
'''simple docstring'''







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


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







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Optional[int]:
	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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Tuple:





	if args.student_type == "roberta":
		_snake_case   						=		False
	elif args.student_type == "gpt2":
		_snake_case   						=		False







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Tuple:





	if args.student_type == "roberta":
		_snake_case   						=		False







def 				SCREAMING_SNAKE_CASE__	(   )       ->	str:
	_snake_case   						=		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=500			,				help='Tensorboard logging interval.'	)
	parser.add_argument('--checkpoint_interval'			,				type=__UpperCamelCase			,				default=4_000			,				help='Checkpoint interval.'	)
	_snake_case   						=		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	)

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

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

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

	_snake_case   						=		LmSeqsDataset(params=__UpperCamelCase			,				data=__UpperCamelCase	)
	logger.info('Data loader created.'	)

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

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

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

	# TEACHER #
	_snake_case   						=		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()
	_snake_case   						=		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()


# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


__lowerCamelCase			: Union[str, Any]							   =		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
   import torch

   print('''Torch version:''', torch.__version__)
   print('''Cuda available:''', torch.cuda.is_available())
   print('''Cuda version:''', torch.version.cuda)
   print('''CuDNN version:''', torch.backends.cudnn.version())
   print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
   print('''Torch version:''', None)

try:
   import transformers

   print('''transformers version:''', transformers.__version__)
except ImportError:
   print('''transformers version:''', None)
