
"""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__   :					List[str]							=   logging.getLogger(__name__)
A__   :					Tuple							=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
A__   :					Optional[Any]							=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


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





         """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__       :
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
       UpperCamelCase_   =		field(default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''The input training data file (a text file).'''} )
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}   ,					)
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}   ,					)
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}   ,					)
       UpperCamelCase_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					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=SCREAMING_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_   =		field(
           default=SCREAMING_SNAKE_CASE__   ,					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=SCREAMING_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 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self.train_file is not None:
           _lowercase:			Optional[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:
           _lowercase:			Dict       =     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		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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

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




def  _lowerCAmelCase		(		):






  """simple docstring"""



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

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

  _lowercase:			str       =     {
      '''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:
    _lowercase:			str       =     AutoTokenizer.from_pretrained(model_args.tokenizer_name ,				**_UpperCamelCase						)
  elif model_args.model_name_or_path:
    _lowercase:			str       =     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:
    _lowercase:			List[str]       =     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'''						)
    _lowercase:			Optional[int]       =     AutoModelForMaskedLM.from_config(_UpperCamelCase						)

  model.resize_token_embeddings(len(_UpperCamelCase						)						)

  # Preprocessing the datasets.
  # First we tokenize all the texts.
  if training_args.do_train:
    _lowercase:			int       =     datasets['''train'''].column_names
  else:
    _lowercase:			Union[str, Any]       =     datasets['''validation'''].column_names
  _lowercase:			Tuple       =     '''text''' if '''text''' in column_names else column_names[0]

  _lowercase:			Optional[Any]       =     '''max_length''' if data_args.pad_to_max_length else False

  def tokenize_function(_UpperCamelCase						):
    # Remove empty lines
    _lowercase:			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						)

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

  # Data collator
  # This one will take care of randomly masking the tokens.
  _lowercase:			Union[str, Any]       =     DataCollatorForWholeWordMask(tokenizer=_UpperCamelCase ,				mlm_probability=data_args.mlm_probability						)

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

    _lowercase:			Optional[Any]       =     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
  _lowercase:			Union[str, Any]       =     {}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''						)

    _lowercase:			List[Any]       =     trainer.evaluate()

    _lowercase:			Tuple       =     math.exp(eval_output['''eval_loss''']						)
    _lowercase:			str       =     perplexity

    _lowercase:			List[str]       =     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  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  main()


if __name__ == "__main__":
  main()


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
