

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


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




def 					_lowercase     (				a_				:					Optional[int]				)				->	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 					_lowercase     (				a_				:					List[str]					,a_				:					List[Any]				)				->	int:




					'''simple docstring'''







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




def 					_lowercase     (				a_				:					Union[str, Any]					,a_				:					str				)				->	Tuple:




					'''simple docstring'''







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




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




					'''simple docstring'''







					__magic_name__							=    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=a_					,required=a_					,help='The output directory (log, checkpoints, parameters, etc.)'				)
					parser.add_argument(
					    '--data_file'					,type=a_					,required=a_					,help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'					,)

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

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

					parser.add_argument('--temperature'					,default=2.0					,type=a_					,help='Temperature for the softmax temperature.'				)
					parser.add_argument(
					    '--alpha_ce'					,default=0.5					,type=a_					,help='Linear weight for the distillation loss. Must be >=0.'				)
					parser.add_argument(
					    '--alpha_mlm'					,default=0.0					,type=a_					,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=a_					,help='Linear weight for the CLM loss. Must be >=0.'				)
					parser.add_argument('--alpha_mse'					,default=0.0					,type=a_					,help='Linear weight of the MSE loss. Must be >=0.'				)
					parser.add_argument(
					    '--alpha_cos'					,default=0.0					,type=a_					,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=a_					,help='Proportion of tokens for which we need to make a prediction.'					,)
					parser.add_argument('--word_mask'					,default=0.8					,type=a_					,help='Proportion of tokens to mask out.'				)
					parser.add_argument('--word_keep'					,default=0.1					,type=a_					,help='Proportion of tokens to keep.'				)
					parser.add_argument('--word_rand'					,default=0.1					,type=a_					,help='Proportion of tokens to randomly replace.'				)
					parser.add_argument(
					    '--mlm_smoothing'					,default=0.7					,type=a_					,help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'					,)
					parser.add_argument('--token_counts'					,type=a_					,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=a_					,default=3					,help='Number of pass on the whole dataset.'				)
					parser.add_argument('--batch_size'					,type=a_					,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=a_					,default=5_0					,help='Gradient accumulation for larger training batches.'					,)
					parser.add_argument('--warmup_prop'					,default=0.05					,type=a_					,help='Linear warmup proportion.'				)
					parser.add_argument('--weight_decay'					,default=0.0					,type=a_					,help='Weight decay if we apply some.'				)
					parser.add_argument('--learning_rate'					,default=5e-4					,type=a_					,help='The initial learning rate for Adam.'				)
					parser.add_argument('--adam_epsilon'					,default=1e-6					,type=a_					,help='Epsilon for Adam optimizer.'				)
					parser.add_argument('--max_grad_norm'					,default=5.0					,type=a_					,help='Max gradient norm.'				)
					parser.add_argument('--initializer_range'					,default=0.02					,type=a_					,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=a_					,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=a_					,default=1					,help='Number of GPUs in the node.'				)
					parser.add_argument('--local_rank'					,type=a_					,default=-1					,help='Distributed training - Local rank'				)
					parser.add_argument('--seed'					,type=a_					,default=5_6					,help='Random seed'				)

					parser.add_argument('--log_interval'					,type=a_					,default=5_0_0					,help='Tensorboard logging interval.'				)
					parser.add_argument('--checkpoint_interval'					,type=a_					,default=4_0_0_0					,help='Checkpoint interval.'				)
					__magic_name__							=    parser.parse_args()
					sanity_checks(a_				)

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

					__magic_name__							=    MODEL_CLASSES[args.student_type]
					__magic_name__							=    MODEL_CLASSES[args.teacher_type]

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

					if args.mlm:
										logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)'				)
										with open(args.token_counts					,'rb'				) as fp:
															__magic_name__							=    pickle.load(a_				)

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

					__magic_name__							=    LmSeqsDataset(params=a_					,data=a_				)
					logger.info('Data loader created.'				)

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

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

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

					# TEACHER #
					__magic_name__							=    teacher_model_class.from_pretrained(args.teacher_name					,output_hidden_states=a_				)
					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(a_					,a_				)
					if args.freeze_token_type_embds:
										freeze_token_type_embeddings(a_					,a_				)

					# 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()
					__magic_name__							=    Distiller(
					    params=a_					,dataset=a_					,token_probs=a_					,student=a_					,teacher=a_				)
					distiller.train()
					logger.info('Let\'s go get some drinks.'				)


if __name__ == "__main__":
						main()







import os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


if __name__ == "__main__":
						print(f'''{solution() = }''')




