



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		:				Union[str, Any]      		=logging.getLogger(__name__)
_lowercase		:				Optional[Any]      		=list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_lowercase		:				Union[str, Any]      		=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class        snake_case__    :







						"""simple docstring"""




						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     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=A__ ,     metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(A__		)} ,     )
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     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=A__ ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     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=A__ ,     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  ) ->				int:
									"""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__    :







						"""simple docstring"""




						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "The name of the dataset to use (via the datasets library)."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
						__lowerCAmelCase					:Optional[str]      =					field(default=A__ ,     metadata={"help": "The input training data file (a text file)."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} ,     )
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "An optional input train ref data file for whole word masking in Chinese."} ,     )
						__lowerCAmelCase					:Optional[str]      =					field(
						    default=A__ ,     metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."} ,     )
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     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=A__ ,     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=A__ ,     metadata={"help": "The number of processes to use for the preprocessing."} ,     )
						__lowerCAmelCase					:float      =					field(
						    default=0.1_5 ,     metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     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  ) ->				int:
									"""simple docstring"""




									if self.train_file is not None:
												a__       :						Tuple							=  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__       :						Optional[Any]							=  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_					(  _lowercase    :       Dict						,       _lowercase    :       str)							->	Tuple:







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

			a__       :						Union[str, Any]							=  {c: dataset[c] for c in dataset.column_names}
			a__       :						List[Any]							=  refs
			return Dataset.from_dict(_lowercase)

def      lowerCAmelCase_					(  )							->	Tuple:







			"""simple docstring"""
			a__       :						Optional[Any]							=  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__       :						List[Any]							=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						a__       :						Optional[int]							=  parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			a__       :						List[str]							=  None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						a__       :						Tuple							=  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"""						,       _lowercase)

			# 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__       :						List[str]							=  load_dataset(data_args.dataset_name						,       data_args.dataset_config_name)
						if "validation" not in datasets.keys():
									a__       :						Optional[int]							=  load_dataset(
									    data_args.dataset_name						,       data_args.dataset_config_name						,       split=F'''train[:{data_args.validation_split_percentage}%]'''						,       )
									a__       :						List[Any]							=  load_dataset(
									    data_args.dataset_name						,       data_args.dataset_config_name						,       split=F'''train[{data_args.validation_split_percentage}%:]'''						,       )
			else:
						a__       :						Optional[int]							=  {}
						if data_args.train_file is not None:
									a__       :						int							=  data_args.train_file
						if data_args.validation_file is not None:
									a__       :						Optional[int]							=  data_args.validation_file
						a__       :						Dict							=  data_args.train_file.split(""".""")[-1]
						if extension == "txt":
									a__       :						Any							=  """text"""
						a__       :						Optional[Any]							=  load_dataset(_lowercase						,       data_files=_lowercase)
			# 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__       :						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:
						a__       :						Dict							=  AutoConfig.from_pretrained(model_args.config_name						,       **_lowercase)
			elif model_args.model_name_or_path:
						a__       :						Tuple							=  AutoConfig.from_pretrained(model_args.model_name_or_path						,       **_lowercase)
			else:
						a__       :						Optional[int]							=  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__       :						Union[str, Any]							=  {
			    """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__       :						int							=  AutoTokenizer.from_pretrained(model_args.tokenizer_name						,       **_lowercase)
			elif model_args.model_name_or_path:
						a__       :						int							=  AutoTokenizer.from_pretrained(model_args.model_name_or_path						,       **_lowercase)
			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__       :						Any							=  AutoModelForMaskedLM.from_pretrained(
						    model_args.model_name_or_path						,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path)						,       config=_lowercase						,       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__       :						Optional[int]							=  AutoModelForMaskedLM.from_config(_lowercase)

			model.resize_token_embeddings(len(_lowercase))

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

			a__       :						Any							=  """max_length""" if data_args.pad_to_max_length else False

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

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

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

			# Initialize our Trainer
			a__       :						List[str]							=  Trainer(
			    model=_lowercase						,       args=_lowercase						,       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=_lowercase						,       data_collator=_lowercase						,       )

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

						a__       :						Optional[int]							=  os.path.join(training_args.output_dir						,       """train_results.txt""")
						if trainer.is_world_process_zero():
									with open(_lowercase						,       """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__       :						int							=  {}
			if training_args.do_eval:
						logger.info("""*** Evaluate ***""")

						a__       :						Tuple							=  trainer.evaluate()

						a__       :						Dict							=  math.exp(eval_output["""eval_loss"""])
						a__       :						List[Any]							=  perplexity

						a__       :						List[Any]							=  os.path.join(training_args.output_dir						,       """eval_results_mlm_wwm.txt""")
						if trainer.is_world_process_zero():
									with open(_lowercase						,       """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_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			main()


if __name__ == "__main__":
		main()









from __future__ import annotations

import math

def      lowerCAmelCase_					(  _lowercase    :       float						,       _lowercase    :       int)							->	float:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  u
			for i in range(1						,       _lowercase):
						a__       :						Optional[int]							=  temp * (u - i)
			return temp

def      lowerCAmelCase_					(  )							->	None:







			"""simple docstring"""
			a__       :						Tuple							=  int(input("""enter the numbers of values: """))
			a__       :						list[list[float]]							=  []
			for _ in range(_lowercase):
						y.append([])
			for i in range(_lowercase):
						for j in range(_lowercase):
									y[i].append(_lowercase)
									a__       :						Optional[Any]							=  0

			print("""enter the values of parameters in a list: """)
			a__       :						List[Any]							=  list(map(_lowercase						,       input().split()))

			print("""enter the values of corresponding parameters: """)
			for i in range(_lowercase):
						a__       :						Optional[Any]							=  float(input())

			a__       :						Tuple							=  int(input("""enter the value to interpolate: """))
			a__       :						int							=  (value - x[0]) / (x[1] - x[0])

			# for calculating forward difference table

			for i in range(1						,       _lowercase):
						for j in range(n - i):
									a__       :						int							=  y[j + 1][i - 1] - y[j][i - 1]

			a__       :						Optional[int]							=  y[0][0]
			for i in range(1						,       _lowercase):
						summ += (ucal(_lowercase						,       _lowercase) * y[0][i]) / math.factorial(_lowercase)

			print(F'''the value at {value} is {summ}''')


if __name__ == "__main__":
		main()




