





"""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 			__UpperCamelCase							(		tf.keras.optimizers.schedules.LearningRateSchedule		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = 1.0 ,   lowerCAmelCase__ = None ,   )      ->					Optional[Any]:
								super().__init__()
								a :						int      =							initial_learning_rate
								a :						str      =							warmup_steps
								a :						Optional[Any]      =							power
								a :						int      =							decay_schedule_fn
								a :						str      =							name






							def __call__(  self ,   lowerCAmelCase__ )      ->					Any:
								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`.
									a :						Dict      =							tf.cast(lowerCAmelCase__ ,   tf.floataa )
									a :						Tuple      =							tf.cast(self.warmup_steps ,   tf.floataa )
									a :						List[str]      =							global_step_float / warmup_steps_float
									a :						Union[str, Any]      =							self.initial_learning_rate * tf.math.pow(lowerCAmelCase__ ,   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=lowerCAmelCase__ ,   )






							def        __a  (  self )      ->					Dict:
								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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       float       ,			_lowercase		:       int       ,			_lowercase		:       int       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 0.9       ,			_lowercase		:       float = 0.999       ,			_lowercase		:       float = 1E-8       ,			_lowercase		:       Optional[float] = None       ,			_lowercase		:       Optional[float] = None       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0       ,			_lowercase		:       Optional[List[str]] = None       ,			)					->Optional[int]:






	'''simple docstring'''






	a :						Optional[Any]      =							tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=_lowercase       ,			decay_steps=num_train_steps - num_warmup_steps       ,			end_learning_rate=init_lr * min_lr_ratio       ,			power=_lowercase       ,			)
	if num_warmup_steps:
		a :						Dict      =							WarmUp(
		    initial_learning_rate=_lowercase       ,			decay_schedule_fn=_lowercase       ,			warmup_steps=_lowercase       ,			)
	if weight_decay_rate > 0.0:
		a :						List[Any]      =							AdamWeightDecay(
		    learning_rate=_lowercase       ,			weight_decay_rate=_lowercase       ,			beta_a=_lowercase       ,			beta_a=_lowercase       ,			epsilon=_lowercase       ,			clipnorm=_lowercase       ,			global_clipnorm=_lowercase       ,			exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]       ,			include_in_weight_decay=_lowercase       ,			)
	else:
		a :						List[str]      =							tf.keras.optimizers.Adam(
		    learning_rate=_lowercase       ,			beta_a=_lowercase       ,			beta_a=_lowercase       ,			epsilon=_lowercase       ,			clipnorm=_lowercase       ,			global_clipnorm=_lowercase       ,			)
	# 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 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ = 0.001 ,   lowerCAmelCase__ = 0.9 ,   lowerCAmelCase__ = 0.999 ,   lowerCAmelCase__ = 1E-7 ,   lowerCAmelCase__ = False ,   lowerCAmelCase__ = 0.0 ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = "AdamWeightDecay" ,   **lowerCAmelCase__ ,   )      ->					Tuple:
								super().__init__(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )
								a :						str      =							weight_decay_rate
								a :						Union[str, Any]      =							include_in_weight_decay
								a :						Tuple      =							exclude_from_weight_decay






							@classmethod
							def        __a  (  cls ,   lowerCAmelCase__ )      ->					int:
								a :						Tuple      =							{"WarmUp": WarmUp}
								return super(lowerCAmelCase__ ,   cls ).from_config(lowerCAmelCase__ ,   custom_objects=lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Union[str, Any]:
								super(lowerCAmelCase__ ,   self )._prepare_local(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )
								a :						Any      =							tf.constant(
								    self.weight_decay_rate ,   name="adam_weight_decay_rate" )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[Any]:
								a :						List[Any]      =							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 ,   lowerCAmelCase__ ,   lowerCAmelCase__=None ,   **lowerCAmelCase__ )      ->					int:
								a,			a :						int      =							list(zip(*lowerCAmelCase__ ) )
								return super(lowerCAmelCase__ ,   self ).apply_gradients(zip(lowerCAmelCase__ ,   lowerCAmelCase__ ) ,   name=lowerCAmelCase__ ,   **lowerCAmelCase__ )






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

								a :						Union[str, Any]      =							apply_state or {}
								a :						Dict      =							apply_state.get((var_device, var_dtype) )
								if coefficients is None:
									a :						Union[str, Any]      =							self._fallback_apply_state(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						List[Any]      =							coefficients

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__=None )      ->					Tuple:
								a,			a :						Optional[Any]      =							self._get_lr(var.device ,   var.dtype.base_dtype ,   lowerCAmelCase__ )
								a :						Optional[Any]      =							self._decay_weights_op(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )
								with tf.control_dependencies([decay] ):
									return super(lowerCAmelCase__ ,   self )._resource_apply_dense(lowerCAmelCase__ ,   lowerCAmelCase__ ,   **lowerCAmelCase__ )






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






							def        __a  (  self )      ->					int:
								a :						Optional[Any]      =							super().get_config()
								config.update({"weight_decay_rate": self.weight_decay_rate} )
								return config






							def        __a  (  self ,   lowerCAmelCase__ )      ->					Optional[Any]:
								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(lowerCAmelCase__ ,   lowerCAmelCase__ ) is not None:
											return True

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


class 			__UpperCamelCase							(		a__		):






							def __init__(  self )      ->					List[Any]:
								a :						List[Any]      =							[]
								a :						int      =							None






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

								return self._accum_steps.value()






							@property
							def        __a  (  self )      ->					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 ,   lowerCAmelCase__ )      ->					Any:
								if not self._gradients:
									a :						Optional[int]      =							self.step  # Create the step variable.
									self._gradients.extend(
									    [
									        tf.Variable(
									            tf.zeros_like(lowerCAmelCase__ ) ,   trainable=lowerCAmelCase__ ,   synchronization=tf.VariableSynchronization.ON_READ ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA ,   )
									        if gradient is not None
									        else gradient
									        for gradient in gradients
									    ] )
								if len(lowerCAmelCase__ ) != len(self._gradients ):
									raise ValueError(f"""Expected {len(self._gradients )} gradients, but got {len(lowerCAmelCase__ )}""" )

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

								self._accum_steps.assign_add(1 )






							def        __a  (  self )      ->					Union[str, Any]:
								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(lowerCAmelCase__ ) )









"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


