





'''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


lowercase__		:     Tuple			=							logging.get_logger(__name__)
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'linear'
       _snake_case      :	Dict									=       'cosine'
       _snake_case      :	Optional[Any]									=       'cosine_with_restarts'
       _snake_case      :	List[Any]									=       'polynomial'
       _snake_case      :	Optional[int]									=       'constant'
       _snake_case      :	Tuple									=       'constant_with_warmup'
       _snake_case      :	int									=       'piecewise_constant'







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int = -1 )						->						List[str]:





     """simple docstring"""

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







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int,   lowercase   :	int = -1 )						->						Optional[Any]:





     """simple docstring"""

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

     return LambdaLR(lowercase,   lowercase,   last_epoch=lowercase )







def    a__							(							lowercase   :	Optimizer,   lowercase   :	str,   lowercase   :	int = -1 )						->						Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase							       =			{}
     _UpperCamelCase							       =			step_rules.split(''',''' )
     for rule_str in rule_list[:-1]:
          _UpperCamelCase       ,							_UpperCamelCase							       =			rule_str.split(''':''' )
          _UpperCamelCase							       =			int(lowercase )
          _UpperCamelCase							       =			float(lowercase )
          _UpperCamelCase							       =			value
     _UpperCamelCase							       =			float(rule_list[-1] )

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

          return rule_func

     _UpperCamelCase							       =			create_rules_function(lowercase,   lowercase )

     return LambdaLR(lowercase,   lowercase,   last_epoch=lowercase )







def    a__							(							lowercase   :	Optional[int],   lowercase   :	Union[str, Any],   lowercase   :	str,   lowercase   :	List[str]=-1 )						->						Optional[Any]:





     """simple docstring"""

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

     return LambdaLR(lowercase,   lowercase,   lowercase )







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int,   lowercase   :	int,   lowercase   :	float = 0.5,   lowercase   :	int = -1 )						->						Tuple:





     """simple docstring"""

     def lr_lambda(lowercase   :	str ):
          if current_step < num_warmup_steps:
               return float(lowercase ) / float(max(1,   lowercase ) )
          _UpperCamelCase							       =			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(lowercase ) * 2.0 * progress )) )

     return LambdaLR(lowercase,   lowercase,   lowercase )







def    a__							(							lowercase   :	Optimizer,   lowercase   :	int,   lowercase   :	int,   lowercase   :	int = 1,   lowercase   :	int = -1 )						->						Optional[int]:





     """simple docstring"""

     def lr_lambda(lowercase   :	List[str] ):
          if current_step < num_warmup_steps:
               return float(lowercase ) / float(max(1,   lowercase ) )
          _UpperCamelCase							       =			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(lowercase ) * progress) % 1.0) )) )

     return LambdaLR(lowercase,   lowercase,   lowercase )







def    a__							(							lowercase   :	int,   lowercase   :	List[Any],   lowercase   :	Any,   lowercase   :	List[str]=1e-7,   lowercase   :	List[Any]=1.0,   lowercase   :	Any=-1 )						->						List[Any]:





     """simple docstring"""

     _UpperCamelCase							       =			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(lowercase   :	int ):
          if current_step < num_warmup_steps:
               return float(lowercase ) / float(max(1,   lowercase ) )
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               _UpperCamelCase							       =			lr_init - lr_end
               _UpperCamelCase							       =			num_training_steps - num_warmup_steps
               _UpperCamelCase							       =			1 - (current_step - num_warmup_steps) / decay_steps
               _UpperCamelCase							       =			lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(lowercase,   lowercase,   lowercase )


lowercase__		:     List[str]			=							{
    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__							(							lowercase   :	Union[str, SchedulerType],   lowercase   :	Optimizer,   lowercase   :	Optional[str] = None,   lowercase   :	Optional[int] = None,   lowercase   :	Optional[int] = None,   lowercase   :	int = 1,   lowercase   :	float = 1.0,   lowercase   :	int = -1,   )						->						Any:





     """simple docstring"""

     _UpperCamelCase							       =			SchedulerType(lowercase )
     _UpperCamelCase							       =			TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(lowercase,   last_epoch=lowercase )

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

     # 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(lowercase,   num_warmup_steps=lowercase,   last_epoch=lowercase )

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

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

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










'''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


lowercase__		:     int			=							logging.get_logger(__name__)

lowercase__		:     Any			=							'T5Config'







def    a__							(							lowercase   :	jnp.array,   lowercase   :	int,   lowercase   :	int )						->						jnp.ndarray:





     """simple docstring"""

     _UpperCamelCase							       =			jnp.zeros_like(lowercase )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
     _UpperCamelCase							       =			shifted_input_ids.at[:, 0].set(lowercase )

     _UpperCamelCase							       =			jnp.where(shifted_input_ids == -100,   lowercase,   lowercase )
     return shifted_input_ids
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'mt5'
       _snake_case      :	Union[str, Any]									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'mt5'
       _snake_case      :	int									=       MTaConfig

class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Optional[int]									=       'mt5'
       _snake_case      :	Optional[Any]									=       MTaConfig



