



'''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


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



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		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      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		if args.student_type == "roberta":
				_a     :      int        =							False
		elif args.student_type == "gpt2":
				_a     :      str        =							False



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		if args.student_type == "roberta":
				_a     :      Optional[int]        =							False



def      __lowerCamelCase     (      )			->      str:
		_a     :      Dict        =							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=lowerCAmelCase_						,			required=lowerCAmelCase_						,			help='The output directory (log, checkpoints, parameters, etc.)'  )
		parser.add_argument(
		    '--data_file'						,			type=lowerCAmelCase_						,			required=lowerCAmelCase_						,			help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'						,			)

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

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

		parser.add_argument('--temperature'						,			default=2.0						,			type=lowerCAmelCase_						,			help='Temperature for the softmax temperature.'  )
		parser.add_argument(
		    '--alpha_ce'						,			default=0.5						,			type=lowerCAmelCase_						,			help='Linear weight for the distillation loss. Must be >=0.'  )
		parser.add_argument(
		    '--alpha_mlm'						,			default=0.0						,			type=lowerCAmelCase_						,			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=lowerCAmelCase_						,			help='Linear weight for the CLM loss. Must be >=0.'  )
		parser.add_argument('--alpha_mse'						,			default=0.0						,			type=lowerCAmelCase_						,			help='Linear weight of the MSE loss. Must be >=0.'  )
		parser.add_argument(
		    '--alpha_cos'						,			default=0.0						,			type=lowerCAmelCase_						,			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=0.15						,			type=lowerCAmelCase_						,			help='Proportion of tokens for which we need to make a prediction.'						,			)
		parser.add_argument('--word_mask'						,			default=0.8						,			type=lowerCAmelCase_						,			help='Proportion of tokens to mask out.'  )
		parser.add_argument('--word_keep'						,			default=0.1						,			type=lowerCAmelCase_						,			help='Proportion of tokens to keep.'  )
		parser.add_argument('--word_rand'						,			default=0.1						,			type=lowerCAmelCase_						,			help='Proportion of tokens to randomly replace.'  )
		parser.add_argument(
		    '--mlm_smoothing'						,			default=0.7						,			type=lowerCAmelCase_						,			help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'						,			)
		parser.add_argument('--token_counts'						,			type=lowerCAmelCase_						,			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=lowerCAmelCase_						,			default=3						,			help='Number of pass on the whole dataset.'  )
		parser.add_argument('--batch_size'						,			type=lowerCAmelCase_						,			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=lowerCAmelCase_						,			default=50						,			help='Gradient accumulation for larger training batches.'						,			)
		parser.add_argument('--warmup_prop'						,			default=0.05						,			type=lowerCAmelCase_						,			help='Linear warmup proportion.'  )
		parser.add_argument('--weight_decay'						,			default=0.0						,			type=lowerCAmelCase_						,			help='Weight decay if we apply some.'  )
		parser.add_argument('--learning_rate'						,			default=5E-4						,			type=lowerCAmelCase_						,			help='The initial learning rate for Adam.'  )
		parser.add_argument('--adam_epsilon'						,			default=1E-6						,			type=lowerCAmelCase_						,			help='Epsilon for Adam optimizer.'  )
		parser.add_argument('--max_grad_norm'						,			default=5.0						,			type=lowerCAmelCase_						,			help='Max gradient norm.'  )
		parser.add_argument('--initializer_range'						,			default=0.02						,			type=lowerCAmelCase_						,			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=lowerCAmelCase_						,			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=lowerCAmelCase_						,			default=1						,			help='Number of GPUs in the node.'  )
		parser.add_argument('--local_rank'						,			type=lowerCAmelCase_						,			default=-1						,			help='Distributed training - Local rank'  )
		parser.add_argument('--seed'						,			type=lowerCAmelCase_						,			default=56						,			help='Random seed'  )

		parser.add_argument('--log_interval'						,			type=lowerCAmelCase_						,			default=500						,			help='Tensorboard logging interval.'  )
		parser.add_argument('--checkpoint_interval'						,			type=lowerCAmelCase_						,			default=4000						,			help='Checkpoint interval.'  )
		_a     :      Tuple        =							parser.parse_args()
		sanity_checks(lowerCAmelCase_  )

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

		_a       ,					_a       ,					_a     :      Dict        =							MODEL_CLASSES[args.student_type]
		_a       ,					_a       ,					_a     :      List[Any]        =							MODEL_CLASSES[args.teacher_type]

