





'''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


__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					__lowercase		):
		lowercase__:   List[Any]				     =   '''linear'''
		lowercase__:   Optional[int]				     =   '''cosine'''
		lowercase__:   Tuple				     =   '''cosine_with_restarts'''
		lowercase__:   str				     =   '''polynomial'''
		lowercase__:   Union[str, Any]				     =   '''constant'''
		lowercase__:   Dict				     =   '''constant_with_warmup'''
		lowercase__:   List[str]				     =   '''piecewise_constant'''







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = -1 ) ->				str:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = -1 ) ->				List[str]:



			"""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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = -1 ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      str							      =     {}
			__snake_case					:      str							      =     step_rules.split(""",""" )
			for rule_str in rule_list[:-1]:
						__snake_case      ,     __snake_case					:      str							      =     rule_str.split(""":""" )
						__snake_case					:      Optional[int]							      =     int(_lowerCamelCase )
						__snake_case					:      Tuple							      =     float(_lowerCamelCase )
						__snake_case					:      Optional[Any]							      =     value
			__snake_case					:      Optional[Any]							      =     float(rule_list[-1] )

			def create_rules_function(_lowerCamelCase    ,	_lowerCamelCase ):
						def rule_func(_lowerCamelCase ) -> float:
									__snake_case					:      str							      =     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

			__snake_case					:      Any							      =     create_rules_function(_lowerCamelCase    ,	_lowerCamelCase )

			return LambdaLR(_lowerCamelCase    ,	_lowerCamelCase    ,	last_epoch=_lowerCamelCase )







def 					_a						(       _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 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = 0.5    ,	_lowerCamelCase = -1 ) ->				List[str]:



			"""simple docstring"""


			def lr_lambda(_lowerCamelCase ):
						if current_step < num_warmup_steps:
									return float(_lowerCamelCase ) / float(max(1    ,	_lowerCamelCase ) )
						__snake_case					:      Optional[Any]							      =     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						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = 1    ,	_lowerCamelCase = -1 ) ->				str:



			"""simple docstring"""


			def lr_lambda(_lowerCamelCase ):
						if current_step < num_warmup_steps:
									return float(_lowerCamelCase ) / float(max(1    ,	_lowerCamelCase ) )
						__snake_case					:      str							      =     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						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=1E-7    ,	_lowerCamelCase=1.0    ,	_lowerCamelCase=-1 ) ->				Any:



			"""simple docstring"""


			__snake_case					:      int							      =     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:
									__snake_case					:      Tuple							      =     lr_init - lr_end
									__snake_case					:      str							      =     num_training_steps - num_warmup_steps
									__snake_case					:      str							      =     1 - (current_step - num_warmup_steps) / decay_steps
									__snake_case					:      Union[str, Any]							      =     lr_range * pct_remaining**power + lr_end
									return decay / lr_init  # as LambdaLR multiplies by lr_init

			return LambdaLR(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )


__UpperCamelCase					    =	{
    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						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None    ,	_lowerCamelCase = 1    ,	_lowerCamelCase = 1.0    ,	_lowerCamelCase = -1    ,	) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      str							      =     SchedulerType(_lowerCamelCase )
			__snake_case					:      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 )




# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


__magic_name__  							=	'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
     import torch

     print('''Torch version:''', torch.__version__)
     print('''Cuda available:''', torch.cuda.is_available())
     print('''Cuda version:''', torch.version.cuda)
     print('''CuDNN version:''', torch.backends.cudnn.version())
     print('''Number of GPUs available:''', torch.cuda.device_count())
     print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
     print('''Torch version:''', None)

try:
     import deepspeed

     print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
     print('''DeepSpeed version:''', None)

try:
     import tensorflow as tf

     print('''TensorFlow version:''', tf.__version__)
     print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
     print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
     print('''TensorFlow version:''', None)



