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



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int]  )   ->			List[str]:






						'''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 		UpperCamelCase     (       _UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Tuple  )   ->			List[str]:






						'''simple docstring'''

						if args.student_type == "roberta":
												_lowercase       :	int           =   False
						elif args.student_type == "gpt2":
												_lowercase       :	Any           =   False



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Tuple  )   ->			int:






						'''simple docstring'''

						if args.student_type == "roberta":
												_lowercase       :	Tuple           =   False



def 		UpperCamelCase     (       )   ->			Dict:






						'''simple docstring'''

						_lowercase       :	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=_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=0.15 ,			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=0.05 ,			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=0.02 ,			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=4000 ,			help="Checkpoint interval."  )
						_lowercase       :	Optional[Any]           =   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  )

						_lowercase      ,    _lowercase      ,    _lowercase       :	Dict           =   MODEL_CLASSES[args.student_type]
						_lowercase      ,    _lowercase      ,    _lowercase       :	Tuple           =   MODEL_CLASSES[args.teacher_type]

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

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

						_lowercase       :	Optional[Any]           =   LmSeqsDataset(params=_UpperCAmelCase ,			data=_UpperCAmelCase  )
						logger.info("Data loader created."  )

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

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""  )
												_lowercase       :	Union[str, Any]           =   student_model_class.from_pretrained(args.student_pretrained_weights ,			config=_UpperCAmelCase  )
						else:
												_lowercase       :	Optional[Any]           =   student_model_class(_UpperCAmelCase  )

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

						# TEACHER #
						_lowercase       :	Optional[Any]           =   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()
						_lowercase       :	Dict           =   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()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_						:     Optional[int]	       =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




