



"""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_			   =					logging.getLogger(__name__)
a_			   =					list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a_			   =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class    __lowercase     :







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self       ):
											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"""







						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
						_A		:							Optional[str]							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """The input training data file (a text file)."""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}					,    )
						_A		:							bool							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
						_A		:							Optional[int]							       =		field(
						    default=5					,    metadata={
						        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
						    }					,    )
						_A		:							Optional[int]							       =		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."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The number of processes to use for the preprocessing."""}					,    )
						_A		:							float							       =		field(
						    default=0.15					,    metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
						_A		:							bool							       =		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 						__UpperCamelCase							(self       ):
											if self.train_file is not None:
																snake_case_					:		Optional[int] 					=					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_					:		List[str] 					=					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__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					with open(SCREAMING_SNAKE_CASE__				,    """r"""				,    encoding="""utf-8"""			) as f:
										snake_case_					:		Dict 					=					[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__			)

					snake_case_					:		List[Any] 					=					{c: dataset[c] for c in dataset.column_names}
					snake_case_					:		Union[str, Any] 					=					refs
					return Dataset.from_dict(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





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

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

					snake_case_					:		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:
										snake_case_					:		Dict 					=					AutoTokenizer.from_pretrained(model_args.tokenizer_name				,    **SCREAMING_SNAKE_CASE__			)
					elif model_args.model_name_or_path:
										snake_case_					:		Optional[Any] 					=					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:
										snake_case_					:		List[str] 					=					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"""			)
										snake_case_					:		Optional[Any] 					=					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:
										snake_case_					:		List[Any] 					=					datasets["""train"""].column_names
					else:
										snake_case_					:		Tuple 					=					datasets["""validation"""].column_names
					snake_case_					:		Union[str, Any] 					=					"""text""" if """text""" in column_names else column_names[0]

					snake_case_					:		Optional[Any] 					=					"""max_length""" if data_args.pad_to_max_length else False

					def tokenize_function(SCREAMING_SNAKE_CASE__ :  Any			):
										# Remove empty lines
										snake_case_					:		List[Any] 					=					[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			)

					snake_case_					:		Any 					=					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:
										snake_case_					:		List[Any] 					=					add_chinese_references(tokenized_datasets["""train"""]				,    data_args.train_ref_file			)
					if data_args.validation_ref_file is not None:
										snake_case_					:		Any 					=					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_					:		str 					=					data_args.train_ref_file or data_args.validation_ref_file
					if has_ref:
										snake_case_					:		List[str] 					=					False

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

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

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

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

										snake_case_					:		Optional[Any] 					=					math.exp(eval_output["""eval_loss"""]			)
										snake_case_					:		List[str] 					=					perplexity

										snake_case_					:		List[Any] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					main()


if __name__ == "__main__":
			main()









"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  float				,    SCREAMING_SNAKE_CASE__ :  bool = False			):



					"""simple docstring"""





					snake_case_					:		dict 					=					{i: [] for i in range(SCREAMING_SNAKE_CASE__			)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE__			)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(i + 1				,    SCREAMING_SNAKE_CASE__			):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE__			)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE__			)
					return graph







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE__			) if i != j] for i in range(SCREAMING_SNAKE_CASE__			)
					}


if __name__ == "__main__":
			import doctest

			doctest.testmod()




