




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     :		str								=       logging.getLogger(__name__)
_lowerCamelCase     :		Any								=       list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_lowerCamelCase     :		Dict								=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class     UpperCamelCase_   :

    '''simple docstring'''






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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Any:





           '''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     UpperCamelCase_   :

    '''simple docstring'''






    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}					)
    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
    UpperCAmelCase__							  =				field(default=__snake_case		, metadata={'''help''': '''The input training data file (a text file).'''}					)
    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}		, )
    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}		, )
    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}		, )
    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)
    UpperCAmelCase__							  =				field(
        default=5		, metadata={
            '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
        }		, )
    UpperCAmelCase__							  =				field(
        default=__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.'''
            )
        }		, )
    UpperCAmelCase__							  =				field(
        default=__snake_case		, metadata={'''help''': '''The number of processes to use for the preprocessing.'''}		, )
    UpperCAmelCase__							  =				field(
        default=0.1_5		, metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}					)
    UpperCAmelCase__							  =				field(
        default=__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 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->List[str]:





           '''simple docstring'''

           if self.train_file is not None:
                  A__							=   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:
                  A__							=   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    (	lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

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

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







def SCREAMING_SNAKE_CASE    (	)       ->	str:



       """simple docstring"""

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

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

       A__							=   {
           '''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:
              A__							=   AutoTokenizer.from_pretrained(model_args.tokenizer_name			,    **_snake_case						)
       elif model_args.model_name_or_path:
              A__							=   AutoTokenizer.from_pretrained(model_args.model_name_or_path			,    **_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:
              A__							=   AutoModelForMaskedLM.from_pretrained(
                  model_args.model_name_or_path			,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)			,    config=_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'''						)
              A__							=   AutoModelForMaskedLM.from_config(_snake_case						)

       model.resize_token_embeddings(len(_snake_case						)						)

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

       A__							=   '''max_length''' if data_args.pad_to_max_length else False

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

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

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

       # Initialize our Trainer
       A__							=   Trainer(
           model=_snake_case			,    args=_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=_snake_case			,    data_collator=_snake_case			,    )

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

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

              A__							=   trainer.evaluate()

              A__							=   math.exp(eval_output['''eval_loss''']						)
              A__							=   perplexity

              A__							=   os.path.join(training_args.output_dir			,    '''eval_results_mlm_wwm.txt'''						)
              if trainer.is_world_process_zero():
                     with open(_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    (	lowercase_						)       ->	str:



       """simple docstring"""

       main()


if __name__ == "__main__":
  main()










from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""						)       ->	dict[str, float]:



       """simple docstring"""

       A__							=   url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
       A__							=   BeautifulSoup(requests.get(lowercase_						).text			,    '''html.parser'''						)
       A__							=   soup.find_all('''td'''			,    attrs='''titleColumn'''						)
       A__							=   soup.find_all('''td'''			,    class_='''ratingColumn imdbRating'''						)
       return {
           title.a.text: float(rating.strong.text						)
           for title, rating in zip(lowercase_			,    lowercase_						)
       }







def SCREAMING_SNAKE_CASE    (	lowercase_ = "IMDb_Top_250_Movies.csv"						)       ->	None:



       """simple docstring"""

       A__							=   get_imdb_top_aaa_movies()
       with open(lowercase_			,    '''w'''			,    newline=''''''						) as out_file:
              A__							=   csv.writer(lowercase_						)
              writer.writerow(['''Movie title''', '''IMDb rating''']						)
              for title, rating in movies.items():
                     writer.writerow([title, rating]						)


if __name__ == "__main__":
  write_movies()


