


'''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   :   int												=   logging.getLogger(__name__)
_a   :   Optional[Any]												=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_a   :   Dict												=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowercase							)}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					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"
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}						,					)
   _SCREAMING_SNAKE_CASE  :  str          =	field(
       default="main"						,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }						,					)
   def a  (						self       :      List[Any]      ) ->							Tuple:
        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 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(default=__lowercase						,					metadata={"help": "The input training data file (a text file)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Overwrite the cached training and evaluation sets"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=5						,					metadata={
           "help": "The percentage of the train set used as validation set in case there's no validation split"
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					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."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					metadata={"help": "The number of processes to use for the preprocessing."}						,					)
   _SCREAMING_SNAKE_CASE  :  float          =	field(
       default=0.15						,					metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}							)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					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 a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        if self.train_file is not None:
             __snake_case           =						self.train_file.split('.'      )[-1]
             assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
        if self.validation_file is not None:
             __snake_case           =						self.validation_file.split('.'      )[-1]
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def   _a		(lowercase__			:   Dict							,   lowercase__			:   Dict						)			->			int:






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

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





def   _a		()			->			Tuple:






     """simple docstring"""
     # 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'							,   lowercase__						)

     # 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(lowercase__							,   data_files=lowercase__						)
     # 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							,   **lowercase__						)
     elif model_args.model_name_or_path:
          __snake_case           =						AutoConfig.from_pretrained(model_args.model_name_or_path							,   **lowercase__						)
     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							,   **lowercase__						)
     elif model_args.model_name_or_path:
          __snake_case           =						AutoTokenizer.from_pretrained(model_args.model_name_or_path							,   **lowercase__						)
     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=lowercase__							,   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(lowercase__						)

     model.resize_token_embeddings(len(lowercase__						)						)

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

     __snake_case           =						datasets.map(
         lowercase__							,   batched=lowercase__							,   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=lowercase__							,   mlm_probability=data_args.mlm_probability						)

     # Initialize our Trainer
     __snake_case           =						Trainer(
         model=lowercase__							,   args=lowercase__							,   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=lowercase__							,   data_collator=lowercase__							,   )

     # 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=lowercase__						)
          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(lowercase__							,   '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(lowercase__							,   '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		(lowercase__			:   int						)			->			Any:






     """simple docstring"""
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
       main()











'''simple docstring'''

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      List[Any]						      ={
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_vision_model"""
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=14_08		,			lowercase_ :int=61_44		,			lowercase_ :List[str]=39		,			lowercase_ :List[Any]=16		,			lowercase_ :Union[str, Any]=2_24		,			lowercase_ :List[str]=14		,			lowercase_ :Dict="gelu"		,			lowercase_ :Tuple=0.0_0_0_0_1		,			lowercase_ :Dict=0.0		,			lowercase_ :Optional[int]=1E-10		,			lowercase_ :Optional[Any]=True		,			**lowercase_ :List[str]		,			)->				int:
     super().__init__(**lowercase_				)

     A__							=     hidden_size
     A__							=     intermediate_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     patch_size
     A__							=     image_size
     A__							=     initializer_range
     A__							=     attention_dropout
     A__							=     layer_norm_eps
     A__							=     hidden_act
     A__							=     qkv_bias



 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :Optional[Any]				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the vision config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["vision_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_qformer"""
 def __init__(							self :Dict		,			lowercase_ :int=3_05_22		,			lowercase_ :Union[str, Any]=7_68		,			lowercase_ :Tuple=12		,			lowercase_ :Union[str, Any]=12		,			lowercase_ :str=30_72		,			lowercase_ :Any="gelu"		,			lowercase_ :Optional[int]=0.1		,			lowercase_ :List[str]=0.1		,			lowercase_ :Dict=5_12		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :Dict=1E-12		,			lowercase_ :List[Any]=0		,			lowercase_ :str="absolute"		,			lowercase_ :List[str]=2		,			lowercase_ :Tuple=14_08		,			**lowercase_ :Tuple		,			)->				List[Any]:
     super().__init__(pad_token_id=lowercase_		,			**lowercase_				)

     A__							=     vocab_size
     A__							=     hidden_size
     A__							=     num_hidden_layers
     A__							=     num_attention_heads
     A__							=     hidden_act
     A__							=     intermediate_size
     A__							=     hidden_dropout_prob
     A__							=     attention_probs_dropout_prob
     A__							=     max_position_embeddings
     A__							=     initializer_range
     A__							=     layer_norm_eps
     A__							=     position_embedding_type
     A__							=     cross_attention_frequency
     A__							=     encoder_hidden_size



 @classmethod
 def 	UpperCAmelCase_				(							cls :int		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :str				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the qformer config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     config_dict["qformer_config"]

     if "model_type" in config_dict and hasattr(cls		,			"model_type"				) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."				)

     return cls.from_dict(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip-2"""
 __lowercase  						=							True
 def __init__(							self :int		,			lowercase_ :List[Any]=None		,			lowercase_ :Dict=None		,			lowercase_ :Tuple=None		,			lowercase_ :List[str]=32		,			**lowercase_ :Any				)->				Union[str, Any]:
     super().__init__(**lowercase_				)

     if vision_config is None:
         A__							=     {}
         logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."				)

     if qformer_config is None:
         A__							=     {}
         logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."				)

     if text_config is None:
         A__							=     {}
         logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."				)

     A__							=     BlipaVisionConfig(**lowercase_				)
     A__							=     BlipaQFormerConfig(**lowercase_				)
     A__							=     text_config["model_type"] if "model_type" in text_config else "opt"
     A__							=     CONFIG_MAPPING[text_model_type](**lowercase_				)

     A__							=     self.text_config.tie_word_embeddings
     A__							=     self.text_config.is_encoder_decoder

     A__							=     num_query_tokens
     A__							=     self.vision_config.hidden_size
     A__							=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     A__							=     1.0
     A__							=     0.0_2
 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :BlipaVisionConfig		,			lowercase_ :BlipaQFormerConfig		,			lowercase_ :PretrainedConfig		,			**lowercase_ :Any		,			)->				Optional[int]:
     return cls(
         vision_config=vision_config.to_dict()		,			qformer_config=qformer_config.to_dict()		,			text_config=text_config.to_dict()		,			**lowercase_		,			)



 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.vision_config.to_dict()
     A__							=     self.qformer_config.to_dict()
     A__							=     self.text_config.to_dict()
     A__							=     self.__class__.model_type
     return output



