"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
					raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





						"""simple docstring"""






						return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			parser.add_parser('train'		,    help='CLI tool to train a model on a task.'    )

										train_parser.add_argument(
										    '--train_data'		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
										train_parser.add_argument(
										    '--column_label'		,    type=lowerCAmelCase		,    default=0		,    help='Column of the dataset csv file with example labels.'    )
										train_parser.add_argument(
										    '--column_text'		,    type=lowerCAmelCase		,    default=1		,    help='Column of the dataset csv file with example texts.'    )
										train_parser.add_argument(
										    '--column_id'		,    type=lowerCAmelCase		,    default=2		,    help='Column of the dataset csv file with example ids.'    )
										train_parser.add_argument(
										    '--skip_first_row'		,    action='store_true'		,    help='Skip the first row of the csv file (headers).'    )

										train_parser.add_argument('--validation_data'		,    type=lowerCAmelCase		,    default=''		,    help='path to validation dataset.'    )
										train_parser.add_argument(
										    '--validation_split'		,    type=lowerCAmelCase		,    default=0.1		,    help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'		,    )

										train_parser.add_argument('--output'		,    type=lowerCAmelCase		,    default='./'		,    help='path to saved the trained model.'    )

										train_parser.add_argument(
										    '--task'		,    type=lowerCAmelCase		,    default='text_classification'		,    help='Task to train the model on.'    )
										train_parser.add_argument(
										    '--model'		,    type=lowerCAmelCase		,    default='bert-base-uncased'		,    help='Model\'s name or path to stored model.'    )
										train_parser.add_argument('--train_batch_size'		,    type=lowerCAmelCase		,    default=32		,    help='Batch size for training.'    )
										train_parser.add_argument('--valid_batch_size'		,    type=lowerCAmelCase		,    default=64		,    help='Batch size for validation.'    )
										train_parser.add_argument('--learning_rate'		,    type=lowerCAmelCase		,    default=3E-5		,    help='Learning rate.'    )
										train_parser.add_argument('--adam_epsilon'		,    type=lowerCAmelCase		,    default=1E-08		,    help='Epsilon for Adam optimizer.'    )
										train_parser.set_defaults(func=lowerCAmelCase    )

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			logging.get_logger('transformers-cli/training'    )

										snake_case								=			'tf' if is_tf_available() else 'torch'

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
										snake_case								=			args.output

										snake_case								=			args.column_label
										snake_case								=			args.column_text
										snake_case								=			args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
										if args.task == "text_classification":
																snake_case								=			TextClassificationPipeline.from_pretrained(args.model    )
										elif args.task == "token_classification":
																raise NotImplementedError
										elif args.task == "question_answering":
																raise NotImplementedError

										self.logger.info(F"""Loading dataset from {args.train_data}"""    )
										snake_case								=			Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										snake_case								=			None
										if args.validation_data:
																self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
																snake_case								=			Processor.create_from_csv(
																    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										snake_case								=			args.validation_split
										snake_case								=			args.train_batch_size
										snake_case								=			args.valid_batch_size
										snake_case								=			args.learning_rate
										snake_case								=			args.adam_epsilon

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.framework == "tf":
																return self.run_tf()
										return self.run_torch()

				def 		snake_case (				self    ):






										"""simple docstring"""

										raise NotImplementedError

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output    )



"""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


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





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				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    lowerCAmelCase__    (			_UpperCamelCase       :				List[str] ,			_UpperCamelCase       :				Optional[Any]					)					->				int:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				Dict					)					->				Tuple:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			)					->				Optional[int]:





						"""simple docstring"""






						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=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.'					)
						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					)

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

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

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

						snake_case								=			LmSeqsDataset(params=_UpperCamelCase ,			data=_UpperCamelCase					)
						logger.info('Data loader created.'					)

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

						if args.student_pretrained_weights is not None:
												logger.info(f"""Loading pretrained weights from {args.student_pretrained_weights}"""					)
												snake_case								=			student_model_class.from_pretrained(args.student_pretrained_weights ,			config=_UpperCamelCase					)
						else:
												snake_case								=			student_model_class(_UpperCamelCase					)

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

						# TEACHER #
						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()
						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()


