


'''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


lowerCAmelCase				:str        =      logging.getLogger(__name__)





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	return (preds == labels).mean()
@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







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


@dataclass
class _lowerCamelCase	:



		'''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=1_28		,					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=lowercase__		,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)





def lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
	__magic_name__	:   Dict					     =					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:
		__magic_name__	:   Union[str, Any]					     =					processors[data_args.task_name]()
		__magic_name__	:   Optional[Any]					     =					processor.get_labels()
		__magic_name__	:   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.

	__magic_name__	:   Optional[Any]					     =					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		,		)
	__magic_name__	:   Union[str, Any]					     =					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		,		)
	__magic_name__	:   List[Any]					     =					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
	__magic_name__	:   List[Any]					     =					(
	    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
	)
	__magic_name__	:   Tuple					     =					(
	    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(lowerCAmelCase    :     EvalPrediction		) -> Dict:
		__magic_name__	:   str					     =					np.argmax(p.predictions		,		axis=1		)
		return {"acc": simple_accuracy(lowerCAmelCase		,		p.label_ids		)}

	# Data collator
	__magic_name__	:   List[Any]					     =					DataCollatorWithPadding(lowerCAmelCase		,		pad_to_multiple_of=8		) if training_args.fpaa else None

	# Initialize our Trainer
	__magic_name__	:   Any					     =					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
	__magic_name__	:   str					     =					{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'		)

		__magic_name__	:   int					     =					trainer.evaluate()

		__magic_name__	:   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 lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	main()


if __name__ == "__main__":
							main()



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					UNetaDModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,					)
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Any:
			__magic_name__	:   Union[str, Any]					     =					self.dummy_uncond_unet
			__magic_name__	:   str					     =					KarrasVeScheduler()

			__magic_name__	:   List[Any]					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					torch.manual_seed(0      )
			__magic_name__	:   int					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   str					     =					pipe(num_inference_steps=2  ,					generator=_A  ,					output_type='numpy'  ,					return_dict=_A      )[0]

			__magic_name__	:   int					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2







@slow
@require_torch
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					'google/ncsnpp-celebahq-256'
			__magic_name__	:   List[str]					     =					UNetaDModel.from_pretrained(_A      )
			__magic_name__	:   int					     =					KarrasVeScheduler()

			__magic_name__	:   str					     =					KarrasVePipeline(unet=_A  ,					scheduler=_A      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe(num_inference_steps=20  ,					generator=_A  ,					output_type='numpy'      ).images

			__magic_name__	:   List[str]					     =					image[0, -3:, -3:, -1]
			assert image.shape == (1, 256, 256, 3)
			__magic_name__	:   int					     =					np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2