



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							 =					{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}

def 			A_      (	__a     :				Tuple		):

				"""simple docstring"""
				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 			A_      (	__a     :				Optional[Any] ,		__a     :				int		):

				"""simple docstring"""
				if args.student_type == "roberta":
								a__				      =		False
				elif args.student_type == "gpt2":
								a__				      =		False

def 			A_      (	__a     :				Tuple ,		__a     :				List[Any]		):

				"""simple docstring"""
				if args.student_type == "roberta":
								a__				      =		False

def 			A_      (	):

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

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

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

				parser.add_argument("""--temperature""" ,		default=2.0 ,		type=__a ,		help="""Temperature for the softmax temperature."""		)
				parser.add_argument(
				    """--alpha_ce""" ,		default=0.5 ,		type=__a ,		help="""Linear weight for the distillation loss. Must be >=0."""		)
				parser.add_argument(
				    """--alpha_mlm""" ,		default=0.0 ,		type=__a ,		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=__a ,		help="""Linear weight for the CLM loss. Must be >=0."""		)
				parser.add_argument("""--alpha_mse""" ,		default=0.0 ,		type=__a ,		help="""Linear weight of the MSE loss. Must be >=0."""		)
				parser.add_argument(
				    """--alpha_cos""" ,		default=0.0 ,		type=__a ,		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=__a ,		help="""Proportion of tokens for which we need to make a prediction.""" ,		)
				parser.add_argument("""--word_mask""" ,		default=0.8 ,		type=__a ,		help="""Proportion of tokens to mask out."""		)
				parser.add_argument("""--word_keep""" ,		default=0.1 ,		type=__a ,		help="""Proportion of tokens to keep."""		)
				parser.add_argument("""--word_rand""" ,		default=0.1 ,		type=__a ,		help="""Proportion of tokens to randomly replace."""		)
				parser.add_argument(
				    """--mlm_smoothing""" ,		default=0.7 ,		type=__a ,		help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" ,		)
				parser.add_argument("""--token_counts""" ,		type=__a ,		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=__a ,		default=3 ,		help="""Number of pass on the whole dataset."""		)
				parser.add_argument("""--batch_size""" ,		type=__a ,		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=__a ,		default=50 ,		help="""Gradient accumulation for larger training batches.""" ,		)
				parser.add_argument("""--warmup_prop""" ,		default=0.0_5 ,		type=__a ,		help="""Linear warmup proportion."""		)
				parser.add_argument("""--weight_decay""" ,		default=0.0 ,		type=__a ,		help="""Weight decay if we apply some."""		)
				parser.add_argument("""--learning_rate""" ,		default=5e-4 ,		type=__a ,		help="""The initial learning rate for Adam."""		)
				parser.add_argument("""--adam_epsilon""" ,		default=1e-6 ,		type=__a ,		help="""Epsilon for Adam optimizer."""		)
				parser.add_argument("""--max_grad_norm""" ,		default=5.0 ,		type=__a ,		help="""Max gradient norm."""		)
				parser.add_argument("""--initializer_range""" ,		default=0.0_2 ,		type=__a ,		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=__a ,		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=__a ,		default=1 ,		help="""Number of GPUs in the node."""		)
				parser.add_argument("""--local_rank""" ,		type=__a ,		default=-1 ,		help="""Distributed training - Local rank"""		)
				parser.add_argument("""--seed""" ,		type=__a ,		default=56 ,		help="""Random seed"""		)

				parser.add_argument("""--log_interval""" ,		type=__a ,		default=500 ,		help="""Tensorboard logging interval."""		)
				parser.add_argument("""--checkpoint_interval""" ,		type=__a ,		default=4_000 ,		help="""Checkpoint interval."""		)
				a__				      =		parser.parse_args()
				sanity_checks(__a		)

				# ARGS #
				init_gpu_params(__a		)
				set_seed(__a		)
				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(__a		) ,		__a ,		indent=4		)
								git_log(args.dump_path		)

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

				# TOKENIZER #
				a__				      =		teacher_tokenizer_class.from_pretrained(args.teacher_name		)
				a__				      =		{}
				for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
								a__				      =		tokenizer.all_special_tokens.index(__a		)
								a__				      =		tokenizer.all_special_ids[idx]
				logger.info(F'''Special tokens {special_tok_ids}'''		)
				a__				      =		special_tok_ids
				a__				      =		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:
								a__				      =		pickle.load(__a		)

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

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

				a__				      =		LmSeqsDataset(params=__a ,		data=__a		)
				logger.info("""Data loader created."""		)

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

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

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

				# TEACHER #
				a__				      =		teacher_model_class.from_pretrained(args.teacher_name ,		output_hidden_states=__a		)
				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(__a ,		__a		)
				if args.freeze_token_type_embds:
								freeze_token_type_embeddings(__a ,		__a		)

				# 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()
				a__				      =		Distiller(
				    params=__a ,		dataset=__a ,		token_probs=__a ,		student=__a ,		teacher=__a		)
				distiller.train()
				logger.info("""Let's go get some drinks."""		)


if __name__ == "__main__":
	main()






def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		set()

				# Replace all the whitespace in our sentence
				a__				      =		input_str.replace(""" """ ,		""""""		)
				for alpha in input_str:
								if "a" <= alpha.lower() <= "z":
												frequency.add(alpha.lower()		)
				return len(__a		) == 26

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				a__				      =		[False] * 26
				for char in input_str:
								if char.islower():
												a__				      =		True
								elif char.isupper():
												a__				      =		True
				return all(__a		)

def 			A_      (	__a     :				str = "The quick brown fox jumps over the lazy dog" ,		):

				"""simple docstring"""
				return len({char for char in input_str.lower() if char.isalpha()}		) == 26

def 			A_      (	):

				"""simple docstring"""
				from timeit import timeit

				a__				      =		"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
				print(timeit("""is_pangram()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_faster()""" ,		setup=__a		)		)
				print(timeit("""is_pangram_fastest()""" ,		setup=__a		)		)
				# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
				# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()

