




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



def        __a							(  __UpperCAmelCase		:						List[Any]      ) ->					int:
							"""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        __a							(  __UpperCAmelCase		:						int   ,		__UpperCAmelCase		:						Any      ) ->					Union[str, Any]:
							"""simple docstring"""




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



def        __a							(  __UpperCAmelCase		:						List[str]   ,		__UpperCAmelCase		:						Dict      ) ->					Tuple:
							"""simple docstring"""




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



def        __a							(  ) ->					Tuple:
							"""simple docstring"""




							lowerCamelCase_			:    Optional[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=0.1_5   ,		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=0.0_5   ,		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=0.0_2   ,		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."      )
							lowerCamelCase_			:    List[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      )

							lowerCamelCase_						,						lowerCamelCase_						,						lowerCamelCase_			:    int   						=      MODEL_CLASSES[args.student_type]
							lowerCamelCase_						,						lowerCamelCase_						,						lowerCamelCase_			:    Dict   						=      MODEL_CLASSES[args.teacher_type]

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

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

							lowerCamelCase_			:    str   						=      LmSeqsDataset(params=UpperCamelCase__   ,		data=UpperCamelCase__      )
							logger.info("Data loader created."      )

							# STUDENT #
							logger.info(f"Loading student config from {args.student_config}"      )
							lowerCamelCase_			:    Tuple   						=      student_config_class.from_pretrained(args.student_config      )
							lowerCamelCase_			:    Any   						=      True

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

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

							# TEACHER #
							lowerCamelCase_			:    Optional[int]   						=      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()
							lowerCamelCase_			:    Optional[Any]   						=      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()


'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def    lowerCAmelCase       (		UpperCamelCase__					: dict							):


	"""simple docstring"""
	return (data["data"], data["target"])





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      xgboost(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	# Error printing
	print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)
	print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()
