    "no_cuda",
						    "no_tpu",
						    "no_speed",
						    "no_memory",
						    "no_env_print",
						    "no_multi_process",
						]



						def __init__(self						,     **_lowercase							):





													'''simple docstring'''

													for deprecated_arg in self.deprecated_args:
																				if deprecated_arg in kwargs:
																											__a		:		Optional[Any]									=       deprecated_arg[3:]
																											__a		:		Optional[int]									=       not kwargs.pop(_lowercase							)
																											logger.warning(
																											    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
																											    F''' {positive_arg}={kwargs[positive_arg]}'''							)
													__a		:		Union[str, Any]									=       kwargs.pop("""tpu_name"""						,     self.tpu_name							)
													__a		:		List[Any]									=       kwargs.pop("""device_idx"""						,     self.device_idx							)
													__a		:		Optional[Any]									=       kwargs.pop("""eager_mode"""						,     self.eager_mode							)
													__a		:		Tuple									=       kwargs.pop("""use_xla"""						,     self.use_xla							)
													super().__init__(**_lowercase							)

						_lowerCAmelCase							=					field(
						    default=__snake_case     ,					metadata={"help": "Name of TPU"}     ,					)
						_lowerCAmelCase							=					field(
						    default=0     ,					metadata={"help": "CPU / GPU device index. Defaults to 0."}     ,					)
						_lowerCAmelCase							=					field(default=__snake_case     ,					metadata={"help": "Benchmark models in eager model."}      )
						_lowerCAmelCase							=					field(
						    default=__snake_case     ,					metadata={
						        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
						    }     ,					)



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													requires_backends(self						,     ["""tf"""]							)
													__a		:		List[Any]									=       None
													if self.tpu:
																				try:
																											if self.tpu_name:
																																		__a		:		List[str]									=       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name							)
																											else:
																																		__a		:		List[Any]									=       tf.distribute.cluster_resolver.TPUClusterResolver()
																				except ValueError:
																											__a		:		List[Any]									=       None
													return tpu



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													requires_backends(self						,     ["""tf"""]							)
													if self.is_tpu:
																				tf.config.experimental_connect_to_cluster(self._setup_tpu							)
																				tf.tpu.experimental.initialize_tpu_system(self._setup_tpu							)

																				__a		:		Optional[Any]									=       tf.distribute.TPUStrategy(self._setup_tpu							)
													else:
																				# currently no multi gpu is allowed
																				if self.is_gpu:
																											# TODO: Currently only single GPU is supported
																											tf.config.set_visible_devices(self.gpu_list[self.device_idx]						,     """GPU"""							)
																											__a		:		Union[str, Any]									=       tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''							)
																				else:
																											tf.config.set_visible_devices([]						,     """GPU"""							)  # disable GPU
																											__a		:		Optional[Any]									=       tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''							)

													return strategy



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													requires_backends(self						,     ["""tf"""]							)
													return self._setup_tpu is not None



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													requires_backends(self						,     ["""tf"""]							)
													return self._setup_strategy



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													requires_backends(self						,     ["""tf"""]							)
													return tf.config.list_physical_devices("""GPU"""							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													requires_backends(self						,     ["""tf"""]							)
													if self.cuda:
																				return len(self.gpu_list							)
													return 0





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.n_gpu > 0






"""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


lowercase__			   =  logging.get_logger(__name__)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"linear"
						_lowerCAmelCase							=					"cosine"
						_lowerCAmelCase							=					"cosine_with_restarts"
						_lowerCAmelCase							=					"polynomial"
						_lowerCAmelCase							=					"constant"
						_lowerCAmelCase							=					"constant_with_warmup"
						_lowerCAmelCase							=					"piecewise_constant"







def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int = -1					):
							return LambdaLR(_lowerCamelCase  ,       lambda _lowerCamelCase					: 1  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: int					):
														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       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int = -1					):
							__a		:		Optional[int]									=       {}
							__a		:		Any									=       step_rules.split(""","""					)
							for rule_str in rule_list[:-1]:
														__a							,     __a		:		int									=       rule_str.split(""":"""					)
														__a		:		Optional[int]									=       int(_lowerCamelCase					)
														__a		:		str									=       float(_lowerCamelCase					)
														__a		:		int									=       value
							__a		:		Dict									=       float(rule_list[-1]					)

							def create_rules_function(_lowerCamelCase				: str  ,       _lowerCamelCase				: Tuple					):
														def rule_func(_lowerCamelCase				: int					) -> float:
																					__a		:		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

							__a		:		Optional[int]									=       create_rules_function(_lowerCamelCase  ,       _lowerCamelCase					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str=-1					):

							def lr_lambda(_lowerCamelCase				: int					):
														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       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: float = 0.5  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Any					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		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       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Optional[int]					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[Any]=1E-7  ,       _lowerCamelCase				: Optional[int]=1.0  ,       _lowerCamelCase				: Optional[int]=-1					):
							__a		:		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(_lowerCamelCase				: int					):
														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:
																					__a		:		Tuple									=       lr_init - lr_end
																					__a		:		int									=       num_training_steps - num_warmup_steps
																					__a		:		Optional[int]									=       1 - (current_step - num_warmup_steps) / decay_steps
																					__a		:		List[str]									=       lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)


lowercase__			   =  {
    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       __magic_name__    (   _lowerCamelCase				: Union[str, SchedulerType]  ,       _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: Optional[str] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: float = 1.0  ,       _lowerCamelCase				: int = -1  ,       ):
							__a		:		int									=       SchedulerType(_lowerCamelCase					)
							__a		:		Optional[int]									=       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					)

