



'''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 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Dict   				=					'linear'
							lowerCAmelCase       :    Union[str, Any]   				=					'cosine'
							lowerCAmelCase       :    Dict   				=					'cosine_with_restarts'
							lowerCAmelCase       :    Dict   				=					'polynomial'
							lowerCAmelCase       :    str   				=					'constant'
							lowerCAmelCase       :    Tuple   				=					'constant_with_warmup'
							lowerCAmelCase       :    List[str]   				=					'piecewise_constant'



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = -1  )			->      Union[str, Any]:
		return LambdaLR(lowerCAmelCase_						,			lambda lowerCAmelCase_  : 1						,			last_epoch=lowerCAmelCase_  )



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

		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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = -1  )			->      List[str]:
		_a     :      List[str]        =							{}
		_a     :      str        =							step_rules.split(','  )
		for rule_str in rule_list[:-1]:
				_a       ,					_a     :      Optional[int]        =							rule_str.split(':'  )
				_a     :      Union[str, Any]        =							int(lowerCAmelCase_  )
				_a     :      int        =							float(lowerCAmelCase_  )
				_a     :      str        =							value
		_a     :      str        =							float(rule_list[-1]  )

		def create_rules_function(lowerCAmelCase_						,			lowerCAmelCase_  ):
				def rule_func(lowerCAmelCase_  ) -> float:
						_a     :      Tuple        =							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[Any]        =							create_rules_function(lowerCAmelCase_						,			lowerCAmelCase_  )

		return LambdaLR(lowerCAmelCase_						,			lowerCAmelCase_						,			last_epoch=lowerCAmelCase_  )



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

		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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = 0.5						,			lowerCAmelCase_ = -1  )			->      Optional[Any]:

		def lr_lambda(lowerCAmelCase_  ):
				if current_step < num_warmup_steps:
						return float(lowerCAmelCase_  ) / float(max(1						,			lowerCAmelCase_  )  )
				_a     :      List[str]        =							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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = 1						,			lowerCAmelCase_ = -1  )			->      str:

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

		return LambdaLR(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )


__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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = 1						,			lowerCAmelCase_ = 1.0						,			lowerCAmelCase_ = -1						,			)			->      Any:
		_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_  )









'''simple docstring'''

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowerCAmelCase									=   logging.getLogger()

@unittest.skip('Temporarily disable the doc tests.'   )
@require_torch
@require_tf
@slow
class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Path	,_UpperCAmelCase					:       Union[str, None] = None	,_UpperCAmelCase					:       Union[List[str], None] = None	,_UpperCAmelCase					:       Union[str, List[str], None] = None	,_UpperCAmelCase					:       bool = True	,):
									_a     :      Dict        =							[file for file in os.listdir(_UpperCAmelCase		) if os.path.isfile(os.path.join(_UpperCAmelCase	,_UpperCAmelCase		)		)]

									if identifier is not None:
											_a     :      str        =							[file for file in files if identifier in file]

									if n_identifier is not None:
											if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													for n_ in n_identifier:
															_a     :      int        =							[file for file in files if n_ not in file]
											else:
													_a     :      Optional[Any]        =							[file for file in files if n_identifier not in file]

									_a     :      Dict        =							ignore_files or []
									ignore_files.append('__init__.py'		)
									_a     :      List[str]        =							[file for file in files if file not in ignore_files]

									for file in files:
											# Open all files
											print('Testing'	,_UpperCAmelCase		)

											if only_modules:
													_a     :      Any        =							file.split('.'		)[0]
													try:
															_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,_UpperCAmelCase		)
															_a     :      Dict        =							doctest.DocTestSuite(_UpperCAmelCase		)
															_a     :      Optional[int]        =							unittest.TextTestRunner().run(_UpperCAmelCase		)
															self.assertIs(len(result.failures		)	,0		)
													except AttributeError:
															logger.info(F"""{module_identifier} is not a module."""		)
											else:
													_a     :      str        =							doctest.testfile(str('..' / directory / file		)	,optionflags=doctest.ELLIPSIS		)
													self.assertIs(result.failed	,0		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Optional[Any]        =							Path('src/transformers'		)
									_a     :      Optional[Any]        =							'modeling'
									_a     :      Union[str, Any]        =							[
									    'modeling_ctrl.py',
									    'modeling_tf_ctrl.py',
									]
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase	,ignore_files=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      str        =							Path('src/transformers'		)
									_a     :      List[str]        =							'tokenization'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      Any        =							Path('src/transformers'		)
									_a     :      str        =							'configuration'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Tuple        =							Path('src/transformers'		)
									_a     :      Optional[int]        =							['configuration', 'modeling', 'tokenization']
									self.analyze_directory(_UpperCAmelCase	,n_identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Union[str, Any]        =							Path('docs/source'		)
									_a     :      List[str]        =							['favicon.ico']
									self.analyze_directory(_UpperCAmelCase	,ignore_files=_UpperCAmelCase	,only_modules=_UpperCAmelCase		)




