





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


SCREAMING_SNAKE_CASE_		     = logging.getLogger(__name__)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     Tuple:


      '''simple docstring'''


      return (preds == labels).mean()

@dataclass
class 	UpperCamelCase__				:



     '''simple docstring'''

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

@dataclass
class 	UpperCamelCase__				:



     '''simple docstring'''

     __snake_case					:			str				=    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()     )}     )
     __snake_case					:			str				=    field(metadata={"help": "Should contain the data files for the task."}     )
     __snake_case					:			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."
             )
         }							,   )
     __snake_case					:			bool				=    field(
         default=lowerCAmelCase_							,   metadata={"help": "Overwrite the cached training and evaluation sets"}     )



def      __lowercase     (   )				->     Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		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"""				,		_SCREAMING_SNAKE_CASE			)

      # Set seed
      set_seed(training_args.seed			)

      try:
            SCREAMING_SNAKE_CASE	=		processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE	=		processor.get_labels()
            SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)
      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	=		AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,		num_labels=_SCREAMING_SNAKE_CASE				,		finetuning_task=data_args.task_name				,		cache_dir=model_args.cache_dir				,		)
      SCREAMING_SNAKE_CASE	=		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	=		AutoModelForMultipleChoice.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				,		)

      # Get datasets
      SCREAMING_SNAKE_CASE	=		(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir				,		tokenizer=_SCREAMING_SNAKE_CASE				,		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	=		(
          MultipleChoiceDataset(
              data_dir=data_args.data_dir				,		tokenizer=_SCREAMING_SNAKE_CASE				,		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(_SCREAMING_SNAKE_CASE			) -> Dict:
            SCREAMING_SNAKE_CASE	=		np.argmax(p.predictions				,		axis=1			)
            return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE				,		p.label_ids			)}

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

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE	=		Trainer(
          model=_SCREAMING_SNAKE_CASE				,		args=_SCREAMING_SNAKE_CASE				,		train_dataset=_SCREAMING_SNAKE_CASE				,		eval_dataset=_SCREAMING_SNAKE_CASE				,		compute_metrics=_SCREAMING_SNAKE_CASE				,		data_collator=_SCREAMING_SNAKE_CASE				,		)

      # 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	=		{}
      if training_args.do_eval:
            logger.info("""*** Evaluate ***"""			)

            SCREAMING_SNAKE_CASE	=		trainer.evaluate()

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

                        results.update(_SCREAMING_SNAKE_CASE			)

      return results



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     List[Any]:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()









import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			str				=    VQModel
     __snake_case					:			Optional[Any]				=    "sample"






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Optional[Any]=(32, 32) )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		4
           SCREAMING_SNAKE_CASE	=		3

           SCREAMING_SNAKE_CASE	=		floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase__ )

           return {"sample": image}






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           return (3, 32, 32)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		{
               """block_out_channels""": [32, 64],
               """in_channels""": 3,
               """out_channels""": 3,
               """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
               """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
               """latent_channels""": 3,
           }
           SCREAMING_SNAKE_CASE	=		self.dummy_input
           return init_dict, inputs_dict






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	str:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[str]:




           '''simple docstring'''


           pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,output_loading_info=lowerCamelCase__ )
           self.assertIsNotNone(lowerCamelCase__ )
           self.assertEqual(len(loading_info["""missing_keys"""] )		,0 )

           model.to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(**self.dummy_input )

           assert image is not None, "Make sure output is not None"






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		VQModel.from_pretrained("""fusing/vqgan-dummy""" )
           model.to(lowerCamelCase__ ).eval()

           torch.manual_seed(0 )
           if torch.cuda.is_available():
                 torch.cuda.manual_seed_all(0 )

           SCREAMING_SNAKE_CASE	=		torch.randn(1		,model.config.in_channels		,model.config.sample_size		,model.config.sample_size )
           SCREAMING_SNAKE_CASE	=		image.to(lowerCamelCase__ )
           with torch.no_grad():
                 SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ ).sample

           SCREAMING_SNAKE_CASE	=		output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           SCREAMING_SNAKE_CASE	=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
           # fmt: on
           self.assertTrue(torch.allclose(lowerCamelCase__		,lowerCamelCase__		,atol=1e-3 ) )


