
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        lowercase	(      tf.keras.optimizers.schedules.LearningRateSchedule	):




							def __init__(       self     ,A__     ,A__     ,A__     ,A__ = 1.0     ,A__ = None     ,):
												super().__init__()
												lowercase					=		initial_learning_rate
												lowercase					=		warmup_steps
												lowercase					=		power
												lowercase					=		decay_schedule_fn
												lowercase					=		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`.
																	lowercase					=		tf.cast(A__     ,tf.floataa)
																	lowercase					=		tf.cast(self.warmup_steps     ,tf.floataa)
																	lowercase					=		global_step_float / warmup_steps_float
																	lowercase					=		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 				A__   (       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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 0.9				,    lowerCAmelCase__ = 0.9_99				,    lowerCAmelCase__ = 1E-8				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = None				,    lowerCAmelCase__ = 0.0				,    lowerCAmelCase__ = 1.0				,    lowerCAmelCase__ = None				,    ):
					'''simple docstring'''
					lowercase					=		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:
										lowercase					=		WarmUp(
										    initial_learning_rate=lowerCAmelCase__				,    decay_schedule_fn=lowerCAmelCase__				,    warmup_steps=lowerCAmelCase__				,    )
					if weight_decay_rate > 0.0:
										lowercase					=		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:
										lowercase					=		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        lowercase	(      SCREAMING_SNAKE_CASE__	):




							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__)
												lowercase					=		weight_decay_rate
												lowercase					=		include_in_weight_decay
												lowercase					=		exclude_from_weight_decay




							@classmethod
							def 				A__   (       cls     ,A__):
												lowercase					=		{'''WarmUp''': WarmUp}
												return super(A__     ,cls).from_config(A__     ,custom_objects=A__)




							def 				A__   (       self     ,A__     ,A__     ,A__):
												super(A__     ,self)._prepare_local(A__     ,A__     ,A__)
												lowercase					=		tf.constant(
												    self.weight_decay_rate     ,name='''adam_weight_decay_rate''')




							def 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		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     ,A__     ,A__=None     ,**A__):
												lowercase			,		lowercase					=		list(zip(*A__))
												return super(A__     ,self).apply_gradients(zip(A__     ,A__)     ,name=A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__):
												if apply_state is None:
																	return self._decayed_lr_t[var_dtype], {}

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

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




							def 				A__   (       self     ,A__     ,A__     ,A__=None):
												lowercase			,		lowercase					=		self._get_lr(var.device     ,var.dtype.base_dtype     ,A__)
												lowercase					=		self._decay_weights_op(A__     ,A__     ,A__)
												with tf.control_dependencies([decay]):
																	return super(A__     ,self)._resource_apply_dense(A__     ,A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__=None):
												lowercase			,		lowercase					=		self._get_lr(var.device     ,var.dtype.base_dtype     ,A__)
												lowercase					=		self._decay_weights_op(A__     ,A__     ,A__)
												with tf.control_dependencies([decay]):
																	return super(A__     ,self)._resource_apply_sparse(A__     ,A__     ,A__     ,**A__)




							def 				A__   (       self):
												lowercase					=		super().get_config()
												config.update({'''weight_decay_rate''': self.weight_decay_rate})
												return config





							def 				A__   (       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        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def __init__(       self):
												lowercase					=		[]
												lowercase					=		None




							@property
							def 				A__   (       self):
												if self._accum_steps is None:
																	lowercase					=		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 				A__   (       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:
																	lowercase					=		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 				A__   (       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__))






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




