



"""simple docstring"""

import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase__					:						List[Any]												=						logging.getLogger(__name__)




def __lowercase    (							_a	,    _a					):
    return (preds == labels).mean()




@dataclass
class  _UpperCAmelCase :
 _lowerCAmelCase     :    Union[str, Any]	      =	field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
 _lowerCAmelCase     :    Union[str, Any]	      =	field(
     default=__UpperCAmelCase					,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
 _lowerCAmelCase     :    Union[str, Any]	      =	field(
     default=__UpperCAmelCase					,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
 _lowerCAmelCase     :    Optional[Any]	      =	field(
     default=__UpperCAmelCase					,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}					,  )





@dataclass
class  _UpperCAmelCase :
 _lowerCAmelCase     :    str	      =	field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys())})
 _lowerCAmelCase     :    Dict	      =	field(metadata={"""help""": """Should contain the data files for the task."""})
 _lowerCAmelCase     :    str	      =	field(
     default=1_2_8					,  metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated, sequences shorter will be padded."""
         )
     }					,  )
 _lowerCAmelCase     :    Any	      =	field(
     default=__UpperCAmelCase					,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""})





def __lowercase    (							):
    snake_case_				:  Optional[Any]       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
    snake_case_,			snake_case_,			snake_case_				:  Union[str, Any]       =			parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir					)
        and os.listdir(training_args.output_dir					)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            ''' --overwrite_output_dir to overcome.'''					)

    # Setup logging
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,    datefmt='''%m/%d/%Y %H:%M:%S'''	,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,    )
    logger.warning(
        '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''	,    training_args.local_rank	,    training_args.device	,    training_args.n_gpu	,    bool(training_args.local_rank != -1					)	,    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
    set_seed(training_args.seed					)

    try:
        snake_case_				:  Union[str, Any]       =			processors[data_args.task_name]()
        snake_case_				:  Tuple       =			processor.get_labels()
        snake_case_				:  Optional[int]       =			len(__lowerCAmelCase					)
    except KeyError:
        raise ValueError('''Task not found: %s''' % (data_args.task_name)					)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case_				:  int       =			AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    num_labels=__lowerCAmelCase	,    finetuning_task=data_args.task_name	,    cache_dir=model_args.cache_dir	,    )
    snake_case_				:  List[str]       =			AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )
    snake_case_				:  Optional[int]       =			AutoModelForMultipleChoice.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	,    )

    # Get datasets
    snake_case_				:  Dict       =			(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir	,    tokenizer=__lowerCAmelCase	,    task=data_args.task_name	,    max_seq_length=data_args.max_seq_length	,    overwrite_cache=data_args.overwrite_cache	,    mode=Split.train	,    )
        if training_args.do_train
        else None
    )
    snake_case_				:  List[str]       =			(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir	,    tokenizer=__lowerCAmelCase	,    task=data_args.task_name	,    max_seq_length=data_args.max_seq_length	,    overwrite_cache=data_args.overwrite_cache	,    mode=Split.dev	,    )
        if training_args.do_eval
        else None
    )

    def compute_metrics(_a					) -> Dict:
        snake_case_				:  List[str]       =			np.argmax(p.predictions	,    axis=1					)
        return {"acc": simple_accuracy(__lowerCAmelCase	,    p.label_ids					)}

    # Data collator
    snake_case_				:  str       =			DataCollatorWithPadding(__lowerCAmelCase	,    pad_to_multiple_of=8					) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case_				:  int       =			Trainer(
        model=__lowerCAmelCase	,    args=__lowerCAmelCase	,    train_dataset=__lowerCAmelCase	,    eval_dataset=__lowerCAmelCase	,    compute_metrics=__lowerCAmelCase	,    data_collator=__lowerCAmelCase	,    )

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir					)

        # Evaluation
    snake_case_				:  int       =			{}
    if training_args.do_eval:
        logger.info('''*** Evaluate ***'''					)

        snake_case_				:  int       =			trainer.evaluate()

        snake_case_				:  Optional[int]       =			os.path.join(training_args.output_dir	,    '''eval_results.txt'''					)
        if trainer.is_world_master():
            with open(__lowerCAmelCase	,    '''w'''					) as writer:
                logger.info('''***** Eval results *****'''					)
                for key, value in result.items():
                    logger.info('''  %s = %s'''	,    __lowerCAmelCase	,    __lowerCAmelCase					)
                    writer.write('''%s = %s\n''' % (key, value)					)

                results.update(__lowerCAmelCase					)

    return results




def __lowercase    (							_a					):
    main()


if __name__ == "__main__":
     main()










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





