



'''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


lowerCAmelCase	:		List[str]				=   logging.getLogger(__name__)
lowerCAmelCase	:		str				=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase	:		int				=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 					UpperCAmelCase__	:
					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     )     ->			Tuple:
						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__	:
					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     )     ->			Dict:
						if self.train_file is not None:
							__lowerCAmelCase												=  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												=  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					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						str					):






	'''simple docstring'''




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

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



def   __lowerCAmelCase					(							):






	'''simple docstring'''




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

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

	# 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												=  load_dataset(data_args.dataset_name				,		data_args.dataset_config_name					)
		if "validation" not in datasets.keys():
			__lowerCAmelCase												=  load_dataset(
			    data_args.dataset_name				,		data_args.dataset_config_name				,		split=f'''train[:{data_args.validation_split_percentage}%]'''				,		)
			__lowerCAmelCase												=  load_dataset(
			    data_args.dataset_name				,		data_args.dataset_config_name				,		split=f'''train[{data_args.validation_split_percentage}%:]'''				,		)
	else:
		__lowerCAmelCase												=  {}
		if data_args.train_file is not None:
			__lowerCAmelCase												=  data_args.train_file
		if data_args.validation_file is not None:
			__lowerCAmelCase												=  data_args.validation_file
		__lowerCAmelCase												=  data_args.train_file.split("."					)[-1]
		if extension == "txt":
			__lowerCAmelCase												=  "text"
		__lowerCAmelCase												=  load_dataset(lowerCamelCase				,		data_files=lowerCamelCase					)
	# 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												=  {
	    "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												=  AutoConfig.from_pretrained(model_args.config_name				,		**lowerCamelCase					)
	elif model_args.model_name_or_path:
		__lowerCAmelCase												=  AutoConfig.from_pretrained(model_args.model_name_or_path				,		**lowerCamelCase					)
	else:
		__lowerCAmelCase												=  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												=  {
	    "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												=  AutoTokenizer.from_pretrained(model_args.tokenizer_name				,		**lowerCamelCase					)
	elif model_args.model_name_or_path:
		__lowerCAmelCase												=  AutoTokenizer.from_pretrained(model_args.model_name_or_path				,		**lowerCamelCase					)
	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												=  AutoModelForMaskedLM.from_pretrained(
		    model_args.model_name_or_path				,		from_tf=bool(".ckpt" in model_args.model_name_or_path					)				,		config=lowerCamelCase				,		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												=  AutoModelForMaskedLM.from_config(lowerCamelCase					)

	model.resize_token_embeddings(len(lowerCamelCase					)					)

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

	__lowerCAmelCase												=  "max_length" if data_args.pad_to_max_length else False

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

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

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

	# Initialize our Trainer
	__lowerCAmelCase												=  Trainer(
	    model=lowerCamelCase				,		args=lowerCamelCase				,		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=lowerCamelCase				,		data_collator=lowerCamelCase				,		)

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

		__lowerCAmelCase												=  os.path.join(training_args.output_dir				,		"train_results.txt"					)
		if trainer.is_world_process_zero():
			with open(lowerCamelCase				,		"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												=  {}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"					)

		__lowerCAmelCase												=  trainer.evaluate()

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

		__lowerCAmelCase												=  os.path.join(training_args.output_dir				,		"eval_results_mlm_wwm.txt"					)
		if trainer.is_world_process_zero():
			with open(lowerCamelCase				,		"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					(							lowerCamelCase   :						List[Any]					):






	'''simple docstring'''




	main()


if __name__ == "__main__":
	main()




'''simple docstring'''



import requests
from bsa import BeautifulSoup



def   __lowerCAmelCase					(							lowerCamelCase   :						str = "https://www.worldometers.info/coronavirus"					):






	'''simple docstring'''




	__lowerCAmelCase												=  BeautifulSoup(requests.get(lowerCamelCase					).text				,		"html.parser"					)
	__lowerCAmelCase												=  soup.findAll("h1"					)
	__lowerCAmelCase												=  soup.findAll("div"				,		{"class": "maincounter-number"}					)
	keys += soup.findAll("span"				,		{"class": "panel-title"}					)
	values += soup.findAll("div"				,		{"class": "number-table-main"}					)
	return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase				,		lowerCamelCase					)}


if __name__ == "__main__":
	print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
	for key, value in world_covidaa_stats().items():
		print(f'{key}\n{value}\n')