





'''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


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






     """simple docstring"""






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



     def      lowerCAmelCase			(   self							:			List[str]						)->	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 			_lowerCAmelCase		:






     """simple docstring"""






     snake_case_						      =   field(
         default=A__							,     metadata={"help": "The name of the dataset to use (via the datasets library)."}	)
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
     snake_case_						      =   field(default=A__							,     metadata={"help": "The input training data file (a text file)."}	)
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}							,     )
     snake_case_						      =   field(
         default=A__							,     metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}							,     )
     snake_case_						      =   field(
         default=A__							,     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=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."
             )
         }							,     )
     snake_case_						      =   field(
         default=A__							,     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=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							:			Tuple						)->	int:
        if self.train_file is not None:
           snake_case 						=   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 						=   self.validation_file.split("""."""						)[-1]
           assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							str		)		->  Optional[Any]:
   with open(__lowerCAmelCase      ,    """r"""      ,    encoding="""utf-8"""		) as f:
      snake_case 						=   [json.loads(__lowerCAmelCase		) for line in f.read().splitlines() if (len(__lowerCAmelCase		) > 0 and not line.isspace())]
   assert len(__lowerCAmelCase		) == len(__lowerCAmelCase		)

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





def 	__lowerCamelCase			(      )		->  List[Any]:
   # 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.

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

   # Detecting last checkpoint.
   snake_case 						=   None
   if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
      snake_case 						=   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"""      ,    __lowerCAmelCase		)

   # 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 						=   load_dataset(data_args.dataset_name      ,    data_args.dataset_config_name		)
      if "validation" not in datasets.keys():
         snake_case 						=   load_dataset(
             data_args.dataset_name      ,    data_args.dataset_config_name      ,    split=F'''train[:{data_args.validation_split_percentage}%]'''      ,    )
         snake_case 						=   load_dataset(
             data_args.dataset_name      ,    data_args.dataset_config_name      ,    split=F'''train[{data_args.validation_split_percentage}%:]'''      ,    )
   else:
      snake_case 						=   {}
      if data_args.train_file is not None:
         snake_case 						=   data_args.train_file
      if data_args.validation_file is not None:
         snake_case 						=   data_args.validation_file
      snake_case 						=   data_args.train_file.split("""."""		)[-1]
      if extension == "txt":
         snake_case 						=   """text"""
      snake_case 						=   load_dataset(__lowerCAmelCase      ,    data_files=__lowerCAmelCase		)
   # 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 						=   {
       """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 						=   AutoConfig.from_pretrained(model_args.config_name      ,    **__lowerCAmelCase		)
   elif model_args.model_name_or_path:
      snake_case 						=   AutoConfig.from_pretrained(model_args.model_name_or_path      ,    **__lowerCAmelCase		)
   else:
      snake_case 						=   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 						=   {
       """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 						=   AutoTokenizer.from_pretrained(model_args.tokenizer_name      ,    **__lowerCAmelCase		)
   elif model_args.model_name_or_path:
      snake_case 						=   AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,    **__lowerCAmelCase		)
   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 						=   AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path      ,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)      ,    config=__lowerCAmelCase      ,    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 						=   AutoModelForMaskedLM.from_config(__lowerCAmelCase		)

   model.resize_token_embeddings(len(__lowerCAmelCase		)		)

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

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

   def tokenize_function(__lowerCAmelCase       :							Optional[Any]		):
      # Remove empty lines
      snake_case 						=   [line for line in examples["""text"""] if len(__lowerCAmelCase		) > 0 and not line.isspace()]
      return tokenizer(examples["""text"""]      ,    padding=__lowerCAmelCase      ,    truncation=__lowerCAmelCase      ,    max_length=data_args.max_seq_length		)

   snake_case 						=   datasets.map(
       __lowerCAmelCase      ,    batched=__lowerCAmelCase      ,    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 						=   add_chinese_references(tokenized_datasets["""train"""]      ,    data_args.train_ref_file		)
   if data_args.validation_ref_file is not None:
      snake_case 						=   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 						=   data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      snake_case 						=   False

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

   # Initialize our Trainer
   snake_case 						=   Trainer(
       model=__lowerCAmelCase      ,    args=__lowerCAmelCase      ,    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=__lowerCAmelCase      ,    data_collator=__lowerCAmelCase      ,    )

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         snake_case 						=   last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path		):
         snake_case 						=   model_args.model_name_or_path
      else:
         snake_case 						=   None
      snake_case 						=   trainer.train(resume_from_checkpoint=__lowerCAmelCase		)
      trainer.save_model()  # Saves the tokenizer too for easy upload

      snake_case 						=   os.path.join(training_args.output_dir      ,    """train_results.txt"""		)
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase      ,    """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 						=   {}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""		)

      snake_case 						=   trainer.evaluate()

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

      snake_case 						=   os.path.join(training_args.output_dir      ,    """eval_results_mlm_wwm.txt"""		)
      if trainer.is_world_process_zero():
         with open(__lowerCAmelCase      ,    """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 	__lowerCamelCase			(      __lowerCAmelCase       :							List[Any]		)		->  int:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
 main()








'''simple docstring'''




import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""						)
        snake_case 						=   AutoTokenizer.from_pretrained("""google/mt5-small"""						)

        snake_case 						=   tokenizer("""Hello there"""   ,       return_tensors="""np"""						).input_ids
        snake_case 						=   tokenizer("""Hi I am"""   ,       return_tensors="""np"""						).input_ids

        snake_case 						=   shift_tokens_right(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

        snake_case 						=   model(__snake_case   ,       decoder_input_ids=__snake_case						).logits
        snake_case 						=   optax.softmax_cross_entropy(__snake_case   ,       onehot(__snake_case   ,       logits.shape[-1]						)						).mean()

        snake_case 						=   -(labels.shape[-1] * loss.item())

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

