






'''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	:			Dict         =logging.getLogger(__name__)
_A	:			List[str]         =list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_A	:			Optional[Any]         =tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

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




							def 		lowerCamelCase_				(							self:				List[Any]     ):






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






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





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Optional[Any]:





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

							lowerCamelCase__					:	str         =       {c: dataset[c] for c in dataset.column_names}
							lowerCamelCase__					:	int         =       refs
							return Dataset.from_dict(A_      )





def  SCREAMING_SNAKE_CASE_       ()      ->       Optional[Any]:
							lowerCamelCase__					:	str         =       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.
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	Union[str, Any]         =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
							else:
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	List[Any]         =       parser.parse_args_into_dataclasses()

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

							lowerCamelCase__					:	Union[str, 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:
														lowerCamelCase__					:	str         =       AutoTokenizer.from_pretrained(model_args.tokenizer_name				,			**A_      )
							elif model_args.model_name_or_path:
														lowerCamelCase__					:	Union[str, 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:
														lowerCamelCase__					:	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"""      )
														lowerCamelCase__					:	Union[str, Any]         =       AutoModelForMaskedLM.from_config(A_      )

							model.resize_token_embeddings(len(A_      )      )

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

							lowerCamelCase__					:	List[Any]         =       """max_length""" if data_args.pad_to_max_length else False

							def tokenize_function(UpperCamelCase      ):
														# Remove empty lines
														lowerCamelCase__					:	Optional[Any]         =       [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      )

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

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

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

														lowerCamelCase__					:	Tuple         =       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
							lowerCamelCase__					:	Any         =       {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""      )

														lowerCamelCase__					:	List[Any]         =       trainer.evaluate()

														lowerCamelCase__					:	str         =       math.exp(eval_output["""eval_loss"""]      )
														lowerCamelCase__					:	Dict         =       perplexity

														lowerCamelCase__					:	Tuple         =       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  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       str:
							main()


if __name__ == "__main__":
			main()



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase			:     int           =   subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase			:     Any           =   (
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
)

__lowerCAmelCase			:     str           =   '|'.join(sys.argv[1:])
__lowerCAmelCase			:     Tuple           =   re.compile(RF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase			:     Union[str, Any]           =   [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')

