


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__							:						Dict							     =   logging.getLogger(__name__)
lowerCamelCase__							:						Union[str, Any]							     =   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCamelCase__							:						Any							     =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={
             'help': (
                 'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
             )
         }   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(snake_case_				)}   ,)
     __lowercase				:							Optional[str]	   =       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'
             )
         }   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,)
     __lowercase				:							bool	   =       field(
         default=snake_case_   ,metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}   ,)
     __lowercase				:							str	   =       field(
         default='main'   ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}   ,)
     __lowercase				:							bool	   =       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:str   ):
       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__						:





     '''simple docstring'''



     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'The name of the dataset to use (via the datasets library).'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}				)
     __lowercase				:							Optional[str]	   =       field(default=snake_case_   ,metadata={'help': 'The input training data file (a text file).'}				)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}   ,)
     __lowercase				:							Optional[str]	   =       field(
         default=snake_case_   ,metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}   ,)
     __lowercase				:							bool	   =       field(
         default=snake_case_   ,metadata={'help': 'Overwrite the cached training and evaluation sets'}				)
     __lowercase				:							Optional[int]	   =       field(
         default=5   ,metadata={
             'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
         }   ,)
     __lowercase				:							Optional[int]	   =       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.'
             )
         }   ,)
     __lowercase				:							Optional[int]	   =       field(
         default=snake_case_   ,metadata={'help': 'The number of processes to use for the preprocessing.'}   ,)
     __lowercase				:							float	   =       field(
         default=0.15   ,metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}				)
     __lowercase				:							bool	   =       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:str   ):
       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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  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 SCREAMING_SNAKE_CASE	(			)    ->				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				):
    # 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
   main()







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

__a							     =					logging.get_logger(__name__)

__a							     =					{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''imagegpt'''
    lowerCAmelCase         = ['''past_key_values''']
    lowerCAmelCase         = {
        '''hidden_size''': '''n_embd''',
        '''max_position_embeddings''': '''n_positions''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }





    def __init__( self  ,_SCREAMING_SNAKE_CASE=512 + 1  ,_SCREAMING_SNAKE_CASE=32 * 32  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=24  ,_SCREAMING_SNAKE_CASE=8  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="quick_gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=1e-5  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[int]:
         UpperCAmelCase_     :				Optional[int]  =     vocab_size
         UpperCAmelCase_     :				Union[str, Any]  =     n_positions
         UpperCAmelCase_     :				Union[str, Any]  =     n_embd
         UpperCAmelCase_     :				Any  =     n_layer
         UpperCAmelCase_     :				Optional[Any]  =     n_head
         UpperCAmelCase_     :				Union[str, Any]  =     n_inner
         UpperCAmelCase_     :				List[Any]  =     activation_function
         UpperCAmelCase_     :				List[str]  =     resid_pdrop
         UpperCAmelCase_     :				str  =     embd_pdrop
         UpperCAmelCase_     :				Optional[Any]  =     attn_pdrop
         UpperCAmelCase_     :				Dict  =     layer_norm_epsilon
         UpperCAmelCase_     :				Union[str, Any]  =     initializer_range
         UpperCAmelCase_     :				Dict  =     scale_attn_weights
         UpperCAmelCase_     :				Any  =     use_cache
         UpperCAmelCase_     :				List[str]  =     scale_attn_by_inverse_layer_idx
         UpperCAmelCase_     :				Tuple  =     reorder_and_upcast_attn
         UpperCAmelCase_     :				int  =     tie_word_embeddings

         super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    @property
    def 				a__ ( self    )					->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ]    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = 1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 3  ,_SCREAMING_SNAKE_CASE = 32  ,_SCREAMING_SNAKE_CASE = 32  ,)					->						Mapping[str, Any]:
         UpperCAmelCase_     :				Any  =     self._generate_dummy_images(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     dict(preprocessor(images=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )

         return inputs