





'''simple docstring'''






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		,      a_		,      a_		,      a_		,      a_ = 1.0		,      a_ = None		,      ):
			super().__init__()
			lowerCAmelCase     :    Any						=   initial_learning_rate
			lowerCAmelCase     :    List[str]						=   warmup_steps
			lowerCAmelCase     :    List[str]						=   power
			lowerCAmelCase     :    int						=   decay_schedule_fn
			lowerCAmelCase     :    Optional[Any]						=   name



	def __call__(   self		,      a_	):

			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     :    Tuple						=   tf.cast(a_		,      tf.floataa	)
					lowerCAmelCase     :    Dict						=   tf.cast(self.warmup_steps		,      tf.floataa	)
					lowerCAmelCase     :    List[Any]						=   global_step_float / warmup_steps_float
					lowerCAmelCase     :    Optional[Any]						=   self.initial_learning_rate * tf.math.pow(a_		,      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=a_		,      )





	def   _lowerCamelCase    (   self	):
			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      __A						(							a_						:  List[Any]      ,a_						:  Optional[Any]      ,a_						:  List[Any]      ,a_						:  Dict = 0.0      ,a_						:  Optional[int] = 0.9      ,a_						:  List[Any] = 0.9_9_9      ,a_						:  Tuple = 1e-8      ,a_						:  Optional[int] = None      ,a_						:  Optional[int] = None      ,a_						:  Optional[Any] = 0.0      ,a_						:  Dict = 1.0      ,a_						:  Dict = None      ,):
		lowerCAmelCase     :    Optional[Any]						=   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     :    List[Any]						=   WarmUp(
				    initial_learning_rate=lowerCamelCase__      ,decay_schedule_fn=lowerCamelCase__      ,warmup_steps=lowerCamelCase__      ,)
		if weight_decay_rate > 0.0:
				lowerCAmelCase     :    List[Any]						=   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     :    int						=   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		,      a_ = 0.001		,      a_ = 0.9		,      a_ = 0.999		,      a_ = 1e-7		,      a_ = False		,      a_ = 0.0		,      a_ = None		,      a_ = None		,      a_ = "AdamWeightDecay"		,      **a_		,      ):
			super().__init__(a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      **a_	)
			lowerCAmelCase     :    Tuple						=   weight_decay_rate
			lowerCAmelCase     :    Optional[Any]						=   include_in_weight_decay
			lowerCAmelCase     :    Tuple						=   exclude_from_weight_decay



	@classmethod
	def   _lowerCamelCase    (   cls		,      a_	):
			lowerCAmelCase     :    Tuple						=   {"WarmUp": WarmUp}
			return super(a_		,      cls	).from_config(a_		,      custom_objects=a_	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			super(a_		,      self	)._prepare_local(a_		,      a_		,      a_	)
			lowerCAmelCase     :    Any						=   tf.constant(
			    self.weight_decay_rate		,      name="adam_weight_decay_rate"	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[int]						=   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   _lowerCamelCase    (   self		,      a_		,      a_=None		,      **a_	):
			lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   list(zip(*a_	)	)
			return super(a_		,      self	).apply_gradients(zip(a_		,      a_	)		,      name=a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):

			if apply_state is None:
					return self._decayed_lr_t[var_dtype], {}

			lowerCAmelCase     :    Optional[int]						=   apply_state or {}
			lowerCAmelCase     :    Any						=   apply_state.get((var_device, var_dtype)	)
			if coefficients is None:
					lowerCAmelCase     :    str						=   self._fallback_apply_state(a_		,      a_	)
					lowerCAmelCase     :    Any						=   coefficients

