
import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
			from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
			from tensorflow.keras.optimizers import Adam





class 	_lowerCAmelCase					(	tf.keras.optimizers.schedules.LearningRateSchedule       ):




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = 1.0				,				_UpperCamelCase = None				,				)				->      List[Any]:
							super().__init__()
							lowerCAmelCase_						=	initial_learning_rate
							lowerCAmelCase_						=	warmup_steps
							lowerCAmelCase_						=	power
							lowerCAmelCase_						=	decay_schedule_fn
							lowerCAmelCase_						=	name




				def __call__(					self				,				_UpperCamelCase     )				->      List[str]:
							with tf.name_scope(self.name or "WarmUp"     ) as name:
										# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
										# learning rate will be `global_step/num_warmup_steps * init_lr`.
										lowerCAmelCase_						=	tf.cast(_UpperCamelCase				,				tf.floataa     )
										lowerCAmelCase_						=	tf.cast(self.warmup_steps				,				tf.floataa     )
										lowerCAmelCase_						=	global_step_float / warmup_steps_float
										lowerCAmelCase_						=	self.initial_learning_rate * tf.math.pow(_UpperCamelCase				,				self.power     )
										return tf.cond(
										    global_step_float < warmup_steps_float				,				lambda: warmup_learning_rate				,				lambda: self.decay_schedule_fn(step - self.warmup_steps     )				,				name=_UpperCamelCase				,				)




				def      __a						(					self     )				->      Tuple:
							return {
							    "initial_learning_rate": self.initial_learning_rate,
							    "decay_schedule_fn": self.decay_schedule_fn,
							    "warmup_steps": self.warmup_steps,
							    "power": self.power,
							    "name": self.name,
							}






def    lowerCamelCase__				(				__lowerCAmelCase  :  float    ,							__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  float = 0.0    ,							__lowerCAmelCase  :  float = 0.9    ,							__lowerCAmelCase  :  float = 0.999    ,							__lowerCAmelCase  :  float = 1e-8    ,							__lowerCAmelCase  :  Optional[float] = None    ,							__lowerCAmelCase  :  Optional[float] = None    ,							__lowerCAmelCase  :  float = 0.0    ,							__lowerCAmelCase  :  float = 1.0    ,							__lowerCAmelCase  :  Optional[List[str]] = None    ,							):




			"""simple docstring"""


			lowerCAmelCase_						=	tf.keras.optimizers.schedules.PolynomialDecay(
			    initial_learning_rate=__lowerCAmelCase    ,							decay_steps=num_train_steps - num_warmup_steps    ,							end_learning_rate=init_lr * min_lr_ratio    ,							power=__lowerCAmelCase    ,							)
			if num_warmup_steps:
						lowerCAmelCase_						=	WarmUp(
						    initial_learning_rate=__lowerCAmelCase    ,							decay_schedule_fn=__lowerCAmelCase    ,							warmup_steps=__lowerCAmelCase    ,							)
			if weight_decay_rate > 0.0:
						lowerCAmelCase_						=	AdamWeightDecay(
						    learning_rate=__lowerCAmelCase    ,							weight_decay_rate=__lowerCAmelCase    ,							beta_a=__lowerCAmelCase    ,							beta_a=__lowerCAmelCase    ,							epsilon=__lowerCAmelCase    ,							clipnorm=__lowerCAmelCase    ,							global_clipnorm=__lowerCAmelCase    ,							exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]    ,							include_in_weight_decay=__lowerCAmelCase    ,							)
			else:
						lowerCAmelCase_						=	tf.keras.optimizers.Adam(
						    learning_rate=__lowerCAmelCase    ,							beta_a=__lowerCAmelCase    ,							beta_a=__lowerCAmelCase    ,							epsilon=__lowerCAmelCase    ,							clipnorm=__lowerCAmelCase    ,							global_clipnorm=__lowerCAmelCase    ,							)
			# We return the optimizer and the LR scheduler in order to better track the
			# evolution of the LR independently of the optimizer.
			return optimizer, lr_schedule





class 	_lowerCAmelCase					(	__a       ):




				def __init__(					self				,				_UpperCamelCase = 0.001				,				_UpperCamelCase = 0.9				,				_UpperCamelCase = 0.999				,				_UpperCamelCase = 1e-7				,				_UpperCamelCase = False				,				_UpperCamelCase = 0.0				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = "AdamWeightDecay"				,				**_UpperCamelCase				,				)				->      Optional[Any]:
							super().__init__(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )
							lowerCAmelCase_						=	weight_decay_rate
							lowerCAmelCase_						=	include_in_weight_decay
							lowerCAmelCase_						=	exclude_from_weight_decay




