



'''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	:		Dict				=   logging.get_logger(__name__)






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[Any]        =							"""linear"""
					a      :     Optional[Any]        =							"""cosine"""
					a      :     Optional[Any]        =							"""cosine_with_restarts"""
					a      :     Union[str, Any]        =							"""polynomial"""
					a      :     Dict        =							"""constant"""
					a      :     List[str]        =							"""constant_with_warmup"""
					a      :     Any        =							"""piecewise_constant"""



def   __lowerCAmelCase					(							lowerCamelCase   :						Optimizer				,		lowerCamelCase   :						int = -1					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						Optimizer				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int = -1					):






	'''simple docstring'''




	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   __lowerCAmelCase					(							lowerCamelCase   :						Optimizer				,		lowerCamelCase   :						str				,		lowerCamelCase   :						int = -1					):






	'''simple docstring'''




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

	def create_rules_function(lowerCamelCase   :						Dict				,		lowerCamelCase   :						Dict					):
		def rule_func(lowerCamelCase   :						int					) -> float:
			__lowerCAmelCase												=  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

	__lowerCAmelCase												=  create_rules_function(lowerCamelCase				,		lowerCamelCase					)

	return LambdaLR(lowerCamelCase				,		lowerCamelCase				,		last_epoch=lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						str				,		lowerCamelCase   :						List[str]				,		lowerCamelCase   :						int=-1					):






	'''simple docstring'''




	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   __lowerCAmelCase					(							lowerCamelCase   :						Optimizer				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int				,		lowerCamelCase   :						float = 0.5				,		lowerCamelCase   :						int = -1					):






	'''simple docstring'''




	def lr_lambda(lowerCamelCase   :						Tuple					):
		if current_step < num_warmup_steps:
			return float(lowerCamelCase					) / float(max(1				,		lowerCamelCase					)					)
		__lowerCAmelCase												=  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   :						Optimizer				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int = 1				,		lowerCamelCase   :						int = -1					):






	'''simple docstring'''




	def lr_lambda(lowerCamelCase   :						List[Any]					):
		if current_step < num_warmup_steps:
			return float(lowerCamelCase					) / float(max(1				,		lowerCamelCase					)					)
		__lowerCAmelCase												=  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   :						List[str]				,		lowerCamelCase   :						Any				,		lowerCamelCase   :						int				,		lowerCamelCase   :						Optional[Any]=1e-7				,		lowerCamelCase   :						str=1.0				,		lowerCamelCase   :						str=-1					):






	'''simple docstring'''




	__lowerCAmelCase												=  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:
			__lowerCAmelCase												=  lr_init - lr_end
			__lowerCAmelCase												=  num_training_steps - num_warmup_steps
			__lowerCAmelCase												=  1 - (current_step - num_warmup_steps) / decay_steps
			__lowerCAmelCase												=  lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)


lowerCAmelCase	:		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   __lowerCAmelCase					(							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				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  SchedulerType(lowerCamelCase					)
	__lowerCAmelCase												=  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'''



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase	:		List[str]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Dict[Optional[str], Type[Formatter]]				=   {}
lowerCAmelCase	:		Dict[Optional[str], str]				=   {}
lowerCAmelCase	:		Dict[Optional[str], Exception]				=   {}



def   __lowerCAmelCase					(							lowerCamelCase   :						type				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''					)
	__lowerCAmelCase												=  formatter_cls
	for alias in set(aliases + [format_type]					):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''					)
		__lowerCAmelCase												=  format_type



def   __lowerCAmelCase					(							lowerCamelCase   :						Exception				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	for alias in set(aliases + [format_type]					):
		__lowerCAmelCase												=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	lowerCAmelCase	:		Optional[int]				=   ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	lowerCAmelCase	:		str				=   ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	lowerCAmelCase	:		Any				=   ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]					):






	'''simple docstring'''




	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		**lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_format_type_from_alias(lowerCamelCase					)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowerCamelCase					)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None					)}, but got \'{format_type}\''''					)