

"""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_							:			Any											= logging.get_logger(__name__)





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

def 	A       (     snake_case__		,				snake_case__ = -1    ):




			'''simple docstring'''


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

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




			'''simple docstring'''


			def lr_lambda(snake_case__    ):
						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 	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__								=      rule_str.split(""":"""    )
						SCREAMING_SNAKE_CASE__								=      int(_lowerCamelCase    )
						SCREAMING_SNAKE_CASE__								=      float(_lowerCamelCase    )
						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(_lowerCamelCase    ):
												if steps < sorted_step:
															return rules_dict[sorted_steps[i]]
									return last_lr_multiple

						return rule_func

			SCREAMING_SNAKE_CASE__								=      create_rules_function(_lowerCamelCase		,				_lowerCamelCase    )

			return LambdaLR(_lowerCamelCase		,				_lowerCamelCase		,				last_epoch=_lowerCamelCase    )

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(_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 	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(_lowerCamelCase    ) / float(max(1		,				_lowerCamelCase    )    )
						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(_lowerCamelCase    ) * 2.0 * progress    ))    )

			return LambdaLR(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase    )

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(_lowerCamelCase    ) / float(max(1		,				_lowerCamelCase    )    )
						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(_lowerCamelCase    ) * progress) % 1.0)    ))    )

			return LambdaLR(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase    )

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(_lowerCamelCase    ) / float(max(1		,				_lowerCamelCase    )    )
						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(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase    )


A_							:			Union[str, 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 	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(_lowerCamelCase    )
			SCREAMING_SNAKE_CASE__								=      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    )







"""simple docstring"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