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



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_=None	):
			lowerCAmelCase	,		lowerCAmelCase     :    int						=   self._get_lr(var.device		,      var.dtype.base_dtype		,      a_	)
			lowerCAmelCase     :    Tuple						=   self._decay_weights_op(a_		,      a_		,      a_	)
			with tf.control_dependencies([decay]	):
					return super(a_		,      self	)._resource_apply_dense(a_		,      a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_=None	):
			lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   self._get_lr(var.device		,      var.dtype.base_dtype		,      a_	)
			lowerCAmelCase     :    int						=   self._decay_weights_op(a_		,      a_		,      a_	)
			with tf.control_dependencies([decay]	):
					return super(a_		,      self	)._resource_apply_sparse(a_		,      a_		,      a_		,      **a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   super().get_config()
			config.update({"weight_decay_rate": self.weight_decay_rate}	)
			return config





	def   _lowerCamelCase    (   self		,      a_	):

			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(a_		,      a_	) is not None:
									return True

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






class 		lowerCamelCase  (      _A       ):



	def __init__(   self	):
			lowerCAmelCase     :    List[Any]						=   []
			lowerCAmelCase     :    int						=   None



	@property
	def   _lowerCamelCase    (   self	):

			if self._accum_steps is None:
					lowerCAmelCase     :    Dict						=   tf.Variable(
					    tf.constant(0		,      dtype=tf.intaa	)		,      trainable=a_		,      synchronization=tf.VariableSynchronization.ON_READ		,      aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA		,      )

			return self._accum_steps.value()



	@property
	def   _lowerCamelCase    (   self	):

			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		,      a_	):

			if not self._gradients:
					lowerCAmelCase     :    Optional[Any]						=   self.step  # Create the step variable.
					self._gradients.extend(
					    [
					        tf.Variable(
					            tf.zeros_like(a_	)		,      trainable=a_		,      synchronization=tf.VariableSynchronization.ON_READ		,      aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA		,      )
					        if gradient is not None
					        else gradient
					        for gradient in gradients
					    ]	)
			if len(a_	) != len(self._gradients	):
					raise ValueError(F'''Expected {len(self._gradients	)} gradients, but got {len(a_	)}'''	)

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

			self._accum_steps.assign_add(1	)





	def   _lowerCamelCase    (   self	):

			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(a_	)	)










'''simple docstring'''






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 		lowerCamelCase  (      _A      ,   _A      ,   _A       ):



	@register_to_config
	def __init__(   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_ = False		,      ):
			super().__init__()

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)
			lowerCAmelCase     :    int						=   False

			lowerCAmelCase     :    int						=   nn.Dropout(p=a_	)

			lowerCAmelCase     :    str						=   TaConfig(
			    vocab_size=a_		,      d_model=a_		,      num_heads=a_		,      d_kv=a_		,      d_ff=a_		,      dropout_rate=a_		,      feed_forward_proj=a_		,      is_decoder=a_		,      is_encoder_decoder=a_		,      )

			lowerCAmelCase     :    Any						=   nn.ModuleList()
			for lyr_num in range(a_	):
					lowerCAmelCase     :    Optional[Any]						=   TaBlock(a_	)
					self.encoders.append(a_	)

			lowerCAmelCase     :    List[str]						=   TaLayerNorm(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   nn.Dropout(p=a_	)





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   self.token_embedder(a_	)

			lowerCAmelCase     :    int						=   encoder_input_tokens.shape[1]
			lowerCAmelCase     :    Optional[int]						=   torch.arange(a_		,      device=encoder_input_tokens.device	)
			x += self.position_encoding(a_	)

			lowerCAmelCase     :    List[str]						=   self.dropout_pre(a_	)

			# inverted the attention mask
			lowerCAmelCase     :    List[Any]						=   encoder_input_tokens.size()
			lowerCAmelCase     :    Dict						=   self.get_extended_attention_mask(a_		,      a_	)

			for lyr in self.encoders:
					lowerCAmelCase     :    Dict						=   lyr(a_		,      a_	)[0]
			lowerCAmelCase     :    int						=   self.layer_norm(a_	)

			return self.dropout_post(a_	), encoder_inputs_mask



