



'''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_					:      Optional[int]    			=    {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def A__       (	UpperCAmelCase_				):
						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_    ,    UpperCAmelCase_				):
						if args.student_type == "roberta":
												_UpperCamelCase   :						Optional[Any]												=  False
						elif args.student_type == "gpt2":
												_UpperCamelCase   :						Union[str, Any]												=  False






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						if args.student_type == "roberta":
												_UpperCamelCase   :						str												=  False






def A__       (	):
						_UpperCamelCase   :						Optional[int]												=  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=5_0    ,    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=5_6    ,    help='Random seed'				)

						parser.add_argument('--log_interval'    ,    type=UpperCAmelCase_    ,    default=5_0_0    ,    help='Tensorboard logging interval.'				)
						parser.add_argument('--checkpoint_interval'    ,    type=UpperCAmelCase_    ,    default=4_0_0_0    ,    help='Checkpoint interval.'				)
						_UpperCamelCase   :						Optional[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				)

						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  MODEL_CLASSES[args.student_type]
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  MODEL_CLASSES[args.teacher_type]

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

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

						_UpperCamelCase   :						str												=  LmSeqsDataset(params=UpperCAmelCase_    ,    data=UpperCAmelCase_				)
						logger.info('Data loader created.'				)

						# STUDENT #
						logger.info(f'Loading student config from {args.student_config}'				)
						_UpperCamelCase   :						Tuple												=  student_config_class.from_pretrained(args.student_config				)
						_UpperCamelCase   :						Optional[int]												=  True

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

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

						# TEACHER #
						_UpperCamelCase   :						Dict												=  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()
						_UpperCamelCase   :						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 unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 				lowercase__      (				unittest.TestCase  ):





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Optional[int]												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Dict												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													with self.subTest(lowerCamelCase__					):
																			_UpperCamelCase   :						Any												=  AutoConfig.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)

																			_UpperCamelCase   :						Any												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					)
																			self.assertIsNotNone(lowerCamelCase__					)
																			self.assertIsInstance(lowerCamelCase__					,lowerCamelCase__					)





	@slow
	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							for model_name in ["bert-base-cased", "bert-large-uncased"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						List[Any]												=  FlaxBertModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[int]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	@slow
	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							for model_name in ["roberta-base", "roberta-large"]:
													_UpperCamelCase   :						Optional[int]												=  AutoTokenizer.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Tuple												=  FlaxRobertaModel.from_pretrained(lowerCamelCase__					)
													_UpperCamelCase   :						Optional[Any]												=  tokenizer('Do you support jax jitted function?'					,return_tensors=TensorType.JAX					)

													@jax.jit
													def eval(**lowerCamelCase__   :							Union[str, Any]					):
																			return model(**lowerCamelCase__					)

													eval(**lowerCamelCase__					).block_until_ready()





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'bert-base is not a local folder and is not a valid model identifier'					):
													_UpperCamelCase   :						int												=  FlaxAutoModel.from_pretrained('bert-base'					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained(lowerCamelCase__					,revision='aaaaaa'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(
							    lowerCamelCase__					,'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'					,):
													_UpperCamelCase   :						List[Any]												=  FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							with self.assertRaisesRegex(lowerCamelCase__					,'Use `from_pt=True` to load this model'					):
													_UpperCamelCase   :						Tuple												=  FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'					)


