



'''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


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






@dataclass
class 						a_		:
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={
							        """help""": (
							            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
							        )
							    }    ,   )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(snake_case    )}    ,   )
							UpperCAmelCase			:		Optional[str]        =				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"""
							        )
							    }    ,   )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}    )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,   )
							UpperCAmelCase			:		bool        =				field(
							    default=snake_case    ,   metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}    ,   )
							UpperCAmelCase			:		str        =				field(
							    default="""main"""    ,   metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,   )
							UpperCAmelCase			:		bool        =				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    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Dict:
										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 						a_		:
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}    )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
							UpperCAmelCase			:		Optional[str]        =				field(default=snake_case    ,   metadata={"""help""": """The input training data file (a text file)."""}    )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}    ,   )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}    ,   )
							UpperCAmelCase			:		Optional[str]        =				field(
							    default=snake_case    ,   metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}    ,   )
							UpperCAmelCase			:		bool        =				field(
							    default=snake_case    ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )
							UpperCAmelCase			:		Optional[int]        =				field(
							    default=5    ,   metadata={
							        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
							    }    ,   )
							UpperCAmelCase			:		Optional[int]        =				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."""
							        )
							    }    ,   )
							UpperCAmelCase			:		Optional[int]        =				field(
							    default=snake_case    ,   metadata={"""help""": """The number of processes to use for the preprocessing."""}    ,   )
							UpperCAmelCase			:		float        =				field(
							    default=0.1_5    ,   metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}    )
							UpperCAmelCase			:		bool        =				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    UpperCamelCase     (				self   :						Any			)			->					List[Any]:
										if self.train_file is not None:
													snake_case:    List[Any]      							=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:    List[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 a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







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

			snake_case:    Dict      							={c: dataset[c] for c in dataset.column_names}
			snake_case:    Optional[Any]      							=refs
			return Dataset.from_dict(__UpperCAmelCase					)







def a_     (    )				->					Dict:





			"""simple docstring"""







			snake_case:    List[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.
						snake_case							,  snake_case							,  snake_case:    Tuple      							=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
			else:
						snake_case							,  snake_case							,  snake_case:    str      							=parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			snake_case:    str      							=None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						snake_case:    Any      							=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.
						snake_case:    Tuple      							=load_dataset(data_args.dataset_name				,							data_args.dataset_config_name					)
						if "validation" not in datasets.keys():
									snake_case:    Optional[Any]      							=load_dataset(
									    data_args.dataset_name				,							data_args.dataset_config_name				,							split=f'''train[:{data_args.validation_split_percentage}%]'''				,							)
									snake_case:    Dict      							=load_dataset(
									    data_args.dataset_name				,							data_args.dataset_config_name				,							split=f'''train[{data_args.validation_split_percentage}%:]'''				,							)
			else:
						snake_case:    Union[str, Any]      							={}
						if data_args.train_file is not None:
									snake_case:    Optional[Any]      							=data_args.train_file
						if data_args.validation_file is not None:
									snake_case:    Any      							=data_args.validation_file
						snake_case:    Tuple      							=data_args.train_file.split('.'					)[-1]
						if extension == "txt":
									snake_case:    Union[str, Any]      							='text'
						snake_case:    Tuple      							=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.
			snake_case:    Dict      							={
			    '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:    List[str]      							=AutoConfig.from_pretrained(model_args.config_name				,							**__UpperCAmelCase					)
			elif model_args.model_name_or_path:
						snake_case:    Dict      							=AutoConfig.from_pretrained(model_args.model_name_or_path				,							**__UpperCAmelCase					)
			else:
						snake_case:    Dict      							=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:    Tuple      							={
			    '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:    Optional[int]      							=AutoTokenizer.from_pretrained(model_args.tokenizer_name				,							**__UpperCAmelCase					)
			elif model_args.model_name_or_path:
						snake_case:    Union[str, Any]      							=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:
						snake_case:    int      							=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'					)
						snake_case:    str      							=AutoModelForMaskedLM.from_config(__UpperCAmelCase					)

			model.resize_token_embeddings(len(__UpperCAmelCase					)					)

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

			snake_case:    List[Any]      							='max_length' if data_args.pad_to_max_length else False

			def tokenize_function(__UpperCAmelCase					):
						# Remove empty lines
						snake_case:    Optional[Any]      							=[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					)

			snake_case:    List[str]      							=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:
						snake_case:    Any      							=add_chinese_references(tokenized_datasets['train']				,							data_args.train_ref_file					)
			if data_args.validation_ref_file is not None:
						snake_case:    Union[str, Any]      							=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:    int      							=data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						snake_case:    List[str]      							=False

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

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

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

						snake_case:    List[str]      							=trainer.evaluate()

						snake_case:    List[str]      							=math.exp(eval_output['eval_loss']					)
						snake_case:    List[str]      							=perplexity

						snake_case:    Any      							=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:





			"""simple docstring"""







			main()


if __name__ == "__main__":
	main()









'''simple docstring'''

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				MgpstrTokenizer
							UpperCAmelCase			:		List[Any]        =				False
							UpperCAmelCase			:		Optional[int]        =				{}
							UpperCAmelCase			:		Any        =				False


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										super().setUp()

										# fmt: off
										snake_case:    List[Any]      							=['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
										# fmt: on
										snake_case:    Union[str, Any]      							=dict(zip(a_      ,						range(len(a_			)			)			)			)

										snake_case:    List[Any]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write(json.dumps(a_			) + '\n'			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						**a_   :						List[Any]			)			->					str:
										return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Dict			)			->					int:
										snake_case:    Any      							='tester'
										snake_case:    List[str]      							='tester'
										return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.'			)
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										pass


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										snake_case:    int      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case:    Optional[Any]      							='[SPECIAL_TOKEN]'

																tokenizer.add_special_tokens({'cls_token': special_token}			)
																snake_case:    List[str]      							=tokenizer.encode([special_token]      ,						add_special_tokens=a_			)
																self.assertEqual(len(a_			)      ,						1			)

																snake_case:    Union[str, Any]      							=tokenizer.decode(a_      ,						skip_special_tokens=a_			)
																self.assertTrue(special_token not in decoded			)


							def    UpperCamelCase     (				self   :						int			)			->					str:
										snake_case:    int      							=self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case							,  snake_case:    Dict      							=self.get_input_output_texts(a_			)

																snake_case:    Dict      							=tokenizer.tokenize(a_			)
																snake_case:    str      							=tokenizer.convert_tokens_to_ids(a_			)
																snake_case:    List[Any]      							=tokenizer.encode(a_      ,						add_special_tokens=a_			)
																self.assertListEqual(a_      ,						a_			)

																snake_case:    str      							=tokenizer.convert_ids_to_tokens(a_			)
																self.assertNotEqual(len(a_			)      ,						0			)
																snake_case:    Tuple      							=tokenizer.decode(a_			)
																self.assertIsInstance(a_      ,						a_			)

																self.assertEqual(text_a.replace(' '      ,						''			)      ,						a_			)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.'			)
							def    UpperCamelCase     (				self   :						Dict			)			->					str:
										pass




							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'			)
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										pass




