


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




@dataclass
class 				UpperCAmelCase			:
 UpperCAmelCase				=					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				=					field(
     default=_snake_case						, metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(_snake_case						)}						, )
 UpperCAmelCase				=					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				=					field(
     default=_snake_case						, metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						, )
 UpperCAmelCase				=					field(
     default=_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=_snake_case						, metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     }						, )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    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 				UpperCAmelCase			:
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "The name of the dataset to use (via the datasets library)."}						)
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
 UpperCAmelCase				=					field(default=_snake_case						, metadata={"help": "The input training data file (a text file)."}						)
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						, )
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}						, )
 UpperCAmelCase				=					field(
     default=_snake_case						, metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}						, )
 UpperCAmelCase				=					field(
     default=_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=_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=_snake_case						, metadata={"help": "The number of processes to use for the preprocessing."}						, )
 UpperCAmelCase				=					field(
     default=0.15						, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}						)
 UpperCAmelCase				=					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        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    if self.train_file is not None:
       UpperCAmelCase__	: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:
       UpperCAmelCase__	:str      =      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_						:      List[Any],   UpperCamelCase_						:      str						):
   with open(UpperCamelCase_,   '''r''',   encoding='''utf-8'''						) as f:
      UpperCAmelCase__	:Tuple      =      [json.loads(UpperCamelCase_						) for line in f.read().splitlines() if (len(UpperCamelCase_						) > 0 and not line.isspace())]
   assert len(UpperCamelCase_						) == len(UpperCamelCase_						)

   UpperCAmelCase__	:Tuple      =      {c: dataset[c] for c in dataset.column_names}
   UpperCAmelCase__	:Tuple      =      refs
   return Dataset.from_dict(UpperCamelCase_						)


def        a__     (					):
   # 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.

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

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

   UpperCAmelCase__	:List[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:
      UpperCAmelCase__	:List[str]      =      AutoTokenizer.from_pretrained(model_args.tokenizer_name,   **UpperCamelCase_						)
   elif model_args.model_name_or_path:
      UpperCAmelCase__	:Dict      =      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:
      UpperCAmelCase__	:Union[str, Any]      =      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'''						)
      UpperCAmelCase__	: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:
      UpperCAmelCase__	:Optional[Any]      =      datasets['''train'''].column_names
   else:
      UpperCAmelCase__	:Any      =      datasets['''validation'''].column_names
   UpperCAmelCase__	:Optional[int]      =      '''text''' if '''text''' in column_names else column_names[0]

   UpperCAmelCase__	:Union[str, Any]      =      '''max_length''' if data_args.pad_to_max_length else False

   def tokenize_function(UpperCamelCase_						:      Optional[int]						):
      # Remove empty lines
      UpperCAmelCase__	:List[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						)

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

   # Data collator
   # This one will take care of randomly masking the tokens.
   UpperCAmelCase__	:Optional[Any]      =      DataCollatorForWholeWordMask(tokenizer=UpperCamelCase_,   mlm_probability=data_args.mlm_probability						)

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

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

      UpperCAmelCase__	:Dict      =      trainer.evaluate()

      UpperCAmelCase__	:Optional[int]      =      math.exp(eval_output['''eval_loss''']						)
      UpperCAmelCase__	:int      =      perplexity

      UpperCAmelCase__	: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        a__     (					UpperCamelCase_						:      List[Any]						):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()







'''simple docstring'''





from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase												=						logging.get_logger(__name__)

__lowerCamelCase												=						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}




class 				UpperCAmelCase			(			_snake_case						):
 UpperCAmelCase				=					"autoformer"
 UpperCAmelCase				=					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }


 def __init__(      self							:	List[str]			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "student_t"			,       __lowerCamelCase							:	str = "nll"			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	List[int] = [1, 2, 3, 4, 5, 6, 7]			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	int = 0			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	Optional[List[int]] = None			,       __lowerCamelCase							:	int = 6_4			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	str = "gelu"			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	float = 0.1			,       __lowerCamelCase							:	int = 1_0_0			,       __lowerCamelCase							:	float = 0.02			,       __lowerCamelCase							:	bool = True			,       __lowerCamelCase							:	Optional[int]=True			,       __lowerCamelCase							:	int = 1_0			,       __lowerCamelCase							:	int = 2_5			,       __lowerCamelCase							:	int = 3			,       **__lowerCamelCase							:	int			,       ):
    # time series specific configuration
    UpperCAmelCase__	:Any      =      prediction_length
    UpperCAmelCase__	:Optional[int]      =      context_length if context_length is not None else prediction_length
    UpperCAmelCase__	:int      =      distribution_output
    UpperCAmelCase__	:str      =      loss
    UpperCAmelCase__	:Tuple      =      input_size
    UpperCAmelCase__	:Optional[int]      =      num_time_features
    UpperCAmelCase__	:List[Any]      =      lags_sequence
    UpperCAmelCase__	:Any      =      scaling
    UpperCAmelCase__	:Union[str, Any]      =      num_dynamic_real_features
    UpperCAmelCase__	:Dict      =      num_static_real_features
    UpperCAmelCase__	:Optional[int]      =      num_static_categorical_features
    if cardinality is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:Any      =      cardinality
    else:
       UpperCAmelCase__	:Dict      =      [0]
    if embedding_dimension is not None and num_static_categorical_features > 0:
       if len(__lowerCamelCase ) != num_static_categorical_features:
          raise ValueError(
              '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
       UpperCAmelCase__	:List[str]      =      embedding_dimension
    else:
       UpperCAmelCase__	:int      =      [min(5_0			,       (cat + 1) // 2 ) for cat in self.cardinality]
    UpperCAmelCase__	:Optional[Any]      =      num_parallel_samples

    # Transformer architecture configuration
    UpperCAmelCase__	:Optional[Any]      =      input_size * len(self.lags_sequence ) + self._number_of_features
    UpperCAmelCase__	:Optional[Any]      =      d_model
    UpperCAmelCase__	:Any      =      encoder_attention_heads
    UpperCAmelCase__	:Any      =      decoder_attention_heads
    UpperCAmelCase__	:int      =      encoder_ffn_dim
    UpperCAmelCase__	:Union[str, Any]      =      decoder_ffn_dim
    UpperCAmelCase__	:Optional[Any]      =      encoder_layers
    UpperCAmelCase__	:str      =      decoder_layers

    UpperCAmelCase__	:Optional[Any]      =      dropout
    UpperCAmelCase__	:str      =      attention_dropout
    UpperCAmelCase__	:int      =      activation_dropout
    UpperCAmelCase__	:Optional[Any]      =      encoder_layerdrop
    UpperCAmelCase__	:Dict      =      decoder_layerdrop

    UpperCAmelCase__	:Union[str, Any]      =      activation_function
    UpperCAmelCase__	:List[Any]      =      init_std

    UpperCAmelCase__	:List[str]      =      use_cache

    # Autoformer
    UpperCAmelCase__	:List[str]      =      label_length
    UpperCAmelCase__	:Dict      =      moving_average
    UpperCAmelCase__	:str      =      autocorrelation_factor

    super().__init__(is_encoder_decoder=__lowerCamelCase			,       **__lowerCamelCase )





 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    return (
        sum(self.embedding_dimension )
        + self.num_dynamic_real_features
        + self.num_time_features
        + self.num_static_real_features
        + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
    )



