




'''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


UpperCamelCase_    	=				logging.getLogger(__name__)




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      return (preds == labels).mean()






@dataclass
class 							_a				:





       '''simple docstring'''






       A						:				str        =						field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
       A						:				Optional[str]        =						field(
           default=SCREAMING_SNAKE_CASE ,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
       A						:				Optional[str]        =						field(
           default=SCREAMING_SNAKE_CASE ,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
       A						:				Optional[str]        =						field(
           default=SCREAMING_SNAKE_CASE ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,      )






@dataclass
class 							_a				:





       '''simple docstring'''






       A						:				str        =						field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys() )} )
       A						:				str        =						field(metadata={'''help''': '''Should contain the data files for the task.'''} )
       A						:				int        =						field(
           default=128 ,      metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           } ,      )
       A						:				bool        =						field(
           default=SCREAMING_SNAKE_CASE ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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'      ,__UpperCamelCase    )

      # Set seed
      set_seed(training_args.seed    )

      try:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						processor.get_labels()
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						len(__UpperCamelCase    )
      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.

      SCREAMING_SNAKE_CASE   :      Any    					=						AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,num_labels=__UpperCamelCase      ,finetuning_task=data_args.task_name      ,cache_dir=model_args.cache_dir      ,)
      SCREAMING_SNAKE_CASE   :      Dict    					=						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      ,)
      SCREAMING_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=__UpperCamelCase      ,cache_dir=model_args.cache_dir      ,)

      # Get datasets
      SCREAMING_SNAKE_CASE   :      List[str]    					=						(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir      ,tokenizer=__UpperCamelCase      ,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
      )
      SCREAMING_SNAKE_CASE   :      str    					=						(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir      ,tokenizer=__UpperCamelCase      ,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(__UpperCamelCase:					EvalPrediction    ) -> Dict:
            SCREAMING_SNAKE_CASE   :      Any    					=						np.argmax(p.predictions      ,axis=1    )
            return {"acc": simple_accuracy(__UpperCamelCase      ,p.label_ids    )}

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

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						Trainer(
          model=__UpperCamelCase      ,args=__UpperCamelCase      ,train_dataset=__UpperCamelCase      ,eval_dataset=__UpperCamelCase      ,compute_metrics=__UpperCamelCase      ,data_collator=__UpperCamelCase      ,)

      # 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
      SCREAMING_SNAKE_CASE   :      List[str]    					=						{}
      if training_args.do_eval:
            logger.info('*** Evaluate ***'    )

            SCREAMING_SNAKE_CASE   :      Any    					=						trainer.evaluate()

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

                        results.update(__UpperCamelCase    )

      return results




def   lowercase__(			__UpperCamelCase:					Tuple    ):

      """simple docstring"""






      main()


if __name__ == "__main__":
      main()








'''simple docstring'''



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             super().__init__()
             SCREAMING_SNAKE_CASE   :      Dict    					=						nn.ModuleList(A  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A,							A,							A = None,							A = None,							A = None,							A = None,							A = False,							A = True,							):







             '''simple docstring'''







             for i, (image, scale, controlnet) in enumerate(zip(A,							A,							self.nets  )  ):
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						controlnet(
                       A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							A,							)

                   # merge samples
                   if i == 0:
                         SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						down_samples, mid_sample
                   else:
                         SCREAMING_SNAKE_CASE   :      str    					=						[
                             samples_prev + samples_curr
                             for samples_prev, samples_curr in zip(A,							A  )
                         ]
                         mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample







       def        UpperCamelCase_							(      self,							A,							A = True,							A = None,							A = False,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						save_directory
             for controlnet in self.nets:
                   controlnet.save_pretrained(
                       A,							is_main_process=A,							save_function=A,							safe_serialization=A,							variant=A,							)

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_path_to_save + F"_{idx}"







       @classmethod
       def        UpperCamelCase_							(      cls,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						0
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pretrained_model_path
             while os.path.isdir(A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ControlNetModel.from_pretrained(A,							**A  )
                   controlnets.append(A  )

                   idx += 1
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pretrained_model_path + F"_{idx}"

             logger.info(F"{len(A  )} controlnets loaded from {pretrained_model_path}."  )

             if len(A  ) == 0:
                   raise ValueError(
                       F"No ControlNets found under {os.path.dirname(A  )}. Expected at least {pretrained_model_path + '_0'}."  )

             return cls(A  )


