





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  __magic_name__  (				lowercase				)				-> Optional[Any]:

							"""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  __magic_name__  (				lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							if args.student_type == "roberta":
														lowercase_	:   int  				=     False
							elif args.student_type == "gpt2":
														lowercase_	:   List[str]  				=     False





def  __magic_name__  (				lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							if args.student_type == "roberta":
														lowercase_	:   List[Any]  				=     False





def  __magic_name__  (				)				-> str:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     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=lowercase				,       required=lowercase				,       help="""The output directory (log, checkpoints, parameters, etc.)"""				)
							parser.add_argument(
							    """--data_file"""				,       type=lowercase				,       required=lowercase				,       help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""				,       )

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

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

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

							parser.add_argument("""--log_interval"""				,       type=lowercase				,       default=500				,       help="""Tensorboard logging interval."""				)
							parser.add_argument("""--checkpoint_interval"""				,       type=lowercase				,       default=4000				,       help="""Checkpoint interval."""				)
							lowercase_	:   Optional[Any]  				=     parser.parse_args()
							sanity_checks(lowercase				)

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

							lowercase_				,		lowercase_				,		lowercase_	:   str  				=     MODEL_CLASSES[args.student_type]
							lowercase_				,		lowercase_				,		lowercase_	:   Dict  				=     MODEL_CLASSES[args.teacher_type]

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

							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[Any]  				=     pickle.load(lowercase				)

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

							lowercase_	:   str  				=     LmSeqsDataset(params=lowercase				,       data=lowercase				)
							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_	:   Dict  				=     True

							if args.student_pretrained_weights is not None:
														logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""				)
														lowercase_	:   List[str]  				=     student_model_class.from_pretrained(args.student_pretrained_weights				,       config=lowercase				)
							else:
														lowercase_	:   str  				=     student_model_class(lowercase				)

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

							# TEACHER #
							lowercase_	:   List[str]  				=     teacher_model_class.from_pretrained(args.teacher_name				,       output_hidden_states=lowercase				)
							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(lowercase				,       lowercase				)
							if args.freeze_token_type_embds:
														freeze_token_type_embeddings(lowercase				,       lowercase				)

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


if __name__ == "__main__":
							main()






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask