
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


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






def 							_lowercase							(     UpperCamelCase_		)		->		Union[str, 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


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






def 							_lowercase							(     )		->		List[str]:
					'''simple docstring'''


					SCREAMING_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=0.15	,	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.'		)
					SCREAMING_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		)

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

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

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

					SCREAMING_SNAKE_CASE__													= LmSeqsDataset(params=UpperCamelCase_	,	data=UpperCamelCase_		)
					logger.info('Data loader created.'		)

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

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

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

					# TEACHER #
					SCREAMING_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()
					SCREAMING_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()







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= type_vocab_size
						SCREAMING_SNAKE_CASE__													= initializer_range
						SCREAMING_SNAKE_CASE__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





