





import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCAmelCase__  :     List[str]   			={
    '''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    '''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    '''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
    '''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}





def        _lowercase   (  _UpperCAmelCase			)  ->					List[Any]:
				assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
				assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
				if args.mlm:
								assert os.path.isfile(args.token_counts			)
								assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
				else:
								assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

				assert args.teacher_type == args.student_type or (
				    args.student_type == "distilbert" and args.teacher_type == "bert"
				)
				assert os.path.isfile(args.student_config			)
				if args.student_pretrained_weights is not None:
								assert os.path.isfile(args.student_pretrained_weights			)

				if args.freeze_token_type_embds:
								assert args.student_type in ["roberta"]

				assert args.alpha_ce >= 0.0
				assert args.alpha_mlm >= 0.0
				assert args.alpha_clm >= 0.0
				assert args.alpha_mse >= 0.0
				assert args.alpha_cos >= 0.0
				assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[Any]:
				if args.student_type == "roberta":
								lowerCamelCase			=False
				elif args.student_type == "gpt2":
								lowerCamelCase			=False





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				if args.student_type == "roberta":
								lowerCamelCase			=False





def        _lowercase   (  )  ->					str:
				lowerCamelCase			=argparse.ArgumentParser(description="""Training"""			)
				parser.add_argument("""--force"""     , action="""store_true"""     , help="""Overwrite dump_path if it already exists."""			)

				parser.add_argument(
				    """--dump_path"""     , type=_UpperCAmelCase     , required=_UpperCAmelCase     , help="""The output directory (log, checkpoints, parameters, etc.)"""			)
				parser.add_argument(
				    """--data_file"""     , type=_UpperCAmelCase     , required=_UpperCAmelCase     , help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""     , )

				parser.add_argument(
				    """--student_type"""     , type=_UpperCAmelCase     , choices=["""distilbert""", """roberta""", """gpt2"""]     , required=_UpperCAmelCase     , help="""The student type (DistilBERT, RoBERTa)."""     , )
				parser.add_argument("""--student_config"""     , type=_UpperCAmelCase     , required=_UpperCAmelCase     , help="""Path to the student configuration."""			)
				parser.add_argument(
				    """--student_pretrained_weights"""     , default=_UpperCAmelCase     , type=_UpperCAmelCase     , help="""Load student initialization checkpoint."""			)

				parser.add_argument(
				    """--teacher_type"""     , choices=["""bert""", """roberta""", """gpt2"""]     , required=_UpperCAmelCase     , help="""Teacher type (BERT, RoBERTa)."""			)
				parser.add_argument("""--teacher_name"""     , type=_UpperCAmelCase     , required=_UpperCAmelCase     , help="""The teacher model."""			)

				parser.add_argument("""--temperature"""     , default=2.0     , type=_UpperCAmelCase     , help="""Temperature for the softmax temperature."""			)
				parser.add_argument(
				    """--alpha_ce"""     , default=0.5     , type=_UpperCAmelCase     , help="""Linear weight for the distillation loss. Must be >=0."""			)
				parser.add_argument(
				    """--alpha_mlm"""     , default=0.0     , type=_UpperCAmelCase     , help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""     , )
				parser.add_argument("""--alpha_clm"""     , default=0.5     , type=_UpperCAmelCase     , help="""Linear weight for the CLM loss. Must be >=0."""			)
				parser.add_argument("""--alpha_mse"""     , default=0.0     , type=_UpperCAmelCase     , help="""Linear weight of the MSE loss. Must be >=0."""			)
				parser.add_argument(
				    """--alpha_cos"""     , default=0.0     , type=_UpperCAmelCase     , help="""Linear weight of the cosine embedding loss. Must be >=0."""			)

				parser.add_argument(
				    """--mlm"""     , action="""store_true"""     , help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""			)
				parser.add_argument(
				    """--mlm_mask_prop"""     , default=0.1_5     , type=_UpperCAmelCase     , help="""Proportion of tokens for which we need to make a prediction."""     , )
				parser.add_argument("""--word_mask"""     , default=0.8     , type=_UpperCAmelCase     , help="""Proportion of tokens to mask out."""			)
				parser.add_argument("""--word_keep"""     , default=0.1     , type=_UpperCAmelCase     , help="""Proportion of tokens to keep."""			)
				parser.add_argument("""--word_rand"""     , default=0.1     , type=_UpperCAmelCase     , help="""Proportion of tokens to randomly replace."""			)
				parser.add_argument(
				    """--mlm_smoothing"""     , default=0.7     , type=_UpperCAmelCase     , help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""     , )
				parser.add_argument("""--token_counts"""     , type=_UpperCAmelCase     , help="""The token counts in the data_file for MLM."""			)

				parser.add_argument(
				    """--restrict_ce_to_mask"""     , action="""store_true"""     , help="""If true, compute the distillation loss only the [MLM] prediction distribution."""     , )
				parser.add_argument(
				    """--freeze_pos_embs"""     , action="""store_true"""     , help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""     , )
				parser.add_argument(
				    """--freeze_token_type_embds"""     , action="""store_true"""     , help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""     , )

				parser.add_argument("""--n_epoch"""     , type=_UpperCAmelCase     , default=3     , help="""Number of pass on the whole dataset."""			)
				parser.add_argument("""--batch_size"""     , type=_UpperCAmelCase     , default=5     , help="""Batch size (for each process)."""			)
				parser.add_argument(
				    """--group_by_size"""     , action="""store_false"""     , help="""If true, group sequences that have similar length into the same batch. Default is true."""     , )

				parser.add_argument(
				    """--gradient_accumulation_steps"""     , type=_UpperCAmelCase     , default=50     , help="""Gradient accumulation for larger training batches."""     , )
				parser.add_argument("""--warmup_prop"""     , default=0.0_5     , type=_UpperCAmelCase     , help="""Linear warmup proportion."""			)
				parser.add_argument("""--weight_decay"""     , default=0.0     , type=_UpperCAmelCase     , help="""Weight decay if we apply some."""			)
				parser.add_argument("""--learning_rate"""     , default=5e-4     , type=_UpperCAmelCase     , help="""The initial learning rate for Adam."""			)
				parser.add_argument("""--adam_epsilon"""     , default=1e-6     , type=_UpperCAmelCase     , help="""Epsilon for Adam optimizer."""			)
				parser.add_argument("""--max_grad_norm"""     , default=5.0     , type=_UpperCAmelCase     , help="""Max gradient norm."""			)
				parser.add_argument("""--initializer_range"""     , default=0.0_2     , type=_UpperCAmelCase     , help="""Random initialization range."""			)

				parser.add_argument(
				    """--fp16"""     , action="""store_true"""     , help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""     , )
				parser.add_argument(
				    """--fp16_opt_level"""     , type=_UpperCAmelCase     , default="""O1"""     , help=(
				        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
				        """See details at https://nvidia.github.io/apex/amp.html"""
				    )     , )
				parser.add_argument("""--n_gpu"""     , type=_UpperCAmelCase     , default=1     , help="""Number of GPUs in the node."""			)
				parser.add_argument("""--local_rank"""     , type=_UpperCAmelCase     , default=-1     , help="""Distributed training - Local rank"""			)
				parser.add_argument("""--seed"""     , type=_UpperCAmelCase     , default=56     , help="""Random seed"""			)

				parser.add_argument("""--log_interval"""     , type=_UpperCAmelCase     , default=5_00     , help="""Tensorboard logging interval."""			)
				parser.add_argument("""--checkpoint_interval"""     , type=_UpperCAmelCase     , default=40_00     , help="""Checkpoint interval."""			)
				lowerCamelCase			=parser.parse_args()
				sanity_checks(_UpperCAmelCase			)

				# ARGS #
				init_gpu_params(_UpperCAmelCase			)
				set_seed(_UpperCAmelCase			)
				if args.is_master:
								if os.path.exists(args.dump_path			):
												if not args.force:
																raise ValueError(
																    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																    """ itUse `--force` if you want to overwrite it"""			)
												else:
																shutil.rmtree(args.dump_path			)

								if not os.path.exists(args.dump_path			):
												os.makedirs(args.dump_path			)
								logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""			)

								# SAVE PARAMS #
								logger.info(F"""Param: {args}"""			)
								with open(os.path.join(args.dump_path     , """parameters.json"""			)     , """w"""			) as f:
												json.dump(vars(_UpperCAmelCase			)     , _UpperCAmelCase     , indent=4			)
								git_log(args.dump_path			)

				lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=MODEL_CLASSES[args.student_type]
				lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=MODEL_CLASSES[args.teacher_type]

				# TOKENIZER #
				lowerCamelCase			=teacher_tokenizer_class.from_pretrained(args.teacher_name			)
				lowerCamelCase			={}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								lowerCamelCase			=tokenizer.all_special_tokens.index(_UpperCAmelCase			)
								lowerCamelCase			=tokenizer.all_special_ids[idx]
				logger.info(F"""Special tokens {special_tok_ids}"""			)
				lowerCamelCase			=special_tok_ids
				lowerCamelCase			=tokenizer.max_model_input_sizes[args.teacher_name]

