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


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





def  _lowerCamelCase					(   a_				: Optional[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  _lowerCamelCase					(   a_				: Optional[int]						,			a_				: List[Any]):
			if args.student_type == "roberta":
						lowerCamelCase						:Dict						  =     False
			elif args.student_type == "gpt2":
						lowerCamelCase						:str						  =     False





def  _lowerCamelCase					(   a_				: Any						,			a_				: int):
			if args.student_type == "roberta":
						lowerCamelCase						:Union[str, Any]						  =     False





def  _lowerCamelCase					(   ):
			lowerCamelCase						:List[Any]						  =     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=0.15						,			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=0.05						,			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=0.02						,			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=5_00						,			help='''Tensorboard logging interval.''')
			parser.add_argument('''--checkpoint_interval'''						,			type=a_						,			default=40_00						,			help='''Checkpoint interval.''')
			lowerCamelCase						:List[Any]						  =     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)

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

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

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

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

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

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

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

			# TEACHER #
			lowerCamelCase						:str						  =     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()
			lowerCamelCase						:Optional[Any]						  =     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()




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     ''
							_UpperCAmelCase						    =     (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							_UpperCAmelCase						    =     None  # compression type in fsspec. ex: "gzip"
							_UpperCAmelCase						    =     None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







							def __init__(				self     :    str	,					__snake_case     :    str = ""	,					__snake_case     :    Optional[str] = None	,					__snake_case     :    Optional[dict] = None	,					**__snake_case     :    Dict		):
										super().__init__(self	,					**__snake_case		)
										# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
										lowerCamelCase						:Optional[Any]						  =     fsspec.open(
										    __snake_case	,					mode='''rb'''	,					protocol=__snake_case	,					compression=self.compression	,					client_kwargs={
										        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
										        '''trust_env''': True,  # Enable reading proxy env variables.
										        **(target_options or {}).pop('''client_kwargs'''	,					{}		),  # To avoid issues if it was already passed.
										    }	,					**(target_options or {})	,					)
										lowerCamelCase						:List[str]						  =     os.path.basename(self.file.path.split('''::'''		)[0]		)
										lowerCamelCase						:Dict						  =     (
										    self.compressed_name[: self.compressed_name.rindex('''.'''		)]
										    if '''.''' in self.compressed_name
										    else self.compressed_name
										)
										lowerCamelCase						:List[str]						  =     None







							@classmethod
							def 				snake_case  (				cls     :    Any	,					__snake_case     :    Any		):
										# compressed file paths are always relative to the archive root
										return super()._strip_protocol(__snake_case		).lstrip('''/'''		)







							def 				snake_case  (				self     :    Any		):
										if self.dir_cache is None:
													lowerCamelCase						:Optional[Any]						  =     {**self.file.fs.info(self.file.path		), '''name''': self.uncompressed_name}
													lowerCamelCase						:Optional[Any]						  =     {f['''name''']: f}







							def 				snake_case  (				self     :    Union[str, Any]	,					__snake_case     :    str		):
										return self.file.open().read()







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    str	,					__snake_case     :    str = "rb"	,					__snake_case     :    int=None	,					__snake_case     :    Optional[int]=True	,					__snake_case     :    str=None	,					**__snake_case     :    str	,					):
										lowerCamelCase						:List[str]						  =     self._strip_protocol(__snake_case		)
										if mode != "rb":
													raise ValueError(F"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"		)
										return self.file.open()


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'bz2'
							_UpperCAmelCase						    =     'bz2'
							_UpperCAmelCase						    =     '.bz2'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'gzip'
							_UpperCAmelCase						    =     'gzip'
							_UpperCAmelCase						    =     '.gz'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'lz4'
							_UpperCAmelCase						    =     'lz4'
							_UpperCAmelCase						    =     '.lz4'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'xz'
							_UpperCAmelCase						    =     'xz'
							_UpperCAmelCase						    =     '.xz'


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'zstd'
							_UpperCAmelCase						    =     'zstd'
							_UpperCAmelCase						    =     '.zst'







							def __init__(				self     :    str	,					__snake_case     :    str	,					__snake_case     :    str = "rb"	,					__snake_case     :    Optional[str] = None	,					__snake_case     :    Optional[dict] = None	,					__snake_case     :    int = DEFAULT_BLOCK_SIZE	,					**__snake_case     :    int	,					):
										super().__init__(
										    fo=__snake_case	,					mode=__snake_case	,					target_protocol=__snake_case	,					target_options=__snake_case	,					block_size=__snake_case	,					**__snake_case	,					)
										# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
										#
										# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
										#     out.close = close
										# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
										#
										# see https://github.com/intake/filesystem_spec/issues/725
										lowerCamelCase						:Tuple						  =     self.file.__enter__
										class   _lowerCAmelCase		:







																	def __init__(				self     :    Dict	,					__snake_case     :    Tuple		):
																				lowerCamelCase						:Optional[int]						  =     file_







																	def __enter__(				self     :    Optional[int]		):
																				self._file.__enter__()
																				return self







																	def __exit__(				self     :    str	,					*__snake_case     :    Optional[Any]	,					**__snake_case     :    List[Any]		):
																				self._file.__exit__(*__snake_case	,					**__snake_case		)







																	def __iter__(				self     :    Optional[Any]		):
																				return iter(self._file		)







																	def 				snake_case  (				self     :    List[Any]		):
																				return next(self._file		)







																	def __getattr__(				self     :    Any	,					__snake_case     :    str		):
																				return getattr(self._file	,					__snake_case		)

										def fixed_enter(*__snake_case     :    Optional[int]	,					**__snake_case     :    str		):
													return WrappedFile(_enter(*__snake_case	,					**__snake_case		)		)

										lowerCamelCase						:Dict						  =     fixed_enter



