


'''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__       (							a__						):





							'''simple docstring'''






							lowercase__    :     str					=   "linear"
							lowercase__    :     List[str]					=   "cosine"
							lowercase__    :     List[str]					=   "cosine_with_restarts"
							lowercase__    :     List[str]					=   "polynomial"
							lowercase__    :     int					=   "constant"
							lowercase__    :     Optional[int]					=   "constant_with_warmup"
							lowercase__    :     List[Any]					=   "piecewise_constant"


def       _snake_case     (  A				,					A = -1 )		->					Optional[int]:
							return LambdaLR(A				,					lambda A : 1				,					last_epoch=A )


def       _snake_case     (  A				,					A				,					A = -1 )		->					Any:

							def lr_lambda(A ):
														if current_step < num_warmup_steps:
																					return float(A ) / float(max(1.0				,					A ) )
														return 1.0

							return LambdaLR(A				,					A				,					last_epoch=A )


def       _snake_case     (  A				,					A				,					A = -1 )		->					Tuple:
							lowerCAmelCase__      =			{}
							lowerCAmelCase__      =			step_rules.split(''',''' )
							for rule_str in rule_list[:-1]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			rule_str.split(''':''' )
														lowerCAmelCase__      =			int(A )
														lowerCAmelCase__      =			float(A )
														lowerCAmelCase__      =			value
							lowerCAmelCase__      =			float(rule_list[-1] )

							def create_rules_function(A				,					A ):
														def rule_func(A ) -> float:
																					lowerCAmelCase__      =			sorted(rules_dict.keys() )
																					for i, sorted_step in enumerate(A ):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							lowerCAmelCase__      =			create_rules_function(A				,					A )

							return LambdaLR(A				,					A				,					last_epoch=A )


def       _snake_case     (  A				,					A				,					A				,					A=-1 )		->					int:

							def lr_lambda(A ):
														if current_step < num_warmup_steps:
																					return float(A ) / float(max(1				,					A ) )
														return max(
														    0.0				,					float(num_training_steps - current_step ) / float(max(1				,					num_training_steps - num_warmup_steps ) ) )

							return LambdaLR(A				,					A				,					A )


def       _snake_case     (  A				,					A				,					A				,					A = 0.5				,					A = -1 )		->					Optional[int]:

							def lr_lambda(A ):
														if current_step < num_warmup_steps:
																					return float(A ) / float(max(1				,					A ) )
														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(A ) * 2.0 * progress )) )

							return LambdaLR(A				,					A				,					A )


def       _snake_case     (  A				,					A				,					A				,					A = 1				,					A = -1 )		->					Dict:

							def lr_lambda(A ):
														if current_step < num_warmup_steps:
																					return float(A ) / float(max(1				,					A ) )
														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(A ) * progress) % 1.0) )) )

							return LambdaLR(A				,					A				,					A )


def       _snake_case     (  A				,					A				,					A				,					A=1E-7				,					A=1.0				,					A=-1 )		->					List[Any]:
							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(A ):
														if current_step < num_warmup_steps:
																					return float(A ) / float(max(1				,					A ) )
														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(A				,					A				,					A )


__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       _snake_case     (  A				,					A				,					A = None				,					A = None				,					A = None				,					A = 1				,					A = 1.0				,					A = -1				,					)		->					Dict:
							lowerCAmelCase__      =			SchedulerType(A )
							lowerCAmelCase__      =			TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(A				,					last_epoch=A )

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(A				,					step_rules=A				,					last_epoch=A )

							# 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(A				,					num_warmup_steps=A				,					last_epoch=A )

							# 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(
														    A				,					num_warmup_steps=A				,					num_training_steps=A				,					num_cycles=A				,					last_epoch=A				,					)

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    A				,					num_warmup_steps=A				,					num_training_steps=A				,					power=A				,					last_epoch=A				,					)

							return schedule_func(
							    A				,					num_warmup_steps=A				,					num_training_steps=A				,					last_epoch=A )



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig