


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


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







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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



 def 		UpperCamelCase__  (			self      ):
       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=lowerCamelCase__      ,		metadata={"help": "The name of the dataset to use (via the datasets library)."} )
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
 __snake_case				=						field(default=lowerCamelCase__      ,		metadata={"help": "The input training data file (a text file)."} )
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}      ,		)
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}      ,		)
 __snake_case				=						field(
     default=lowerCamelCase__      ,		metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}      ,		)
 __snake_case				=						field(
     default=lowerCamelCase__      ,		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=lowerCamelCase__      ,		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=lowerCamelCase__      ,		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=lowerCamelCase__      ,		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      ):
       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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

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

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






def  __lowerCAmelCase					()->						List[str]:




      """simple docstring"""

      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'''      ,      SCREAMING_SNAKE_CASE      )

      # 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(SCREAMING_SNAKE_CASE      ,      data_files=SCREAMING_SNAKE_CASE      )
      # 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      ,      **SCREAMING_SNAKE_CASE      )
      elif model_args.model_name_or_path:
            snake_case_ =   AutoConfig.from_pretrained(model_args.model_name_or_path      ,      **SCREAMING_SNAKE_CASE      )
      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      ,      **SCREAMING_SNAKE_CASE      )
      elif model_args.model_name_or_path:
            snake_case_ =   AutoTokenizer.from_pretrained(model_args.model_name_or_path      ,      **SCREAMING_SNAKE_CASE      )
      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=SCREAMING_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      ,      )
      else:
            logger.info('''Training new model from scratch'''      )
            snake_case_ =   AutoModelForMaskedLM.from_config(SCREAMING_SNAKE_CASE      )

      model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE      )      )

      # 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(SCREAMING_SNAKE_CASE      ):
            # Remove empty lines
            snake_case_ =   [line for line in examples['''text'''] if len(SCREAMING_SNAKE_CASE      ) > 0 and not line.isspace()]
            return tokenizer(examples['''text''']      ,      padding=SCREAMING_SNAKE_CASE      ,      truncation=SCREAMING_SNAKE_CASE      ,      max_length=data_args.max_seq_length      )

      snake_case_ =   datasets.map(
          SCREAMING_SNAKE_CASE      ,      batched=SCREAMING_SNAKE_CASE      ,      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=SCREAMING_SNAKE_CASE      ,      mlm_probability=data_args.mlm_probability      )

      # Initialize our Trainer
      snake_case_ =   Trainer(
          model=SCREAMING_SNAKE_CASE      ,      args=SCREAMING_SNAKE_CASE      ,      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=SCREAMING_SNAKE_CASE      ,      data_collator=SCREAMING_SNAKE_CASE      ,      )

      # 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=SCREAMING_SNAKE_CASE      )
            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(SCREAMING_SNAKE_CASE      ,      '''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(SCREAMING_SNAKE_CASE      ,      '''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					(SCREAMING_SNAKE_CASE      )->						Union[str, Any]:




      """simple docstring"""

      main()


if __name__ == "__main__":
   main()




from typing import List, Optional, Union

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


UpperCAmelCase    =					logging.get_logger(__name__)

UpperCAmelCase    =					{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"informer"
 __snake_case				=						{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }



 def __init__(			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "student_t"	,   _UpperCAmelCase = "nll"	,   _UpperCAmelCase = 1	,   _UpperCAmelCase = None	,   _UpperCAmelCase = "mean"	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = 0	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 64	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 32	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = 2	,   _UpperCAmelCase = True	,   _UpperCAmelCase = "gelu"	,   _UpperCAmelCase = 0.05	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 0.1	,   _UpperCAmelCase = 1_00	,   _UpperCAmelCase = 0.02	,   _UpperCAmelCase=True	,   _UpperCAmelCase = "prob"	,   _UpperCAmelCase = 5	,   _UpperCAmelCase = True	,   **_UpperCAmelCase	,   ):
       # time series specific configuration
       snake_case_ =   prediction_length
       snake_case_ =   context_length or prediction_length
       snake_case_ =   distribution_output
       snake_case_ =   loss
       snake_case_ =   input_size
       snake_case_ =   num_time_features
       snake_case_ =   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       snake_case_ =   scaling
       snake_case_ =   num_dynamic_real_features
       snake_case_ =   num_static_real_features
       snake_case_ =   num_static_categorical_features

       # set cardinality
       if cardinality and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The cardinality should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   cardinality
       else:
             snake_case_ =   [0]

       # set embedding_dimension
       if embedding_dimension and num_static_categorical_features > 0:
             if len(_UpperCAmelCase      ) != num_static_categorical_features:
                   raise ValueError(
                       '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''      )
             snake_case_ =   embedding_dimension
       else:
             snake_case_ =   [min(50	,   (cat + 1) // 2      ) for cat in self.cardinality]

       snake_case_ =   num_parallel_samples

       # Transformer architecture configuration
       snake_case_ =   input_size * len(self.lags_sequence      ) + self._number_of_features
       snake_case_ =   d_model
       snake_case_ =   encoder_attention_heads
       snake_case_ =   decoder_attention_heads
       snake_case_ =   encoder_ffn_dim
       snake_case_ =   decoder_ffn_dim
       snake_case_ =   encoder_layers
       snake_case_ =   decoder_layers

       snake_case_ =   dropout
       snake_case_ =   attention_dropout
       snake_case_ =   activation_dropout
       snake_case_ =   encoder_layerdrop
       snake_case_ =   decoder_layerdrop

       snake_case_ =   activation_function
       snake_case_ =   init_std

       snake_case_ =   use_cache

       # Informer
       snake_case_ =   attention_type
       snake_case_ =   sampling_factor
       snake_case_ =   distil

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





 @property
 def 		UpperCamelCase__  (			self      ):
       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
       )