"""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


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)







class        UpperCamelCase		(			__a  ):
   lowercase							=  'linear'
   lowercase							=  'cosine'
   lowercase							=  'cosine_with_restarts'
   lowercase							=  'polynomial'
   lowercase							=  'constant'
   lowercase							=  'constant_with_warmup'
   lowercase							=  'piecewise_constant'






def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optimizer     ,			__SCREAMING_SNAKE_CASE							:     int = -1      ):
     return LambdaLR(__lowerCAmelCase     ,			lambda __SCREAMING_SNAKE_CASE      : 1     ,			last_epoch=__lowerCAmelCase      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optimizer     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int = -1      ):


     def lr_lambda(__SCREAMING_SNAKE_CASE							:     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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optimizer     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     int = -1      ):
     lowercase_	:				Optional[Any]													=	{}
     lowercase_	:				List[str]													=	step_rules.split(','      )
     for rule_str in rule_list[:-1]:
          lowercase_							,  lowercase_	:				Union[str, Any]													=	rule_str.split(':'      )
          lowercase_	:				Any													=	int(__lowerCAmelCase      )
          lowercase_	:				List[Any]													=	float(__lowerCAmelCase      )
          lowercase_	:				Union[str, Any]													=	value
     lowercase_	:				Optional[Any]													=	float(rule_list[-1]      )

     def create_rules_function(__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
          def rule_func(__SCREAMING_SNAKE_CASE							:     int      ) -> float:
               lowercase_	:				List[str]													=	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

     lowercase_	:				str													=	create_rules_function(__lowerCAmelCase     ,			__lowerCAmelCase      )

     return LambdaLR(__lowerCAmelCase     ,			__lowerCAmelCase     ,			last_epoch=__lowerCAmelCase      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[int]     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]=-1      ):


     def lr_lambda(__SCREAMING_SNAKE_CASE							:     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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optimizer     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     float = 0.5     ,			__SCREAMING_SNAKE_CASE							:     int = -1      ):


     def lr_lambda(__SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
          if current_step < num_warmup_steps:
               return float(__lowerCAmelCase      ) / float(max(1     ,			__lowerCAmelCase      )      )
          lowercase_	:				Optional[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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optimizer     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     int = 1     ,			__SCREAMING_SNAKE_CASE							:     int = -1      ):


     def lr_lambda(__SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
          if current_step < num_warmup_steps:
               return float(__lowerCAmelCase      ) / float(max(1     ,			__lowerCAmelCase      )      )
          lowercase_	:				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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]=1E-7     ,			__SCREAMING_SNAKE_CASE							:     Any=1.0     ,			__SCREAMING_SNAKE_CASE							:     int=-1      ):
     lowercase_	:				Union[str, 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(__SCREAMING_SNAKE_CASE							:     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:
               lowercase_	:				Tuple													=	lr_init - lr_end
               lowercase_	:				Dict													=	num_training_steps - num_warmup_steps
               lowercase_	:				Tuple													=	1 - (current_step - num_warmup_steps) / decay_steps
               lowercase_	:				List[Any]													=	lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(__lowerCAmelCase     ,			__lowerCAmelCase     ,			__lowerCAmelCase      )


__SCREAMING_SNAKE_CASE    	={
    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 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, SchedulerType]     ,			__SCREAMING_SNAKE_CASE							:     Optimizer     ,			__SCREAMING_SNAKE_CASE							:     Optional[str] = None     ,			__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,			__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,			__SCREAMING_SNAKE_CASE							:     int = 1     ,			__SCREAMING_SNAKE_CASE							:     float = 1.0     ,			__SCREAMING_SNAKE_CASE							:     int = -1     ,			):
     lowercase_	:				Any													=	SchedulerType(__lowerCAmelCase      )
     lowercase_	:				List[str]													=	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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =     logging.get_logger(__name__)

_A   =     {
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					='''sew'''




				def __init__(					self				,				_UpperCamelCase=32				,				_UpperCamelCase=768				,				_UpperCamelCase=12				,				_UpperCamelCase=12				,				_UpperCamelCase=3_072				,				_UpperCamelCase=2				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.0				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.02				,				_UpperCamelCase=1e-5				,				_UpperCamelCase="group"				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,				_UpperCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,				_UpperCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,				_UpperCamelCase=False				,				_UpperCamelCase=128				,				_UpperCamelCase=16				,				_UpperCamelCase=True				,				_UpperCamelCase=0.05				,				_UpperCamelCase=10				,				_UpperCamelCase=2				,				_UpperCamelCase=0.0				,				_UpperCamelCase=10				,				_UpperCamelCase=0				,				_UpperCamelCase="mean"				,				_UpperCamelCase=False				,				_UpperCamelCase=False				,				_UpperCamelCase=256				,				_UpperCamelCase=0				,				_UpperCamelCase=1				,				_UpperCamelCase=2				,				**_UpperCamelCase				,				)				->      Union[str, Any]:
							super().__init__(**_UpperCamelCase				,				pad_token_id=_UpperCamelCase				,				bos_token_id=_UpperCamelCase				,				eos_token_id=_UpperCamelCase     )
							lowerCAmelCase_						=	hidden_size
							lowerCAmelCase_						=	feat_extract_norm
							lowerCAmelCase_						=	feat_extract_activation
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	list(_UpperCamelCase     )
							lowerCAmelCase_						=	conv_bias
							lowerCAmelCase_						=	num_conv_pos_embeddings
							lowerCAmelCase_						=	num_conv_pos_embedding_groups
							lowerCAmelCase_						=	len(self.conv_dim     )
							lowerCAmelCase_						=	num_hidden_layers
							lowerCAmelCase_						=	intermediate_size
							lowerCAmelCase_						=	squeeze_factor
							lowerCAmelCase_						=	hidden_act
							lowerCAmelCase_						=	num_attention_heads
							lowerCAmelCase_						=	hidden_dropout
							lowerCAmelCase_						=	attention_dropout
							lowerCAmelCase_						=	activation_dropout
							lowerCAmelCase_						=	feat_proj_dropout
							lowerCAmelCase_						=	final_dropout
							lowerCAmelCase_						=	layerdrop
							lowerCAmelCase_						=	layer_norm_eps
							lowerCAmelCase_						=	initializer_range
							lowerCAmelCase_						=	vocab_size

							if (
							    (len(self.conv_stride     ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim     ) != self.num_feat_extract_layers)
							):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim     )}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride     )}`, `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							lowerCAmelCase_						=	apply_spec_augment
							lowerCAmelCase_						=	mask_time_prob
							lowerCAmelCase_						=	mask_time_length
							lowerCAmelCase_						=	mask_time_min_masks
							lowerCAmelCase_						=	mask_feature_prob
							lowerCAmelCase_						=	mask_feature_length
							lowerCAmelCase_						=	mask_feature_min_masks

							# ctc loss
							lowerCAmelCase_						=	ctc_loss_reduction
							lowerCAmelCase_						=	ctc_zero_infinity

							# sequence classification
							lowerCAmelCase_						=	use_weighted_layer_sum
							lowerCAmelCase_						=	classifier_proj_size




				@property
				def      __a						(					self     )				->      Optional[Any]:
							return functools.reduce(operator.mul				,				self.conv_stride				,				1     )



