




"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowercase						   =     logging.getLogger(__name__)

@dataclass
class    __lowercase							:


				'''simple docstring'''


				_lowerCAmelCase						      =field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
				_lowerCAmelCase						      =field(
				    default=A_    , metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
				_lowerCAmelCase						      =field(
				    default=A_    , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
				_lowerCAmelCase						      =field(default=A_    , metadata={"help": "Set this flag to use fast tokenization."}						)
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				_lowerCAmelCase						      =field(
				    default=A_    , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    , )







def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       Dict				      =       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__							:       Optional[int]				      =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												snake_case__      ,						snake_case__      ,						snake_case__							:       Any				      =       parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir		)
						    and os.listdir(training_args.output_dir		)
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
												    ''' --overwrite_output_dir to overcome.'''		)

						# Setup logging
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,      datefmt='''%m/%d/%Y %H:%M:%S'''   ,      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,      )
						logger.warning(
						    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''   ,      training_args.local_rank   ,      training_args.device   ,      training_args.n_gpu   ,      bool(training_args.local_rank != -1		)   ,      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'''   ,      A__		)

						# Prepare Question-Answering task
						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						snake_case__							:       str				      =       AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,      cache_dir=model_args.cache_dir   ,      )
						snake_case__							:       List[str]				      =       AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,      cache_dir=model_args.cache_dir   ,      use_fast=A__   ,      )
						snake_case__							:       Optional[int]				      =       AutoModelForQuestionAnswering.from_pretrained(
						    model_args.model_name_or_path   ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)   ,      config=A__   ,      cache_dir=model_args.cache_dir   ,      )

						# Get datasets
						snake_case__							:       str				      =       hasattr(model.config   ,      '''lang2id'''		)
						snake_case__							:       Optional[Any]				      =       (
						    SquadDataset(
						        A__   ,      tokenizer=A__   ,      is_language_sensitive=A__   ,      cache_dir=model_args.cache_dir		)
						    if training_args.do_train
						    else None
						)
						snake_case__							:       Dict				      =       (
						    SquadDataset(
						        A__   ,      tokenizer=A__   ,      mode='''dev'''   ,      is_language_sensitive=A__   ,      cache_dir=model_args.cache_dir   ,      )
						    if training_args.do_eval
						    else None
						)

						# Data collator
						snake_case__							:       Optional[int]				      =       DataCollatorWithPadding(A__   ,      pad_to_multiple_of=8		) if training_args.fpaa else None

						# Initialize our Trainer
						snake_case__							:       Dict				      =       Trainer(
						    model=A__   ,      args=A__   ,      train_dataset=A__   ,      eval_dataset=A__   ,      data_collator=A__   ,      )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_master():
																		tokenizer.save_pretrained(training_args.output_dir		)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						main()


if __name__ == "__main__":
							main()




from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest







@custom_tokenizers
class 	lowerCamelCase						(    A_    ):





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       Tuple							,       _A :       Tuple							,       _A :       str							,       _A :       Tuple							,       _A :       Any  )		->		Optional[int]:
         return True





     def 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"  )
         snake_case						=   "Hugging Face大法好，谁用谁知道。"
         snake_case						=   "Hugging Face大法好,谁用谁知道。<unk>"
         snake_case						=   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

         snake_case						=   tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokens + [tokenizer.unk_token]

         snake_case						=   [1_3_7_8_9, 1_3_2_8_3, 1_4_2_1, 8, 1_0, 1_1_6_4, 1_3_6_0_8, 1_6_5_2_8, 6_3, 8, 9, 4_4_0, 1_0_8, 4_4_0, 1_2_1, 9_0, 8, 1_2, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A  )							,       _A  )

         snake_case						=   tokenizer.decode(_A  )
         self.assertEqual(_A							,       _A  )

