




"""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


lowerCAmelCase							:			str     							=							logging.getLogger(__name__)
lowerCAmelCase							:			str     							=							list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase							:			Tuple     							=							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class     __magic_name__							:






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





				def    _lowerCAmelCase				(       self ):





							"""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     __magic_name__							:






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





							"""simple docstring"""
							if self.train_file is not None:
										lowerCamelCase			      =					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:
										lowerCamelCase			      =					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__ (				snake_case__       , snake_case__				)						->					Optional[Any]:




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

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



def     a__ (				)						->					Any:
			lowerCamelCase			      =					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.
						lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
			else:
						lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					parser.parse_args_into_dataclasses()

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

			lowerCamelCase			      =					{
			    """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:
						lowerCamelCase			      =					AutoTokenizer.from_pretrained(model_args.tokenizer_name       , **snake_case_				)
			elif model_args.model_name_or_path:
						lowerCamelCase			      =					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:
						lowerCamelCase			      =					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"""				)
						lowerCamelCase			      =					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:
						lowerCamelCase			      =					datasets["""train"""].column_names
			else:
						lowerCamelCase			      =					datasets["""validation"""].column_names
			lowerCamelCase			      =					"""text""" if """text""" in column_names else column_names[0]

			lowerCamelCase			      =					"""max_length""" if data_args.pad_to_max_length else False

			def tokenize_function(snake_case__				):
						# Remove empty lines
						lowerCamelCase			      =					[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				)

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

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

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

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

						lowerCamelCase			      =					trainer.evaluate()

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

						lowerCamelCase			      =					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     a__ (				snake_case__				)						->					Union[str, Any]:
			main()


if __name__ == "__main__":
					main()




'''simple docstring'''





import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   tempfile.mkdtemp()
         UpperCAmelCase_           =   5

         # Realm tok
         UpperCAmelCase_           =   [
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "test",
             "question",
             "this",
             "is",
             "the",
             "first",
             "second",
             "third",
             "fourth",
             "fifth",
             "record",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_tokenizer" )
         os.makedirs(__a   ,			exist_ok=__a )
         UpperCAmelCase_           =   os.path.join(__a   ,			VOCAB_FILES_NAMES["vocab_file"] )
         with open(self.vocab_file   ,			"w"   ,			encoding="utf-8" ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )

         UpperCAmelCase_           =   os.path.join(self.tmpdirname   ,			"realm_block_records" )
         os.makedirs(__a   ,			exist_ok=__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_tokenizer" ) )



    def 					_lowercase						(self    :    Any ):
         shutil.rmtree(self.tmpdirname )



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   RealmConfig(num_block_records=self.num_block_records )
         return config



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "question": ["foo", "bar"],
                 "answers": [["Foo", "Bar"], ["Bar"]],
             } )
         return dataset



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   np.array(
             [
                 B"This is the first record",
                 B"This is the second record",
                 B"This is the third record",
                 B"This is the fourth record",
                 B"This is the fifth record",
                 B"This is a longer longer longer record",
             ]   ,			dtype=__a   ,			)
         return block_records



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   RealmRetriever(
             block_records=self.get_dummy_block_records()   ,			tokenizer=self.get_tokenizer()   ,			)
         return retriever



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(len(__a )   ,			2 )
         self.assertEqual(concat_inputs.input_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.attention_mask.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.token_type_ids.shape   ,			(2, 10) )
         self.assertEqual(concat_inputs.special_tokens_mask.shape   ,			(2, 10) )
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]   ,			)
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] )   ,			["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]   ,			)



    def 					_lowercase						(self    :    List[Any] ):
         UpperCAmelCase_           =   self.get_config()
         UpperCAmelCase_           =   self.get_dummy_retriever()
         UpperCAmelCase_           =   retriever.tokenizer

         UpperCAmelCase_           =   np.array([0, 3, 5]   ,			dtype="long" )
         UpperCAmelCase_           =   tokenizer(["Test question"] ).input_ids
         UpperCAmelCase_           =   tokenizer(
             ["the fourth", "longer longer"]   ,			add_special_tokens=__a   ,			return_token_type_ids=__a   ,			return_attention_mask=__a   ,			).input_ids
         UpperCAmelCase_           =   config.reader_seq_len

         UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   retriever(
             __a   ,			__a   ,			answer_ids=__a   ,			max_length=__a   ,			return_tensors="np" )

         self.assertEqual([False, True, True]   ,			__a )
         self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]   ,			__a )
         self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]   ,			__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )

         # Test local path
         UpperCAmelCase_           =   retriever.from_pretrained(os.path.join(self.tmpdirname   ,			"realm_block_records" ) )
         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )

         # Test mocked remote path
         with patch("transformers.models.realm.retrieval_realm.hf_hub_download" ) as mock_hf_hub_download:
              UpperCAmelCase_           =   os.path.join(
                  os.path.join(self.tmpdirname   ,			"realm_block_records" )   ,			_REALM_BLOCK_RECORDS_FILENAME )
              UpperCAmelCase_           =   RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa" )

         self.assertEqual(retriever.block_records[0]   ,			B"This is the first record" )




