





"""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


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"linear"
						UpperCAmelCase_ =			"cosine"
						UpperCAmelCase_ =			"cosine_with_restarts"
						UpperCAmelCase_ =			"polynomial"
						UpperCAmelCase_ =			"constant"
						UpperCAmelCase_ =			"constant_with_warmup"
						UpperCAmelCase_ =			"piecewise_constant"



def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = -1    ):




		"""simple docstring"""


		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    ):




		"""simple docstring"""


		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    ):




		"""simple docstring"""


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

		def create_rules_function(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				def rule_func(_SCREAMING_SNAKE_CASE    ) -> float:
						UpperCamelCase   =	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

		UpperCamelCase   =	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    ):




		"""simple docstring"""


		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    ):




		"""simple docstring"""


		def lr_lambda(_SCREAMING_SNAKE_CASE    ):
				if current_step < num_warmup_steps:
						return float(_SCREAMING_SNAKE_CASE    ) / float(max(1  ,	_SCREAMING_SNAKE_CASE    )    )
				UpperCamelCase   =	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    ):




		"""simple docstring"""


		def lr_lambda(_SCREAMING_SNAKE_CASE    ):
				if current_step < num_warmup_steps:
						return float(_SCREAMING_SNAKE_CASE    ) / float(max(1  ,	_SCREAMING_SNAKE_CASE    )    )
				UpperCamelCase   =	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    ):




		"""simple docstring"""


		UpperCamelCase   =	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:
						UpperCamelCase   =	lr_init - lr_end
						UpperCamelCase   =	num_training_steps - num_warmup_steps
						UpperCamelCase   =	1 - (current_step - num_warmup_steps) / decay_steps
						UpperCamelCase   =	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    )


lowerCAmelCase__     			=			{
    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  ,	):




		"""simple docstring"""


		UpperCamelCase   =	SchedulerType(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	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"""





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






