




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					:       str    					=      logging.get_logger(__name__)

class 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  '''linear'''
							UpperCamelCase__					     =  '''cosine'''
							UpperCamelCase__					     =  '''cosine_with_restarts'''
							UpperCamelCase__					     =  '''polynomial'''
							UpperCamelCase__					     =  '''constant'''
							UpperCamelCase__					     =  '''constant_with_warmup'''
							UpperCamelCase__					     =  '''piecewise_constant'''







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = -1    )      ->       List[Any]:
	return LambdaLR(__lowerCamelCase							,			lambda __lowerCamelCase    : 1							,			last_epoch=__lowerCamelCase    )





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = -1    )      ->       Tuple:

	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    )      ->       str:
	a										=							{}
	a										=							step_rules.split(""","""    )
	for rule_str in rule_list[:-1]:
		a				, a										=							rule_str.split(""":"""    )
		a										=							int(__lowerCamelCase    )
		a										=							float(__lowerCamelCase    )
		a										=							value
	a										=							float(rule_list[-1]    )

	def create_rules_function(__lowerCamelCase							,			__lowerCamelCase    ):
		def rule_func(__lowerCamelCase    ) -> float:
			a										=							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										=							create_rules_function(__lowerCamelCase							,			__lowerCamelCase    )

	return LambdaLR(__lowerCamelCase							,			__lowerCamelCase							,			last_epoch=__lowerCamelCase    )





def 		__A		(	__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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = 0.5							,			__lowerCamelCase = -1    )      ->       List[Any]:

	def lr_lambda(__lowerCamelCase    ):
		if current_step < num_warmup_steps:
			return float(__lowerCamelCase    ) / float(max(1							,			__lowerCamelCase    )    )
		a										=							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    )      ->       Optional[int]:

	def lr_lambda(__lowerCamelCase    ):
		if current_step < num_warmup_steps:
			return float(__lowerCamelCase    ) / float(max(1							,			__lowerCamelCase    )    )
		a										=							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    )      ->       List[str]:
	a										=							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										=							lr_init - lr_end
			a										=							num_training_steps - num_warmup_steps
			a										=							1 - (current_step - num_warmup_steps) / decay_steps
			a										=							lr_range * pct_remaining**power + lr_end
			return decay / lr_init  # as LambdaLR multiplies by lr_init

	return LambdaLR(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )


__UpperCamelCase					:       List[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		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			__lowerCamelCase = None							,			__lowerCamelCase = None							,			__lowerCamelCase = 1							,			__lowerCamelCase = 1.0							,			__lowerCamelCase = -1							,			)      ->       Dict:
	a										=							SchedulerType(__lowerCamelCase    )
	a										=							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    )







# 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


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

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None							,			)      ->       Optional[int]:
	a										=							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__})'    )
	a										=							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})'    )
		a										=							format_type





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = None    )      ->       List[str]:
	a										=							aliases if aliases is not None else []
	for alias in set(aliases + [format_type]    ):
		a										=							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:
		__UpperCamelCase					:       str    					=      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:
		__UpperCamelCase					:       List[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:
		__UpperCamelCase					:       List[str]    					=      ValueError("JAX needs to be installed to be able to return JAX arrays.")
		_register_unavailable_formatter(_jax_error, "jax", aliases=[])





def 		__A		(	__lowerCamelCase    )      ->       Optional[str]:
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type





def 		__A		(	__lowerCamelCase							,			**__lowerCamelCase    )      ->       Formatter:
	a										=							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}\''    )

