



"""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


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




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

 def 						_snake_case					(     self		:       str				):
     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       :
 _lowerCAmelCase   :			Optional[str]							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""})
 _lowerCAmelCase   :			Optional[str]							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
 _lowerCAmelCase   :			Optional[str]							=		field(default=lowerCAmelCase__    ,     metadata={"""help""": """The input training data file (a text file)."""})
 _lowerCAmelCase   :			Optional[str]							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}    ,     )
 _lowerCAmelCase   :			Optional[str]							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}    ,     )
 _lowerCAmelCase   :			Optional[str]							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}    ,     )
 _lowerCAmelCase   :			bool							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""})
 _lowerCAmelCase   :			Optional[int]							=		field(
     default=5    ,     metadata={
         """help""": """The percentage of the train set used as validation set in case there's no validation split"""
     }    ,     )
 _lowerCAmelCase   :			Optional[int]							=		field(
     default=lowerCAmelCase__    ,     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."""
         )
     }    ,     )
 _lowerCAmelCase   :			Optional[int]							=		field(
     default=lowerCAmelCase__    ,     metadata={"""help""": """The number of processes to use for the preprocessing."""}    ,     )
 _lowerCAmelCase   :			float							=		field(
     default=0.15    ,     metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""})
 _lowerCAmelCase   :			bool							=		field(
     default=lowerCAmelCase__    ,     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 						_snake_case					(     self		:       Tuple				):
     if self.train_file is not None:
         snake_case_ :     Any				=	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_ :     int				=	self.validation_file.split('''.'''				)[-1]
         assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def __lowercase			(							_a		,			_a    ):
    with open(_a		,			'''r'''		,			encoding='''utf-8'''    ) as f:
        snake_case_ :     Union[str, Any]				=	[json.loads(_a    ) for line in f.read().splitlines() if (len(_a    ) > 0 and not line.isspace())]
    assert len(_a    ) == len(_a    )

    snake_case_ :     Dict				=	{c: dataset[c] for c in dataset.column_names}
    snake_case_ :     List[str]				=	refs
    return Dataset.from_dict(_a    )




def __lowercase			(							):
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

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

    # Detecting last checkpoint.
    snake_case_ :     Optional[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'''		,			_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.
        snake_case_ :     Optional[Any]				=	load_dataset(data_args.dataset_name		,			data_args.dataset_config_name    )
        if "validation" not in datasets.keys():
            snake_case_ :     int				=	load_dataset(
                data_args.dataset_name		,			data_args.dataset_config_name		,			split=f"train[:{data_args.validation_split_percentage}%]"		,			)
            snake_case_ :     List[Any]				=	load_dataset(
                data_args.dataset_name		,			data_args.dataset_config_name		,			split=f"train[{data_args.validation_split_percentage}%:]"		,			)
    else:
        snake_case_ :     Any				=	{}
        if data_args.train_file is not None:
            snake_case_ :     List[Any]				=	data_args.train_file
        if data_args.validation_file is not None:
            snake_case_ :     Optional[Any]				=	data_args.validation_file
        snake_case_ :     Optional[int]				=	data_args.train_file.split('''.'''    )[-1]
        if extension == "txt":
            snake_case_ :     Tuple				=	'''text'''
        snake_case_ :     Any				=	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.
    snake_case_ :     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:
        snake_case_ :     Tuple				=	AutoConfig.from_pretrained(model_args.config_name		,			**_a    )
    elif model_args.model_name_or_path:
        snake_case_ :     int				=	AutoConfig.from_pretrained(model_args.model_name_or_path		,			**_a    )
    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_ :     List[str]				=	{
        '''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_ :     List[Any]				=	AutoTokenizer.from_pretrained(model_args.tokenizer_name		,			**_a    )
    elif model_args.model_name_or_path:
        snake_case_ :     str				=	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:
        snake_case_ :     Optional[int]				=	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'''    )
        snake_case_ :     str				=	AutoModelForMaskedLM.from_config(_a    )

    model.resize_token_embeddings(len(_a    )    )

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

    snake_case_ :     int				=	'''max_length''' if data_args.pad_to_max_length else False

    def tokenize_function(_a    ):
        # Remove empty lines
        snake_case_ :     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    )

    snake_case_ :     List[Any]				=	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:
        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_ :     Optional[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_ :     List[str]				=	data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        snake_case_ :     List[Any]				=	False

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

    # Initialize our Trainer
    snake_case_ :     int				=	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:
            snake_case_ :     Dict				=	last_checkpoint
        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    ):
            snake_case_ :     Optional[int]				=	model_args.model_name_or_path
        else:
            snake_case_ :     str				=	None
        snake_case_ :     Union[str, Any]				=	trainer.train(resume_from_checkpoint=_a    )
        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(_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
    snake_case_ :     Any				=	{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''    )

        snake_case_ :     Dict				=	trainer.evaluate()

        snake_case_ :     int				=	math.exp(eval_output['''eval_loss''']    )
        snake_case_ :     Any				=	perplexity

        snake_case_ :     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 __lowercase			(							_a    ):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()










"""simple docstring"""

from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def __lowercase			(							_a		,			_a		,			_a = "x"		,			_a = 10**-10		,			_a = 1		,			):
    snake_case_ :     Any				=	symbols(_a    )
    snake_case_ :     int				=	lambdify(_a		,			_a    )
    snake_case_ :     Optional[Any]				=	lambdify(_a		,			diff(_a		,			_a    )    )

    snake_case_ :     Optional[Any]				=	starting_point

    while True:
        if diff_function(_a    ) != 0:
            snake_case_ :     Optional[int]				=	prev_guess - multiplicity * func(_a    ) / diff_function(
                _a    )
        else:
            raise ZeroDivisionError('''Could not find root'''    ) from None

        # Precision is checked by comparing the difference of consecutive guesses
        if abs(next_guess - prev_guess    ) < precision:
            return next_guess

        snake_case_ :     int				=	next_guess


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')

     # Find root of polynomial
     # Find fourth Root of 5
     print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')

     # Find value of e
     print(
         '''The root of log(y) - 1 = 0 is ''',
         f'{newton_raphson("log(y) - 1", 2, variable="y")}',
     )

     # Exponential Roots
     print(
         '''The root of exp(x) - 1 = 0 is''',
         f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
     )

     # Find root of cos(x)
     print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')





