




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


lowercase__				:     int 		=						logging.getLogger(__name__)
lowercase__				:     List[str] 		=						list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowercase__				:     Union[str, Any] 		=						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={
						        '''help''': (
						            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
						        )
						    }       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCAmelCase__			)}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={
						        '''help''': (
						            '''Override some existing default config settings when a model is trained from scratch. Example: '''
						            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
						        )
						    }       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default='''main'''       ,   metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={
						        '''help''': (
						            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
						            '''with private models).'''
						        )
						    }       ,   )






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
								raise ValueError(
								    "--config_overrides can't be used in combination with --config_name or --model_name_or_path"						)







@dataclass
class 		UpperCAmelCase   :







						'''simple docstring'''





						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}			)
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
						lowerCAmelCase_     		=   field(default=UpperCAmelCase__       ,   metadata={'''help''': '''The input training data file (a text file).'''}			)
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)
						lowerCAmelCase_     		=   field(
						    default=5       ,   metadata={
						        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
						    }       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={
						        '''help''': (
						            '''The maximum total input sequence length after tokenization. Sequences longer '''
						            '''than this will be truncated. Default to the max input length of the model.'''
						        )
						    }       ,   )
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,   )
						lowerCAmelCase_     		=   field(
						    default=0.15       ,   metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}			)
						lowerCAmelCase_     		=   field(
						    default=UpperCAmelCase__       ,   metadata={
						        '''help''': (
						            '''Whether to pad all samples to `max_seq_length`. '''
						            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
						        )
						    }       ,   )






						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							if self.train_file is not None:
								snake_case_		    =     self.train_file.split("."						)[-1]
								assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
							if self.validation_file is not None:
								snake_case_		    =     self.validation_file.split("."						)[-1]
								assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	with open(_A     ,	"r"     ,	encoding="utf-8"							) as f:
		snake_case_		    =     [json.loads(_A							) for line in f.read().splitlines() if (len(_A							) > 0 and not line.isspace())]
	assert len(_A							) == len(_A							)

	snake_case_		    =     {c: dataset[c] for c in dataset.column_names}
	snake_case_		    =     refs
	return Dataset.from_dict(_A							)


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
	if len(sys.argv							) == 2 and sys.argv[1].endswith(".json"							):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		snake_case_    ,      snake_case_    ,      snake_case_		    =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
	else:
		snake_case_    ,      snake_case_    ,      snake_case_		    =     parser.parse_args_into_dataclasses()

	# Detecting last checkpoint.
	snake_case_		    =     None
	if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
		snake_case_		    =     get_last_checkpoint(training_args.output_dir							)
		if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
			raise ValueError(
			    f"Output directory ({training_args.output_dir}) already exists and is not empty. "
			    "Use --overwrite_output_dir to overcome."							)
		elif last_checkpoint is not None:
			logger.info(
			    f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
			    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."							)

    # Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,	datefmt="%m/%d/%Y %H:%M:%S"     ,	handlers=[logging.StreamHandler(sys.stdout							)]     ,	)
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

	# Log on each process the small summary:
	logger.warning(
	    f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
	    + f"distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"							)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank							):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"     ,	_A							)

	# Set seed before initializing model.
	set_seed(training_args.seed							)

	# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
	# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
	# (the dataset will be downloaded automatically from the datasets Hub).
	#
	# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
	# 'text' is found. You can easily tweak this behavior (see below).
	#
	# In distributed training, the load_dataset function guarantee that only one local process can concurrently
	# download the dataset.
	if data_args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		snake_case_		    =     load_dataset(data_args.dataset_name     ,	data_args.dataset_config_name							)
		if "validation" not in datasets.keys():
			snake_case_		    =     load_dataset(
			    data_args.dataset_name     ,	data_args.dataset_config_name     ,	split=f"train[:{data_args.validation_split_percentage}%]"     ,	)
			snake_case_		    =     load_dataset(
			    data_args.dataset_name     ,	data_args.dataset_config_name     ,	split=f"train[{data_args.validation_split_percentage}%:]"     ,	)
	else:
		snake_case_		    =     {}
		if data_args.train_file is not None:
			snake_case_		    =     data_args.train_file
		if data_args.validation_file is not None:
			snake_case_		    =     data_args.validation_file
		snake_case_		    =     data_args.train_file.split("."							)[-1]
		if extension == "txt":
			snake_case_		    =     "text"
		snake_case_		    =     load_dataset(_A     ,	data_files=_A							)
	# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
	# https://huggingface.co/docs/datasets/loading_datasets.html.

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	snake_case_		    =     {
	    "cache_dir": model_args.cache_dir,
	    "revision": model_args.model_revision,
	    "use_auth_token": True if model_args.use_auth_token else None,
	}
	if model_args.config_name:
		snake_case_		    =     AutoConfig.from_pretrained(model_args.config_name     ,	**_A							)
	elif model_args.model_name_or_path:
		snake_case_		    =     AutoConfig.from_pretrained(model_args.model_name_or_path     ,	**_A							)
	else:
		snake_case_		    =     CONFIG_MAPPING[model_args.model_type]()
		logger.warning("You are instantiating a new config instance from scratch."							)
		if model_args.config_overrides is not None:
			logger.info(f"Overriding config: {model_args.config_overrides}"							)
			config.update_from_string(model_args.config_overrides							)
			logger.info(f"New config: {config}"							)

