






"""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   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = 1.0   ,				lowerCAmelCase_ = None   ,				)		->							Union[str, Any]:
										super().__init__()
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    initial_learning_rate
										_SCREAMING_SNAKE_CASE   :		Dict       		=    warmup_steps
										_SCREAMING_SNAKE_CASE   :		Any       		=    power
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    decay_schedule_fn
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    name







						def __call__(    self   ,				lowerCAmelCase_							)		->							int:
										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`.
														_SCREAMING_SNAKE_CASE   :		str       		=    tf.cast(lowerCAmelCase_   ,				tf.floataa							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    tf.cast(self.warmup_steps   ,				tf.floataa							)
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    global_step_float / warmup_steps_float
														_SCREAMING_SNAKE_CASE   :		int       		=    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							)		->							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 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase = 0.0,						lowerCamelCase = 0.9,						lowerCamelCase = 0.999,						lowerCamelCase = 1E-8,						lowerCamelCase = None,						lowerCamelCase = None,						lowerCamelCase = 0.0,						lowerCamelCase = 1.0,						lowerCamelCase = None,						):
				_SCREAMING_SNAKE_CASE   :		str       		=    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:
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    WarmUp(
								    initial_learning_rate=lowerCamelCase,						decay_schedule_fn=lowerCamelCase,						warmup_steps=lowerCamelCase,						)
				if weight_decay_rate > 0.0:
								_SCREAMING_SNAKE_CASE   :		Optional[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:
								_SCREAMING_SNAKE_CASE   :		Optional[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						(    __UpperCAmelCase       ):







						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_   ,				)		->							List[Any]:
										super().__init__(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    weight_decay_rate
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    include_in_weight_decay
										_SCREAMING_SNAKE_CASE   :		Any       		=    exclude_from_weight_decay







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







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										super(lowerCAmelCase_   ,				self							)._prepare_local(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    tf.constant(
										    self.weight_decay_rate   ,				name='adam_weight_decay_rate'							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		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 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None   ,				**lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Tuple       		=    list(zip(*lowerCAmelCase_							)							)
										return super(lowerCAmelCase_   ,				self							).apply_gradients(zip(lowerCAmelCase_   ,				lowerCAmelCase_							)   ,				name=lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Dict:
										if apply_state is None:
														return self._decayed_lr_t[var_dtype], {}

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

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







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None							)		->							Any:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    self._get_lr(var.device   ,				var.dtype.base_dtype   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[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[str]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    self._get_lr(var.device   ,				var.dtype.base_dtype   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    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:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    super().get_config()
										config.update({'weight_decay_rate': self.weight_decay_rate}							)
										return config







						def 			A      (    self   ,				lowerCAmelCase_							)		->							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 _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    []
										_SCREAMING_SNAKE_CASE   :		Any       		=    None







						@property
						def 			A      (    self							)		->							Optional[int]:
										if self._accum_steps is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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							)		->							List[str]:
										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:
														_SCREAMING_SNAKE_CASE   :		Any       		=    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							)		->							Tuple:
										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 transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			argparse.ArgumentParser(
			    description=(
			        'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
			        ' Distillation'
			    )
			)
			parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
			parser.add_argument('--model_name', default='roberta-large', type=str)
			parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
			parser.add_argument('--vocab_transform', action='store_true')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			0
			for teacher_idx in [0, 2, 4, 7, 9, 11]:
						if args.model_type == "gpt2":
									for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			state_dict[F'{prefix}.h.{teacher_idx}.attn.bias']
						else:
									for layer in [
									    "attention.self.query",
									    "attention.self.key",
									    "attention.self.value",
									    "attention.output.dense",
									    "attention.output.LayerNorm",
									    "intermediate.dense",
									    "output.dense",
									    "output.LayerNorm",
									]:
												for w in ["weight", "bias"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'
															]
						std_idx += 1

			# Language Modeling Head ###s
			if args.model_type == "roberta":
						for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			state_dict['lm_head.weight']

			print(F'N layers selected for distillation: {std_idx}')
			print(F'Number of params transferred for distillation: {len(compressed_sd.keys())}')

			print(F'Save transferred checkpoint to {args.dump_checkpoint}.')
			torch.save(compressed_sd, args.dump_checkpoint)

