





"""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


__magic_name__       :					Tuple						=			logging.getLogger(__name__)
__magic_name__       :					Any						=			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__magic_name__       :					Optional[Any]						=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 							lowercase__			:

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







			def   _a      (      self    ):






									'''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 							lowercase__			:

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






									'''simple docstring'''







									if self.train_file is not None:
															UpperCamelCase				:		Tuple				   =			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:
															UpperCamelCase				:		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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						with open(SCREAMING_SNAKE_CASE							,     """r"""							,     encoding="""utf-8"""     ) as f:
												UpperCamelCase				:		Optional[int]				   =			[json.loads(SCREAMING_SNAKE_CASE     ) for line in f.read().splitlines() if (len(SCREAMING_SNAKE_CASE     ) > 0 and not line.isspace())]
						assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			{c: dataset[c] for c in dataset.column_names}
						UpperCamelCase				:		Optional[int]				   =			refs
						return Dataset.from_dict(SCREAMING_SNAKE_CASE     )

def      UpperCamelCase							():
						# 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.

						UpperCamelCase				:		int				   =			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.
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
						else:
												UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Tuple				   =			parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						UpperCamelCase				:		Any				   =			None
						if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
												UpperCamelCase				:		Any				   =			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"""							,     SCREAMING_SNAKE_CASE     )

						# 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.
												UpperCamelCase				:		Any				   =			load_dataset(data_args.dataset_name							,     data_args.dataset_config_name     )
												if "validation" not in datasets.keys():
																		UpperCamelCase				:		Optional[Any]				   =			load_dataset(
																		    data_args.dataset_name							,     data_args.dataset_config_name							,     split=f"""train[:{data_args.validation_split_percentage}%]"""							,     )
																		UpperCamelCase				:		List[Any]				   =			load_dataset(
																		    data_args.dataset_name							,     data_args.dataset_config_name							,     split=f"""train[{data_args.validation_split_percentage}%:]"""							,     )
						else:
												UpperCamelCase				:		int				   =			{}
												if data_args.train_file is not None:
																		UpperCamelCase				:		Optional[Any]				   =			data_args.train_file
												if data_args.validation_file is not None:
																		UpperCamelCase				:		Union[str, Any]				   =			data_args.validation_file
												UpperCamelCase				:		Union[str, Any]				   =			data_args.train_file.split("""."""     )[-1]
												if extension == "txt":
																		UpperCamelCase				:		str				   =			"""text"""
												UpperCamelCase				:		Union[str, Any]				   =			load_dataset(SCREAMING_SNAKE_CASE							,     data_files=SCREAMING_SNAKE_CASE     )
						# 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.
						UpperCamelCase				:		Optional[int]				   =			{
						    """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:
												UpperCamelCase				:		Any				   =			AutoConfig.from_pretrained(model_args.config_name							,     **SCREAMING_SNAKE_CASE     )
						elif model_args.model_name_or_path:
												UpperCamelCase				:		str				   =			AutoConfig.from_pretrained(model_args.model_name_or_path							,     **SCREAMING_SNAKE_CASE     )
						else:
												UpperCamelCase				:		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}"""     )

						UpperCamelCase				:		Optional[Any]				   =			{
						    """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:
												UpperCamelCase				:		Optional[Any]				   =			AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **SCREAMING_SNAKE_CASE     )
						elif model_args.model_name_or_path:
												UpperCamelCase				:		List[str]				   =			AutoTokenizer.from_pretrained(model_args.model_name_or_path							,     **SCREAMING_SNAKE_CASE     )
						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:
												UpperCamelCase				:		List[Any]				   =			AutoModelForMaskedLM.from_pretrained(
												    model_args.model_name_or_path							,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path     )							,     config=SCREAMING_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							,     )
						else:
												logger.info("""Training new model from scratch"""     )
												UpperCamelCase				:		Dict				   =			AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE     )

						model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE     )     )

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

						UpperCamelCase				:		List[str]				   =			"""max_length""" if data_args.pad_to_max_length else False

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

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

						# Data collator
						# This one will take care of randomly masking the tokens.
						UpperCamelCase				:		List[str]				   =			DataCollatorForWholeWordMask(tokenizer=SCREAMING_SNAKE_CASE							,     mlm_probability=data_args.mlm_probability     )

						# Initialize our Trainer
						UpperCamelCase				:		int				   =			Trainer(
						    model=SCREAMING_SNAKE_CASE							,     args=SCREAMING_SNAKE_CASE							,     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=SCREAMING_SNAKE_CASE							,     data_collator=SCREAMING_SNAKE_CASE							,     )

						# Training
						if training_args.do_train:
												if last_checkpoint is not None:
																		UpperCamelCase				:		Tuple				   =			last_checkpoint
												elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path     ):
																		UpperCamelCase				:		Optional[int]				   =			model_args.model_name_or_path
												else:
																		UpperCamelCase				:		Tuple				   =			None
												UpperCamelCase				:		Optional[Any]				   =			trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE     )
												trainer.save_model()  # Saves the tokenizer too for easy upload

												UpperCamelCase				:		Optional[Any]				   =			os.path.join(training_args.output_dir							,     """train_results.txt"""     )
												if trainer.is_world_process_zero():
																		with open(SCREAMING_SNAKE_CASE							,     """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
						UpperCamelCase				:		Any				   =			{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""     )

												UpperCamelCase				:		List[str]				   =			trainer.evaluate()

												UpperCamelCase				:		Union[str, Any]				   =			math.exp(eval_output["""eval_loss"""]     )
												UpperCamelCase				:		List[Any]				   =			perplexity

												UpperCamelCase				:		int				   =			os.path.join(training_args.output_dir							,     """eval_results_mlm_wwm.txt"""     )
												if trainer.is_world_process_zero():
																		with open(SCREAMING_SNAKE_CASE							,     """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      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
		main()










"""simple docstring"""


# Copyright 2023 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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__magic_name__       :					Tuple						=			{"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					Tuple						=			[
		    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MraForMaskedLM""",
		    """MraForMultipleChoice""",
		    """MraForQuestionAnswering""",
		    """MraForSequenceClassification""",
		    """MraForTokenClassification""",
		    """MraLayer""",
		    """MraModel""",
		    """MraPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mra import (
				    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MraForMaskedLM,
				    MraForMultipleChoice,
				    MraForQuestionAnswering,
				    MraForSequenceClassification,
				    MraForTokenClassification,
				    MraLayer,
				    MraModel,
				    MraPreTrainedModel,
				)
else:
		import sys

		__magic_name__       :					Any						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