				# DATA LOADER #
				logger.info(F"""Loading data from {args.data_file}"""			)
				with open(args.data_file     , """rb"""			) as fp:
								lowerCamelCase			=pickle.load(_UpperCAmelCase			)

				if args.mlm:
								logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""			)
								with open(args.token_counts     , """rb"""			) as fp:
												lowerCamelCase			=pickle.load(_UpperCAmelCase			)

								lowerCamelCase			=np.maximum(_UpperCAmelCase     , 1			) ** -args.mlm_smoothing
								for idx in special_tok_ids.values():
												lowerCamelCase			=0.0  # do not predict special tokens
								lowerCamelCase			=torch.from_numpy(_UpperCAmelCase			)
				else:
								lowerCamelCase			=None

				lowerCamelCase			=LmSeqsDataset(params=_UpperCAmelCase     , data=_UpperCAmelCase			)
				logger.info("""Data loader created."""			)

				# STUDENT #
				logger.info(F"""Loading student config from {args.student_config}"""			)
				lowerCamelCase			=student_config_class.from_pretrained(args.student_config			)
				lowerCamelCase			=True

				if args.student_pretrained_weights is not None:
								logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""			)
								lowerCamelCase			=student_model_class.from_pretrained(args.student_pretrained_weights     , config=_UpperCAmelCase			)
				else:
								lowerCamelCase			=student_model_class(_UpperCAmelCase			)

				if args.n_gpu > 0:
								student.to(F"""cuda:{args.local_rank}"""			)
				logger.info("""Student loaded."""			)

				# TEACHER #
				lowerCamelCase			=teacher_model_class.from_pretrained(args.teacher_name     , output_hidden_states=_UpperCAmelCase			)
				if args.n_gpu > 0:
								teacher.to(F"""cuda:{args.local_rank}"""			)
				logger.info(F"""Teacher loaded from {args.teacher_name}."""			)

				# FREEZING #
				if args.freeze_pos_embs:
								freeze_pos_embeddings(_UpperCAmelCase     , _UpperCAmelCase			)
				if args.freeze_token_type_embds:
								freeze_token_type_embeddings(_UpperCAmelCase     , _UpperCAmelCase			)

				# SANITY CHECKS #
				assert student.config.vocab_size == teacher.config.vocab_size
				assert student.config.hidden_size == teacher.config.hidden_size
				assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
				if args.mlm:
								assert token_probs.size(0			) == stu_architecture_config.vocab_size

				# DISTILLER #
				torch.cuda.empty_cache()
				lowerCamelCase			=Distiller(
				    params=_UpperCAmelCase     , dataset=_UpperCAmelCase     , token_probs=_UpperCAmelCase     , student=_UpperCAmelCase     , teacher=_UpperCAmelCase			)
				distiller.train()
				logger.info("""Let's go get some drinks."""			)


if __name__ == "__main__":
							main()












import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class  __A       (  a      ,   unittest.TestCase      ):
				__A								=			BioGptTokenizer
				__A								=			False


				def _snake_case    (						self							):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase			=[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase			=dict(zip(UpperCAmelCase_				,	range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase			=["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""merges_file"""]							)
								with open(self.vocab_file				,	"""w"""							) as fp:
												fp.write(json.dumps(UpperCAmelCase_							)							)
								with open(self.merges_file				,	"""w"""							) as fp:
												fp.write("""\n""".join(UpperCAmelCase_							)							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""lower newer"""
								lowerCamelCase			="""lower newer"""
								return input_text, output_text


				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer(self.vocab_file				,	self.merges_file							)

								lowerCamelCase			="""lower"""
								lowerCamelCase			=["""low""", """er</w>"""]
								lowerCamelCase			=tokenizer.tokenize(UpperCAmelCase_							)
								self.assertListEqual(UpperCAmelCase_				,	UpperCAmelCase_							)

								lowerCamelCase			=tokens + ["""<unk>"""]
								lowerCamelCase			=[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)				,	UpperCAmelCase_							)


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=BioGptTokenizer.from_pretrained("""microsoft/biogpt"""							)

								lowerCamelCase			=tokenizer.encode("""sequence builders"""				,	add_special_tokens=UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.encode("""multi-sequence build"""				,	add_special_tokens=UpperCAmelCase_							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,	UpperCAmelCase_							)

								self.assertTrue(encoded_sentence == [2] + text							)
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a							)





