






'''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


a_					= logging.getLogger(__name__)





def  _a(  UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						List[Any]							):




      '''simple docstring'''






      return (preds == labels).mean()






@dataclass
class      __SCREAMING_SNAKE_CASE      :
 snake_case_  							=							field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
 snake_case_  							=							field(
     default=lowerCamelCase	,				metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
 snake_case_  							=							field(
     default=lowerCamelCase	,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
 snake_case_  							=							field(
     default=lowerCamelCase	,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,				)






@dataclass
class      __SCREAMING_SNAKE_CASE      :
 snake_case_  							=							field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()				)}				)
 snake_case_  							=							field(metadata={"""help""": """Should contain the data files for the task."""}				)
 snake_case_  							=							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_  							=							field(
     default=lowerCamelCase	,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Tuple									=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=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__     :				List[Any]									=processors[data_args.task_name]()
            SCREAMING_SNAKE_CASE__     :				int									=processor.get_labels()
            SCREAMING_SNAKE_CASE__     :				Tuple									=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__     :				Optional[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,	)
      SCREAMING_SNAKE_CASE__     :				Tuple									=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__     :				Tuple									=(
          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__     :				Union[str, Any]									=(
          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__     :				Optional[int]									=np.argmax(p.predictions,	axis=1							)
            return {"acc": simple_accuracy(UpperCamelCase__,	p.label_ids							)}

      # Data collator
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DataCollatorWithPadding(UpperCamelCase__,	pad_to_multiple_of=8							) if training_args.fpaa else None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE__     :				Dict									=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__     :				Dict									={}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''							)

            SCREAMING_SNAKE_CASE__     :				List[str]									=trainer.evaluate()

            SCREAMING_SNAKE_CASE__     :				str									=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  _a(  UpperCamelCase__				:						Optional[int]							):




      '''simple docstring'''






      main()


if __name__ == "__main__":
  main()







'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyVaaImgaImgPipeline
 snake_case_  							=							["""image_embeds""", """negative_image_embeds""", """image"""]
 snake_case_  							=							[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						str:
       return 32




 @property
 def      __magic_name__	(      self	:							Any      )	->						Optional[int]:
       return self.time_input_dim




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=UNetaDConditionModel(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Any:
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=VQModel(**self.dummy_movq_kwargs      )
       return model




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 10_00,
           '''beta_schedule''': '''linear''',
           '''beta_start''': 0.00085,
           '''beta_end''': 0.012,
           '''clip_sample''': False,
           '''set_alpha_to_one''': False,
           '''steps_offset''': 0,
           '''prediction_type''': '''epsilon''',
           '''thresholding''': False,
       }

       SCREAMING_SNAKE_CASE__     :				str									=DDIMScheduler(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any=0      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(seed + 1      )      ).to(
           __lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									={
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipe(
           **self.get_dummy_inputs(__lowercase      )					,      return_dict=__lowercase					,      )[0]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__     :				Tuple									=np.array(
           [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]      )
       assert (
           np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






@slow
@require_torch_gpu
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							int      )	->						Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,      torch_dtype=torch.floataa      )
       pipe_prior.to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline.to(__lowercase      )

       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      strength=0.2					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				int									=output.images[0]

       assert image.shape == (7_68, 7_68, 3)

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )