


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, 1_1]:
		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)














import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


SCREAMING_SNAKE_CASE__   :      List[str]				=		logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met
class     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       *__lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__(*__lowerCAmelCase							,       **__lowerCAmelCase  )
								__magic_name__		:Any   =   eval_examples
								__magic_name__		:str   =   post_process_function
								__magic_name__		:int   =   quant_trainer_args
								__magic_name__		:List[str]   =   1_2_8  # default number of calibration samples







				def       A						(    self							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								if calib_dataset is None and self.calib_dataset is None:
												raise ValueError('''Trainer: calibration requires an calib_dataset.'''  )
								__magic_name__		:Optional[Any]   =   calib_dataset if calib_dataset is not None else self.calib_dataset

								__magic_name__		:Optional[int]   =   self._remove_unused_columns(__lowerCAmelCase							,       description='''Calibration'''  )

								return DataLoader(
								    __lowerCAmelCase							,       batch_size=self.args.eval_batch_size							,       collate_fn=self.data_collator							,       drop_last=self.args.dataloader_drop_last							,       num_workers=self.args.dataloader_num_workers							,       pin_memory=self.args.dataloader_pin_memory							,       shuffle=__lowerCAmelCase							,       )







				def       A						(    self							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.train_dataset if calib_dataset is None else calib_dataset
								__magic_name__		:Any   =   self.get_calib_dataloader(__lowerCAmelCase  )

								__magic_name__		:List[str]   =   self.model
								quant_trainer.configure_model(__lowerCAmelCase							,       self.quant_trainer_args							,       calib=__lowerCAmelCase  )
								model.eval()
								quant_trainer.enable_calibration(__lowerCAmelCase  )

								logger.info('''***** Running calibration *****'''  )
								logger.info(F'''  Num examples = {self.calib_num}'''  )
								logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''  )

								for step, inputs in enumerate(__lowerCAmelCase  ):
												# Prediction step
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:str   =   self.prediction_step(__lowerCAmelCase							,       __lowerCAmelCase							,       prediction_loss_only=__lowerCAmelCase  )
												if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																break

								quant_trainer.finish_calibration(__lowerCAmelCase							,       self.quant_trainer_args  )
								__magic_name__		:Any   =   model







				def       A						(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase = "eval"  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.eval_dataset if eval_dataset is None else eval_dataset
								__magic_name__		:Optional[Any]   =   self.get_eval_dataloader(__lowerCAmelCase  )
								__magic_name__		:str   =   self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								__magic_name__		:Any   =   self.compute_metrics
								__magic_name__		:List[Any]   =   None
								__magic_name__		:List[str]   =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
												__magic_name__		:Optional[Any]   =   eval_loop(
												    __lowerCAmelCase							,       description='''Evaluation'''							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=__lowerCAmelCase							,       )
								finally:
												__magic_name__		:Union[str, Any]   =   compute_metrics

								if self.post_process_function is not None and self.compute_metrics is not None:
												__magic_name__		:Union[str, Any]   =   self.post_process_function(__lowerCAmelCase							,       __lowerCAmelCase							,       output.predictions  )
												__magic_name__		:int   =   self.compute_metrics(__lowerCAmelCase  )

												# Prefix all keys with metric_key_prefix + '_'
												for key in list(metrics.keys()  ):
																if not key.startswith(F'''{metric_key_prefix}_'''  ):
																				__magic_name__		:Dict   =   metrics.pop(__lowerCAmelCase  )

												self.log(__lowerCAmelCase  )
								else:
												__magic_name__		:List[str]   =   {}

								if self.args.tpu_metrics_debug or self.args.debug:
												# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
												xm.master_print(met.metrics_report()  )

								__magic_name__		:Optional[Any]   =   self.callback_handler.on_evaluate(self.args							,       self.state							,       self.control							,       __lowerCAmelCase  )
								return metrics







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase = "test"  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.get_test_dataloader(__lowerCAmelCase  )

								# Temporarily disable metric computation, we will do it in the loop here.
								__magic_name__		:Dict   =   self.compute_metrics
								__magic_name__		:str   =   None
								__magic_name__		:Optional[int]   =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
												__magic_name__		:int   =   eval_loop(
												    __lowerCAmelCase							,       description='''Prediction'''							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=__lowerCAmelCase							,       )
								finally:
												__magic_name__		:List[Any]   =   compute_metrics

								if self.post_process_function is None or self.compute_metrics is None:
												return output

								__magic_name__		:Optional[Any]   =   self.post_process_function(__lowerCAmelCase							,       __lowerCAmelCase							,       output.predictions							,       '''predict'''  )
								__magic_name__		:Dict   =   self.compute_metrics(__lowerCAmelCase  )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()  ):
												if not key.startswith(F'''{metric_key_prefix}_'''  ):
																__magic_name__		:List[str]   =   metrics.pop(__lowerCAmelCase  )

								return PredictionOutput(predictions=predictions.predictions							,       label_ids=predictions.label_ids							,       metrics=__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase="./"  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.eval_dataset
								__magic_name__		:Any   =   self.get_eval_dataloader(__lowerCAmelCase  )

								__magic_name__		:int   =   next(iter(__lowerCAmelCase  )  )

								# saving device - to make it consistent
								__magic_name__		:str   =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

								# convert to tuple
								__magic_name__		:int   =   tuple(v.to(__lowerCAmelCase  ) for k, v in batch.items()  )

								logger.info('''Converting model to be onnx compatible'''  )
								from pytorch_quantization.nn import TensorQuantizer

								__magic_name__		:Any   =   True

								__magic_name__		:Optional[int]   =   self.model.to(__lowerCAmelCase  )

								model.eval()
								model.float()

								__magic_name__		:Any   =   model.module if hasattr(__lowerCAmelCase							,       '''module'''  ) else model
								quant_trainer.configure_model(__lowerCAmelCase							,       self.quant_trainer_args  )

								__magic_name__		:int   =   os.path.join(__lowerCAmelCase							,       '''model.onnx'''  )
								logger.info(F'''exporting model to {output_model_file}'''  )

								__magic_name__		:Dict   =   {0: '''batch_size''', 1: '''seq_len'''}

								torch.onnx.export(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       export_params=__lowerCAmelCase							,       opset_version=1_3							,       do_constant_folding=__lowerCAmelCase							,       input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']							,       output_names=['''output_start_logits''', '''output_end_logits''']							,       dynamic_axes={
								        '''input_ids''': axes,
								        '''attention_mask''': axes,
								        '''token_type_ids''': axes,
								        '''output_start_logits''': axes,
								        '''output_end_logits''': axes,
								    }							,       verbose=__lowerCAmelCase							,       )
								logger.info('''onnx export finished'''  )






