



'''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')