
'''simple docstring'''







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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Dict  ):
			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		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
			if args.student_type == "roberta":
						_SCREAMING_SNAKE_CASE				= False
			elif args.student_type == "gpt2":
						_SCREAMING_SNAKE_CASE				= False



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
			if args.student_type == "roberta":
						_SCREAMING_SNAKE_CASE				= False



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

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

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

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

			parser.add_argument("--log_interval"	,			type=SCREAMING_SNAKE_CASE_	,			default=500	,			help="Tensorboard logging interval."  )
			parser.add_argument("--checkpoint_interval"	,			type=SCREAMING_SNAKE_CASE_	,			default=4000	,			help="Checkpoint interval."  )
			_SCREAMING_SNAKE_CASE				= parser.parse_args()
			sanity_checks(SCREAMING_SNAKE_CASE_  )

			# ARGS #
			init_gpu_params(SCREAMING_SNAKE_CASE_  )
			set_seed(SCREAMING_SNAKE_CASE_  )
			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(SCREAMING_SNAKE_CASE_  )	,			SCREAMING_SNAKE_CASE_	,			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(SCREAMING_SNAKE_CASE_  )
						_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(SCREAMING_SNAKE_CASE_  )

			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(SCREAMING_SNAKE_CASE_  )

						_SCREAMING_SNAKE_CASE				= np.maximum(SCREAMING_SNAKE_CASE_	,			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(SCREAMING_SNAKE_CASE_  )
			else:
						_SCREAMING_SNAKE_CASE				= None

			_SCREAMING_SNAKE_CASE				= LmSeqsDataset(params=SCREAMING_SNAKE_CASE_	,			data=SCREAMING_SNAKE_CASE_  )
			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=SCREAMING_SNAKE_CASE_  )
			else:
						_SCREAMING_SNAKE_CASE				= student_model_class(SCREAMING_SNAKE_CASE_  )

			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=SCREAMING_SNAKE_CASE_  )
			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(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_  )
			if args.freeze_token_type_embds:
						freeze_token_type_embeddings(SCREAMING_SNAKE_CASE_	,			SCREAMING_SNAKE_CASE_  )

			# 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=SCREAMING_SNAKE_CASE_	,			dataset=SCREAMING_SNAKE_CASE_	,			token_probs=SCREAMING_SNAKE_CASE_	,			student=SCREAMING_SNAKE_CASE_	,			teacher=SCREAMING_SNAKE_CASE_  )
			distiller.train()
			logger.info("Let's go get some drinks."  )


if __name__ == "__main__":
						main()

'''simple docstring'''







import numpy

# List of input, output pairs
_snake_case				:   Optional[Any]     =				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case				:   Any     =				(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case				:   Any     =				[2, 4, 1, 5]
_snake_case				:   str     =				len(train_data)
_snake_case				:   int     =				0.009



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]="train"  ):
						return calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) - output(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(len(_SCREAMING_SNAKE_CASE  ) - 1  ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str  ):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]  )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]  )
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=m  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(_SCREAMING_SNAKE_CASE  ):
												if index == -1:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  )
												else:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  ) * train_data[i][0][index]
						return summation_value



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= summation_of_cost_derivative(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) / m
						return cost_derivative_value



def 			_a		(	):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_SCREAMING_SNAKE_CASE				= 0.000_002
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						while True:
												j += 1
												_SCREAMING_SNAKE_CASE				= [0, 0, 0, 0]
												for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )  ):
																		_SCREAMING_SNAKE_CASE				= get_cost_derivative(i - 1  )
																		_SCREAMING_SNAKE_CASE				= (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			atol=_SCREAMING_SNAKE_CASE	,			rtol=_SCREAMING_SNAKE_CASE	,			):
																		break
												_SCREAMING_SNAKE_CASE				= temp_parameter_vector
						print(("Number of iterations:", j)  )



def 			_a		(	):
						for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
												print(("Actual output value:", output(_SCREAMING_SNAKE_CASE	,			"test"  ))  )
												print(("Hypothesis output:", calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			"test"  ))  )


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()