



"""simple docstring"""

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


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


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={
	        '''help''': (
	            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
	        )
	    }							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_lowerCamelCase )}							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							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'''
	        )
	    }							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,							)
	_SCREAMING_SNAKE_CASE	:		bool  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}							,							)
	_SCREAMING_SNAKE_CASE	:		str  						= field(
	    default='''main'''							,							metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}							,							)
	_SCREAMING_SNAKE_CASE	:		bool  						= field(
	    default=_lowerCamelCase							,							metadata={
	        '''help''': (
	            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
	            '''with private models).'''
	        )
	    }							,							)





	def 			_lowerCAmelCase     (      self				:       int    )						->	List[Any]:





					"""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     lowerCAmelCase__						:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(default=_lowerCamelCase							,							metadata={'''help''': '''The input training data file (a text file).'''} )
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[str]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}							,							)
	_SCREAMING_SNAKE_CASE	:		bool  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= field(
	    default=5							,							metadata={
	        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
	    }							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= field(
	    default=_lowerCamelCase							,							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.'''
	        )
	    }							,							)
	_SCREAMING_SNAKE_CASE	:		Optional[int]  						= field(
	    default=_lowerCamelCase							,							metadata={'''help''': '''The number of processes to use for the preprocessing.'''}							,							)
	_SCREAMING_SNAKE_CASE	:		float  						= field(
	    default=0.15							,							metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
	_SCREAMING_SNAKE_CASE	:		bool  						= field(
	    default=_lowerCamelCase							,							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 			_lowerCAmelCase     (      self				:       str    )						->	List[Any]:





					"""simple docstring"""


					if self.train_file is not None:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     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:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.validation_file.split('.'    )[-1]
									assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."




def  __snake_case							(	__A      :						Optional[int]							,		__A      :						int     )     ->       Optional[Any]:






				'''simple docstring'''
				with open(__A							,		'r'							,		encoding='utf-8'     ) as f:
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     [json.loads(__A     ) for line in f.read().splitlines() if (len(__A     ) > 0 and not line.isspace())]
				assert len(__A     ) == len(__A     )

				SCREAMING_SNAKE_CASE						:							Any							=     {c: dataset[c] for c in dataset.column_names}
				SCREAMING_SNAKE_CASE						:							Dict							=     refs
				return Dataset.from_dict(__A     )
def  __snake_case							(	)     ->       Any:






				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				SCREAMING_SNAKE_CASE						:							Tuple							=     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.
								SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
				else:
								SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     parser.parse_args_into_dataclasses()

				# Detecting last checkpoint.
				SCREAMING_SNAKE_CASE						:							Any							=     None
				if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
								SCREAMING_SNAKE_CASE						:							Optional[int]							=     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.
								SCREAMING_SNAKE_CASE						:							List[Any]							=     load_dataset(data_args.dataset_name							,		data_args.dataset_config_name     )
								if "validation" not in datasets.keys():
												SCREAMING_SNAKE_CASE						:							str							=     load_dataset(
												    data_args.dataset_name							,		data_args.dataset_config_name							,		split=F"""train[:{data_args.validation_split_percentage}%]"""							,		)
												SCREAMING_SNAKE_CASE						:							Dict							=     load_dataset(
												    data_args.dataset_name							,		data_args.dataset_config_name							,		split=F"""train[{data_args.validation_split_percentage}%:]"""							,		)
				else:
								SCREAMING_SNAKE_CASE						:							Any							=     {}
								if data_args.train_file is not None:
												SCREAMING_SNAKE_CASE						:							Optional[Any]							=     data_args.train_file
								if data_args.validation_file is not None:
												SCREAMING_SNAKE_CASE						:							Dict							=     data_args.validation_file
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     data_args.train_file.split('.'     )[-1]
								if extension == "txt":
												SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     'text'
								SCREAMING_SNAKE_CASE						:							int							=     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.
				SCREAMING_SNAKE_CASE						:							Tuple							=     {
				    '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:
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     AutoConfig.from_pretrained(model_args.config_name							,		**__A     )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE						:							str							=     AutoConfig.from_pretrained(model_args.model_name_or_path							,		**__A     )
				else:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     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}"""     )

				SCREAMING_SNAKE_CASE						:							int							=     {
				    '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:
								SCREAMING_SNAKE_CASE						:							int							=     AutoTokenizer.from_pretrained(model_args.tokenizer_name							,		**__A     )
				elif model_args.model_name_or_path:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     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:
								SCREAMING_SNAKE_CASE						:							Tuple							=     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'     )
								SCREAMING_SNAKE_CASE						:							int							=     AutoModelForMaskedLM.from_config(__A     )

				model.resize_token_embeddings(len(__A     )     )

				# Preprocessing the datasets.
				# First we tokenize all the texts.
				if training_args.do_train:
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     datasets['train'].column_names
				else:
								SCREAMING_SNAKE_CASE						:							Any							=     datasets['validation'].column_names
				SCREAMING_SNAKE_CASE						:							Tuple							=     'text' if 'text' in column_names else column_names[0]

				SCREAMING_SNAKE_CASE						:							Tuple							=     'max_length' if data_args.pad_to_max_length else False

				def tokenize_function(__A      :						int     ):
								# Remove empty lines
								SCREAMING_SNAKE_CASE						:							str							=     [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     )

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     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:
								SCREAMING_SNAKE_CASE						:							Dict							=     add_chinese_references(tokenized_datasets['train']							,		data_args.train_ref_file     )
				if data_args.validation_ref_file is not None:
								SCREAMING_SNAKE_CASE						:							int							=     add_chinese_references(
								    tokenized_datasets['validation']							,		data_args.validation_ref_file     )
				# If we have ref files, need to avoid it removed by trainer
				SCREAMING_SNAKE_CASE						:							List[str]							=     data_args.train_ref_file or data_args.validation_ref_file
				if has_ref:
								SCREAMING_SNAKE_CASE						:							Any							=     False

				# Data collator
				# This one will take care of randomly masking the tokens.
				SCREAMING_SNAKE_CASE						:							Any							=     DataCollatorForWholeWordMask(tokenizer=__A							,		mlm_probability=data_args.mlm_probability     )

				# Initialize our Trainer
				SCREAMING_SNAKE_CASE						:							Any							=     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:
												SCREAMING_SNAKE_CASE						:							str							=     last_checkpoint
								elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     ):
												SCREAMING_SNAKE_CASE						:							int							=     model_args.model_name_or_path
								else:
												SCREAMING_SNAKE_CASE						:							List[Any]							=     None
								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     trainer.train(resume_from_checkpoint=__A     )
								trainer.save_model()  # Saves the tokenizer too for easy upload

								SCREAMING_SNAKE_CASE						:							Dict							=     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
				SCREAMING_SNAKE_CASE						:							Tuple							=     {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'     )

								SCREAMING_SNAKE_CASE						:							Dict							=     trainer.evaluate()

								SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     math.exp(eval_output['eval_loss']     )
								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     perplexity

								SCREAMING_SNAKE_CASE						:							Optional[Any]							=     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  __snake_case							(	__A      :						Any     )     ->       Tuple:






				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()










"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_						:    str				=      {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A_						:    Optional[int]				=      [
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A_						:    int				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





