





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_		:				Union[str, Any]				=						logging.get_logger(__name__)


class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	List[str]								=	"linear"
						_UpperCamelCase  :	Optional[int]								=	"cosine"
						_UpperCamelCase  :	Union[str, Any]								=	"cosine_with_restarts"
						_UpperCamelCase  :	Tuple								=	"polynomial"
						_UpperCamelCase  :	Union[str, Any]								=	"constant"
						_UpperCamelCase  :	str								=	"constant_with_warmup"
						_UpperCamelCase  :	Tuple								=	"piecewise_constant"



def       A__(		__lowerCAmelCase	,			__lowerCAmelCase = -1 ):
				return LambdaLR(__lowerCAmelCase	,			lambda __lowerCAmelCase : 1	,			last_epoch=__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = -1 ):

				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 ):
				_snake_case :			str  						=   {}
				_snake_case :			Optional[int]  						=   step_rules.split(',' )
				for rule_str in rule_list[:-1]:
								_snake_case						,			_snake_case :			Tuple  						=   rule_str.split(':' )
								_snake_case :			Union[str, Any]  						=   int(__lowerCAmelCase )
								_snake_case :			Dict  						=   float(__lowerCAmelCase )
								_snake_case :			Any  						=   value
				_snake_case :			List[str]  						=   float(rule_list[-1] )

				def create_rules_function(__lowerCAmelCase	,			__lowerCAmelCase ):
								def rule_func(__lowerCAmelCase ) -> float:
												_snake_case :			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

				_snake_case :			Any  						=   create_rules_function(__lowerCAmelCase	,			__lowerCAmelCase )

				return LambdaLR(__lowerCAmelCase	,			__lowerCAmelCase	,			last_epoch=__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase=-1 ):

				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 ):

				def lr_lambda(__lowerCAmelCase ):
								if current_step < num_warmup_steps:
												return float(__lowerCAmelCase ) / float(max(1	,			__lowerCAmelCase ) )
								_snake_case :			Any  						=   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 ):

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

				return LambdaLR(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )


lowercase_		:				Optional[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	,			):
				_snake_case :			List[str]  						=   SchedulerType(__lowerCAmelCase )
				_snake_case :			Tuple  						=   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 )













import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 			lowercase				(							a_		,			a_		,			a_     ):



						"""simple docstring"""

						@register_to_config
						def __init__(    self			:		Any ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		float ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		str ,				lowerCamelCase_			:		bool = False ,				):





										'''simple docstring'''







										super().__init__()

										_snake_case :			Optional[int]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)

										_snake_case :			List[str]  						=   nn.Embedding(lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Union[str, Any]  						=   False

										_snake_case :			Tuple  						=   nn.Dropout(p=lowerCamelCase_					)

										_snake_case :			Union[str, Any]  						=   TaConfig(
										    vocab_size=lowerCamelCase_ ,				d_model=lowerCamelCase_ ,				num_heads=lowerCamelCase_ ,				d_kv=lowerCamelCase_ ,				d_ff=lowerCamelCase_ ,				dropout_rate=lowerCamelCase_ ,				feed_forward_proj=lowerCamelCase_ ,				is_decoder=lowerCamelCase_ ,				is_encoder_decoder=lowerCamelCase_ ,				)

										_snake_case :			Union[str, Any]  						=   nn.ModuleList()
										for lyr_num in range(lowerCamelCase_					):
														_snake_case :			Any  						=   TaBlock(lowerCamelCase_					)
														self.encoders.append(lowerCamelCase_					)

										_snake_case :			Tuple  						=   TaLayerNorm(lowerCamelCase_					)
										_snake_case :			List[str]  						=   nn.Dropout(p=lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		int ,				lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   self.token_embedder(lowerCamelCase_					)

										_snake_case :			List[Any]  						=   encoder_input_tokens.shape[1]
										_snake_case :			Any  						=   torch.arange(lowerCamelCase_ ,				device=encoder_input_tokens.device					)
										x += self.position_encoding(lowerCamelCase_					)

										_snake_case :			Tuple  						=   self.dropout_pre(lowerCamelCase_					)

										# inverted the attention mask
										_snake_case :			Dict  						=   encoder_input_tokens.size()
										_snake_case :			Optional[int]  						=   self.get_extended_attention_mask(lowerCamelCase_ ,				lowerCamelCase_					)

										for lyr in self.encoders:
														_snake_case :			str  						=   lyr(lowerCamelCase_ ,				lowerCamelCase_					)[0]
										_snake_case :			Any  						=   self.layer_norm(lowerCamelCase_					)

										return self.dropout_post(lowerCamelCase_					), encoder_inputs_mask