	snake_case_		    =     {
	    "cache_dir": model_args.cache_dir,
	    "use_fast": model_args.use_fast_tokenizer,
	    "revision": model_args.model_revision,
	    "use_auth_token": True if model_args.use_auth_token else None,
	}
	if model_args.tokenizer_name:
		snake_case_		    =     AutoTokenizer.from_pretrained(model_args.tokenizer_name     ,	**_A							)
	elif model_args.model_name_or_path:
		snake_case_		    =     AutoTokenizer.from_pretrained(model_args.model_name_or_path     ,	**_A							)
	else:
		raise ValueError(
		    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
		    "You can do it from another script, save it, and load it from here, using --tokenizer_name."							)

	if model_args.model_name_or_path:
		snake_case_		    =     AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path     ,	from_tf=bool(".ckpt" in model_args.model_name_or_path							)     ,	config=_A     ,	cache_dir=model_args.cache_dir     ,	revision=model_args.model_revision     ,	use_auth_token=True if model_args.use_auth_token else None     ,	)
	else:
		logger.info("Training new model from scratch"							)
		snake_case_		    =     AutoModelForMaskedLM.from_config(_A							)

	model.resize_token_embeddings(len(_A							)							)

	# Preprocessing the datasets.
	# First we tokenize all the texts.
	if training_args.do_train:
		snake_case_		    =     datasets["train"].column_names
	else:
		snake_case_		    =     datasets["validation"].column_names
	snake_case_		    =     "text" if "text" in column_names else column_names[0]

	snake_case_		    =     "max_length" if data_args.pad_to_max_length else False

	def tokenize_function(_A							):
		# Remove empty lines
		snake_case_		    =     [line for line in examples["text"] if len(_A							) > 0 and not line.isspace()]
		return tokenizer(examples["text"]     ,	padding=_A     ,	truncation=_A     ,	max_length=data_args.max_seq_length							)

	snake_case_		    =     datasets.map(
	    _A     ,	batched=_A     ,	num_proc=data_args.preprocessing_num_workers     ,	remove_columns=[text_column_name]     ,	load_from_cache_file=not data_args.overwrite_cache     ,	)

	# Add the chinese references if provided
	if data_args.train_ref_file is not None:
		snake_case_		    =     add_chinese_references(tokenized_datasets["train"]     ,	data_args.train_ref_file							)
	if data_args.validation_ref_file is not None:
		snake_case_		    =     add_chinese_references(
		    tokenized_datasets["validation"]     ,	data_args.validation_ref_file							)
	# If we have ref files, need to avoid it removed by trainer
	snake_case_		    =     data_args.train_ref_file or data_args.validation_ref_file
	if has_ref:
		snake_case_		    =     False

	# Data collator
	# This one will take care of randomly masking the tokens.
	snake_case_		    =     DataCollatorForWholeWordMask(tokenizer=_A     ,	mlm_probability=data_args.mlm_probability							)

	# Initialize our Trainer
	snake_case_		    =     Trainer(
	    model=_A     ,	args=_A     ,	train_dataset=tokenized_datasets["train"] if training_args.do_train else None     ,	eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None     ,	tokenizer=_A     ,	data_collator=_A     ,	)

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			snake_case_		    =     last_checkpoint
		elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path							):
			snake_case_		    =     model_args.model_name_or_path
		else:
			snake_case_		    =     None
		snake_case_		    =     trainer.train(resume_from_checkpoint=_A							)
		trainer.save_model()  # Saves the tokenizer too for easy upload

		snake_case_		    =     os.path.join(training_args.output_dir     ,	"train_results.txt"							)
		if trainer.is_world_process_zero():
			with open(_A     ,	"w"							) as writer:
				logger.info("***** Train results *****"							)
				for key, value in sorted(train_result.metrics.items()							):
					logger.info(f"  {key} = {value}"							)
					writer.write(f"{key} = {value}\n"							)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
			trainer.state.save_to_json(os.path.join(training_args.output_dir     ,	"trainer_state.json"							)							)

    # Evaluation
	snake_case_		    =     {}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"							)

		snake_case_		    =     trainer.evaluate()

		snake_case_		    =     math.exp(eval_output["eval_loss"]							)
		snake_case_		    =     perplexity

		snake_case_		    =     os.path.join(training_args.output_dir     ,	"eval_results_mlm_wwm.txt"							)
		if trainer.is_world_process_zero():
			with open(_A     ,	"w"							) as writer:
				logger.info("***** Eval results *****"							)
				for key, value in sorted(results.items()							):
					logger.info(f"  {key} = {value}"							)
					writer.write(f"{key} = {value}\n"							)

	return results


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	main()


if __name__ == "__main__":
		main()






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     ArgumentParser("Accelerate CLI tool"     ,	usage="accelerate <command> [<args>]"     ,	allow_abbrev=_A							)
	snake_case_		    =     parser.add_subparsers(help="accelerate command helpers"							)

	# Register commands
	get_config_parser(subparsers=_A							)
	env_command_parser(subparsers=_A							)
	launch_command_parser(subparsers=_A							)
	tpu_command_parser(subparsers=_A							)
	test_command_parser(subparsers=_A							)

	# Let's go
	snake_case_		    =     parser.parse_args()

	if not hasattr(_A     ,	"func"							):
		parser.print_help()
		exit(1							)

	# Run
	args.func(_A							)


if __name__ == "__main__":
		main()