				@classmethod
				def      __a						(					cls				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	{"WarmUp": WarmUp}
							return super(_UpperCamelCase				,				cls     ).from_config(_UpperCamelCase				,				custom_objects=_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Dict:
							super(_UpperCamelCase				,				self     )._prepare_local(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )
							lowerCAmelCase_						=	tf.constant(
							    self.weight_decay_rate				,				name="adam_weight_decay_rate"     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	self._do_use_weight_decay(var.name     )
							if do_decay:
										return var.assign_sub(
										    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]				,				use_locking=self._use_locking				,				)
							return tf.no_op()




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=None				,				**_UpperCamelCase     )				->      Any:
							lowerCAmelCase_	,  lowerCAmelCase_						=	list(zip(*_UpperCamelCase     )     )
							return super(_UpperCamelCase				,				self     ).apply_gradients(zip(_UpperCamelCase				,				_UpperCamelCase     )				,				name=_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Optional[int]:
							if apply_state is None:
										return self._decayed_lr_t[var_dtype], {}

							lowerCAmelCase_						=	apply_state or {}
							lowerCAmelCase_						=	apply_state.get((var_device, var_dtype)     )
							if coefficients is None:
										lowerCAmelCase_						=	self._fallback_apply_state(_UpperCamelCase				,				_UpperCamelCase     )
										lowerCAmelCase_						=	coefficients

							return coefficients["lr_t"], {"apply_state": apply_state}




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      List[str]:
							lowerCAmelCase_	,  lowerCAmelCase_						=	self._get_lr(var.device				,				var.dtype.base_dtype				,				_UpperCamelCase     )
							lowerCAmelCase_						=	self._decay_weights_op(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )
							with tf.control_dependencies([decay]     ):
										return super(_UpperCamelCase				,				self     )._resource_apply_dense(_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      str:
							lowerCAmelCase_	,  lowerCAmelCase_						=	self._get_lr(var.device				,				var.dtype.base_dtype				,				_UpperCamelCase     )
							lowerCAmelCase_						=	self._decay_weights_op(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )
							with tf.control_dependencies([decay]     ):
										return super(_UpperCamelCase				,				self     )._resource_apply_sparse(_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	super().get_config()
							config.update({"weight_decay_rate": self.weight_decay_rate}     )
							return config




				def      __a						(					self				,				_UpperCamelCase     )				->      int:
							if self.weight_decay_rate == 0:
										return False

							if self._include_in_weight_decay:
										for r in self._include_in_weight_decay:
													if re.search(_UpperCamelCase				,				_UpperCamelCase     ) is not None:
																return True

							if self._exclude_from_weight_decay:
										for r in self._exclude_from_weight_decay:
													if re.search(_UpperCamelCase				,				_UpperCamelCase     ) is not None:
																return False
							return True





class 	_lowerCAmelCase					(	__a       ):




				def __init__(					self     )				->      Tuple:
							lowerCAmelCase_						=	[]
							lowerCAmelCase_						=	None




				@property
				def      __a						(					self     )				->      Dict:
							if self._accum_steps is None:
										lowerCAmelCase_						=	tf.Variable(
										    tf.constant(0				,				dtype=tf.intaa     )				,				trainable=_UpperCamelCase				,				synchronization=tf.VariableSynchronization.ON_READ				,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,				)

							return self._accum_steps.value()




				@property
				def      __a						(					self     )				->      Optional[int]:
							if not self._gradients:
										raise ValueError("The accumulator should be called first to initialize the gradients"     )
							return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]




				def __call__(					self				,				_UpperCamelCase     )				->      List[str]:
							if not self._gradients:
										lowerCAmelCase_						=	self.step  # Create the step variable.
										self._gradients.extend(
										    [
										        tf.Variable(
										            tf.zeros_like(_UpperCamelCase     )				,				trainable=_UpperCamelCase				,				synchronization=tf.VariableSynchronization.ON_READ				,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,				)
										        if gradient is not None
										        else gradient
										        for gradient in gradients
										    ]     )
							if len(_UpperCamelCase     ) != len(self._gradients     ):
										raise ValueError(f"""Expected {len(self._gradients     )} gradients, but got {len(_UpperCamelCase     )}"""     )

							for accum_gradient, gradient in zip(self._gradients				,				_UpperCamelCase     ):
										if accum_gradient is not None and gradient is not None:
													accum_gradient.assign_add(_UpperCamelCase     )

							self._accum_steps.assign_add(1     )




				def      __a						(					self     )				->      str:
							if not self._gradients:
										return
							self._accum_steps.assign(0     )
							for gradient in self._gradients:
										if gradient is not None:
													gradient.assign(tf.zeros_like(_UpperCamelCase     )     )





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     )