		# TOKENIZER #
		_a     :      Dict        =							teacher_tokenizer_class.from_pretrained(args.teacher_name  )
		_a     :      Tuple        =							{}
		for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
				_a     :      Union[str, Any]        =							tokenizer.all_special_tokens.index(lowerCAmelCase_  )
				_a     :      int        =							tokenizer.all_special_ids[idx]
		logger.info(f"""Special tokens {special_tok_ids}"""  )
		_a     :      Dict        =							special_tok_ids
		_a     :      Optional[int]        =							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     :      Optional[Any]        =							pickle.load(lowerCAmelCase_  )

		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     :      List[Any]        =							pickle.load(lowerCAmelCase_  )

				_a     :      str        =							np.maximum(lowerCAmelCase_						,			1  ) ** -args.mlm_smoothing
				for idx in special_tok_ids.values():
						_a     :      Optional[int]        =							0.0  # do not predict special tokens
				_a     :      Optional[Any]        =							torch.from_numpy(lowerCAmelCase_  )
		else:
				_a     :      Optional[int]        =							None

		_a     :      Optional[int]        =							LmSeqsDataset(params=lowerCAmelCase_						,			data=lowerCAmelCase_  )
		logger.info('Data loader created.'  )

		# STUDENT #
		logger.info(f"""Loading student config from {args.student_config}"""  )
		_a     :      List[Any]        =							student_config_class.from_pretrained(args.student_config  )
		_a     :      List[Any]        =							True

		if args.student_pretrained_weights is not None:
				logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
				_a     :      int        =							student_model_class.from_pretrained(args.student_pretrained_weights						,			config=lowerCAmelCase_  )
		else:
				_a     :      Dict        =							student_model_class(lowerCAmelCase_  )

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

		# TEACHER #
		_a     :      Tuple        =							teacher_model_class.from_pretrained(args.teacher_name						,			output_hidden_states=lowerCAmelCase_  )
		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(lowerCAmelCase_						,			lowerCAmelCase_  )
		if args.freeze_token_type_embds:
				freeze_token_type_embeddings(lowerCAmelCase_						,			lowerCAmelCase_  )

		# 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     :      Tuple        =							Distiller(
		    params=lowerCAmelCase_						,			dataset=lowerCAmelCase_						,			token_probs=lowerCAmelCase_						,			student=lowerCAmelCase_						,			teacher=lowerCAmelCase_  )
		distiller.train()
		logger.info('Let\'s go get some drinks.'  )


if __name__ == "__main__":
							main()









'''simple docstring'''

import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch

class 	__magic_name__						(    _UpperCamelCase   ):







							@require_torch
							def 							__lowercase			(							self					:       Tuple		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Optional[int]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      List[str]        =							'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

									_a     :      Tuple        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

									# Force fetching the files so that we can use the cache
									_a     :      List[Any]        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[int]        =							[sys.executable, '-c', '\n'.join([load, run, mock]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      int        =							'1'
									_a     :      List[Any]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       Any		):
									# python one-liner segments
									# this must be loaded before socket.socket is monkey-patched
									_a     :      Dict        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      Optional[int]        =							'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

									_a     :      Optional[Any]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

									# Force fetching the files so that we can use the cache
									_a     :      int        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[int]        =							[sys.executable, '-c', '\n'.join([load, run, mock]		)]

									# should succeed
									_a     :      str        =							self.get_env()
									_a     :      Optional[Any]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Union[str, Any]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

									_a     :      Optional[Any]        =							'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

									_a     :      str        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

									# baseline - just load from_pretrained with normal network
									_a     :      Optional[Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Dict        =							self.get_env()
									_a     :      int        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)

									# next emulate no network
									_a     :      List[Any]        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]

									# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
									# env["TRANSFORMERS_OFFLINE"] = "0"
									# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
									# self.assertEqual(result.returncode, 1, result.stderr)

									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      int        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[Any]        =							'\nfrom transformers import pipeline\n        '
									_a     :      str        =							'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

									_a     :      List[str]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
									_a     :      List[Any]        =							self.get_env()
									_a     :      Dict        =							'1'
									_a     :      Dict        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]
									_a     :      str        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,1	,result.stderr		)
									self.assertIn(
									    'You cannot infer task automatically within `pipeline` when using offline mode'	,result.stderr.decode().replace('\n'	,''		)	,)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[int]        =							'\nfrom transformers import AutoModel\n        '
									_a     :      List[Any]        =							'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

									# baseline - just load from_pretrained with normal network
									_a     :      Union[str, Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									_a     :      List[str]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)

									# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
									_a     :      Optional[Any]        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)




