



# Algorithm for the pigeonhole sorting







def 							A			(  _UpperCAmelCase       :     List[str]					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     min(_UpperCAmelCase					)  # min() finds the minimum value
			_UpperCAmelCase	 =     max(_UpperCAmelCase					)  # max() finds the maximum value

			_UpperCAmelCase	 =     max_val - min_val + 1  # size is difference of max and min values plus one

			# list of pigeonholes of size equal to the variable size
			_UpperCAmelCase	 =     [0] * size

			# Populate the pigeonholes.
			for x in a:
						assert isinstance(_UpperCAmelCase							,     _UpperCAmelCase					), "integers only please"
						holes[x - min_val] += 1

			# Putting the elements back into the array in an order.
			_UpperCAmelCase	 =     0
			for count in range(_UpperCAmelCase					):
						while holes[count] > 0:
									holes[count] -= 1
									_UpperCAmelCase	 =     count + min_val
									i += 1





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     [8, 3, 2, 7, 4, 6, 8]
			pigeonhole_sort(_UpperCAmelCase					)
			print('Sorted order is:'							,     ' '.join(_UpperCAmelCase					)					)


if __name__ == "__main__":
							main()





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


UpperCAmelCase__     = logging.getLogger(__name__)
UpperCAmelCase__     = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase__     = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A		)}       ,		)
			UpperCamelCase           =  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'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		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=A       ,		metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }       ,		)



			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""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     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(default=A       ,		metadata={'''help''': '''The input training data file (a text file).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		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=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.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,		)
			UpperCamelCase           =  field(
			    default=0.15       ,		metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}		)
			UpperCamelCase           =  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 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



						"""simple docstring"""






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





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Optional[Any]:







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

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





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			# 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.

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

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

			_UpperCAmelCase	 =     {
			    '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:
						_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     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:
						_UpperCAmelCase	 =     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'					)
						_UpperCAmelCase	 =     AutoModelForMaskedLM.from_config(_UpperCAmelCase					)

			model.resize_token_embeddings(len(_UpperCAmelCase					)					)

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

			_UpperCAmelCase	 =     'max_length' if data_args.pad_to_max_length else False

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

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

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

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

						_UpperCAmelCase	 =     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
			_UpperCAmelCase	 =     {}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'					)

						_UpperCAmelCase	 =     trainer.evaluate()

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

						_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     str					)       ->				Optional[Any]:







			'''simple docstring'''
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()
