




'''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


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.getLogger(__name__)
_SCREAMING_SNAKE_CASE							:       List[str]						=			list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			_snake_case		:
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						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=lowercase_						,						metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowercase_							)}						,						)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						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=lowercase_						,						metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,						)
			lowerCAmelCase_					:				bool							=   field(
			    default=lowercase_						,						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=lowercase_						,						metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }						,						)




			def        lowerCAmelCase__    ( self							)    ->    Optional[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 			_snake_case		:
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
			lowerCAmelCase_					:				Optional[str]							=   field(default=lowercase_						,						metadata={"help": "The input training data file (a text file)."}							)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						,						)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}						,						)
			lowerCAmelCase_					:				Optional[str]							=   field(
			    default=lowercase_						,						metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}						,						)
			lowerCAmelCase_					:				bool							=   field(
			    default=lowercase_						,						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=lowercase_						,						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=lowercase_						,						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=lowercase_						,						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        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										if self.train_file is not None:
																	snake_case_      =				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_      =				self.validation_file.split("."							)[-1]
																	assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."


def 					UpperCamelCase_(     snake_case							:	Any							,    snake_case							:	Dict					):

							'''simple docstring'''






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

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


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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

							# Detecting last checkpoint.
							snake_case_      =				None
							if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
														snake_case_      =				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.
														snake_case_      =				load_dataset(data_args.dataset_name							,    data_args.dataset_config_name					)
														if "validation" not in datasets.keys():
																					snake_case_      =				load_dataset(
																					    data_args.dataset_name							,    data_args.dataset_config_name							,    split=f'train[:{data_args.validation_split_percentage}%]'							,    )
																					snake_case_      =				load_dataset(
																					    data_args.dataset_name							,    data_args.dataset_config_name							,    split=f'train[{data_args.validation_split_percentage}%:]'							,    )
							else:
														snake_case_      =				{}
														if data_args.train_file is not None:
																					snake_case_      =				data_args.train_file
														if data_args.validation_file is not None:
																					snake_case_      =				data_args.validation_file
														snake_case_      =				data_args.train_file.split("."					)[-1]
														if extension == "txt":
																					snake_case_      =				"text"
														snake_case_      =				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.
							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,
							}
							if model_args.config_name:
														snake_case_      =				AutoConfig.from_pretrained(model_args.config_name							,    **snake_case					)
							elif model_args.model_name_or_path:
														snake_case_      =				AutoConfig.from_pretrained(model_args.model_name_or_path							,    **snake_case					)
							else:
														snake_case_      =				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_      =				{
							    "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_      =				AutoTokenizer.from_pretrained(model_args.tokenizer_name							,    **snake_case					)
							elif model_args.model_name_or_path:
														snake_case_      =				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:
														snake_case_      =				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"					)
														snake_case_      =				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:
														snake_case_      =				datasets["train"].column_names
							else:
														snake_case_      =				datasets["validation"].column_names
							snake_case_      =				"text" if "text" in column_names else column_names[0]

							snake_case_      =				"max_length" if data_args.pad_to_max_length else False

							def tokenize_function(snake_case							:	str					):
														# Remove empty lines
														snake_case_      =				[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					)

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

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

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

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

														snake_case_      =				trainer.evaluate()

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

														snake_case_      =				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 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

							'''simple docstring'''






							main()


if __name__ == "__main__":
		main()










'''simple docstring'''





from __future__ import annotations

from scipy.special import comb  # type: ignore




class 			_snake_case		:




			def __init__( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										snake_case_      =				len(a__							) - 1




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    list[float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										snake_case_      =				[]
										for i in range(len(self.list_of_points							)							):
																	# basis function for each i
																	output_values.append(
																	    comb(self.degree    ,							a__							) * ((1 - t) ** (self.degree - i)) * (t**i)							)
										# the basis must sum up to 1 for it to produce a valid Bezier curve.
										assert round(sum(a__							)    ,							5							) == 1
										return output_values




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    tuple[float, float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										snake_case_      =				self.basis_function(a__							)
										snake_case_      =				0.0
										snake_case_      =				0.0
										for i in range(len(self.list_of_points							)							):
																	# For all points, sum up the product of i-th basis function and i-th point.
																	x += basis_function[i] * self.list_of_points[i][0]
																	y += basis_function[i] * self.list_of_points[i][1]
										return (x, y)




			def        lowerCAmelCase__    ( self    ,							a__ = 0.0_1							)    ->    Any:

										'''simple docstring'''




										from matplotlib import pyplot as plt  # type: ignore

										snake_case_      =				[]  # x coordinates of points to plot
										snake_case_      =				[]  # y coordinates of points to plot

										snake_case_      =				0.0
										while t <= 1:
																	snake_case_      =				self.bezier_curve_function(a__							)
																	to_plot_x.append(value[0]							)
																	to_plot_y.append(value[1]							)
																	t += step_size

										snake_case_      =				[i[0] for i in self.list_of_points]
										snake_case_      =				[i[1] for i in self.list_of_points]

										plt.plot(
										    a__    ,							a__    ,							color="blue"    ,							label="Curve of Degree " + str(self.degree							)    ,							)
										plt.scatter(a__    ,							a__    ,							color="red"    ,							label="Control Points"							)
										plt.legend()
										plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
		BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
		BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3




