






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_           = logging.getLogger(__name__)
lowerCAmelCase_           = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase_           = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   field(
		    default=snake_case__			,       metadata={
		        "help": (
		            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
		        )
		    }			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(snake_case__ )}			,       )
		snake_case    =   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"
		        )
		    }			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}			,       )
		snake_case    =   field(
		    default="main"			,       metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,       )
		snake_case    =   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       A(  self		:       Optional[Any]		)    ->   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      UpperCamelCase :







		"""simple docstring"""



		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "The name of the dataset to use (via the datasets library)."} )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
		snake_case    =   field(default=snake_case__			,       metadata={"help": "The input training data file (a text file)."} )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "Overwrite the cached training and evaluation sets"} )
		snake_case    =   field(
		    default=5			,       metadata={
		        "help": "The percentage of the train set used as validation set in case there's no validation split"
		    }			,       )
		snake_case    =   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."
		        )
		    }			,       )
		snake_case    =   field(
		    default=snake_case__			,       metadata={"help": "The number of processes to use for the preprocessing."}			,       )
		snake_case    =   field(
		    default=0.15			,       metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} )
		snake_case    =   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       A(  self		:       str		)    ->   int:


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

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







def      snake_case		(							):
				# 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.

				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 ,		A ,		A	 =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								A ,		A ,		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',						UpperCAmelCase   )

				# 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(UpperCAmelCase,						data_files=UpperCAmelCase   )
				# 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,						**UpperCAmelCase   )
				elif model_args.model_name_or_path:
								A	 =      AutoConfig.from_pretrained(model_args.model_name_or_path,						**UpperCAmelCase   )
				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,						**UpperCAmelCase   )
				elif model_args.model_name_or_path:
								A	 =      AutoTokenizer.from_pretrained(model_args.model_name_or_path,						**UpperCAmelCase   )
				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=UpperCAmelCase,						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(UpperCAmelCase   )

				model.resize_token_embeddings(len(UpperCAmelCase   )   )

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

				A	 =      datasets.map(
				    UpperCAmelCase,						batched=UpperCAmelCase,						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=UpperCAmelCase,						mlm_probability=data_args.mlm_probability   )

				# Initialize our Trainer
				A	 =      Trainer(
				    model=UpperCAmelCase,						args=UpperCAmelCase,						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=UpperCAmelCase,						data_collator=UpperCAmelCase,						)

				# 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=UpperCAmelCase   )
								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(UpperCAmelCase,						'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(UpperCAmelCase,						'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      snake_case		(							UpperCAmelCase	:       Any   ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()













from math import factorial
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      real
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      [1] * rank
						else:
										A	 =      rank
		def __repr__(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						return (
						    f'{self.real}+'
						    f'{"+".join(str(_SCREAMING_SNAKE_CASE		)+"E"+str(n+1		)for n,dual in enumerate(self.duals		)		)}'
						)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.duals.copy()
						while cur[-1] == 0:
										cur.pop(-1		)
						return Dual(self.real						,_SCREAMING_SNAKE_CASE		)
		def __add__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										return Dual(self.real + other						,self.duals		)
						A	 =      self.duals.copy()
						A	 =      other.duals.copy()
						if len(_SCREAMING_SNAKE_CASE		) > len(_SCREAMING_SNAKE_CASE		):
										o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						elif len(_SCREAMING_SNAKE_CASE		) < len(_SCREAMING_SNAKE_CASE		):
										s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						A	 =      []
						for i in range(len(_SCREAMING_SNAKE_CASE		)		):
										new_duals.append(s_dual[i] + o_dual[i]		)
						return Dual(self.real + other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __add__
		def __sub__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						return self + other * -1
		def __mul__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i * other		)
										return Dual(self.real * other						,_SCREAMING_SNAKE_CASE		)
						A	 =      [0] * (len(self.duals		) + len(other.duals		) + 1)
						for i, item in enumerate(self.duals		):
										for j, jtem in enumerate(other.duals		):
														new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals		)		):
										new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals		)		):
										new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __mul__
		def __truediv__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i / other		)
										return Dual(self.real / other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError
		def __floordiv__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   Dict:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i // other		)
										return Dual(self.real // other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError







		def __pow__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						if n < 0 or isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										raise ValueError('power must be a positive integer'		)
						if n == 0:
										return 1
						if n == 1:
										return self
						A	 =      self
						for _ in range(n - 1		):
										x *= self
						return x







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int   ):
				if not callable(UpperCAmelCase   ):
								raise ValueError('differentiate() requires a function as input for func'   )
				if not isinstance(UpperCAmelCase,						(float, int)   ):
								raise ValueError('differentiate() requires a float as input for position'   )
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								raise ValueError('differentiate() requires an int as input for order'   )
				A	 =      Dual(UpperCAmelCase,						1   )
				A	 =      func(UpperCAmelCase   )
				if order == 0:
								return result.real
				return result.duals[order - 1] * factorial(UpperCAmelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def      snake_case		(							UpperCAmelCase	:       int   ):
									return y**2 * y**4



					print(differentiate(f, 9, 2))





