





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					:				Union[str, Any]				=			logging.getLogger(__name__)


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    return (preds == labels).mean()



@dataclass
class 					__snake_case	:


    '''simple docstring'''







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





@dataclass
class 					__snake_case	:


    '''simple docstring'''







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





def 							__snake_case							(    )				->						Tuple:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    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''' ,							lowerCAmelCase_	)

    # 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(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.

    SCREAMING_SNAKE_CASE__											=			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 ,							)
    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=lowerCAmelCase_ ,							cache_dir=model_args.cache_dir ,							)

    # Get datasets
    SCREAMING_SNAKE_CASE__											=			(
        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
    )
    SCREAMING_SNAKE_CASE__											=			(
        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_	) -> Dict:
        SCREAMING_SNAKE_CASE__											=			np.argmax(p.predictions ,							axis=1	)
        return {"acc": simple_accuracy(lowerCAmelCase_ ,							p.label_ids	)}

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

    # Initialize our Trainer
    SCREAMING_SNAKE_CASE__											=			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
    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(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 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
  main()



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    KandinskyVaaPipeline
       lowercase_						=    [
           """image_embeds""",
           """negative_image_embeds""",
       ]
       lowercase_						=    ["""image_embeds""", """negative_image_embeds"""]
       lowercase_						=    [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_						=    False

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 3_2

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self.time_input_dim * 4

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return 1_0_0

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )

             __A    ={
                 '''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,
             }

             __A    =UNetaDConditionModel(**lowercase__     )
             return model

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return {
                 "block_out_channels": [3_2, 6_4],
                 "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": 1_2,
                 "out_channels": 3,
                 "up_block_types": [
                     "AttnUpDecoderBlock2D",
                     "UpDecoderBlock2D",
                 ],
                 "vq_embed_dim": 4,
             }

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             torch.manual_seed(0     )
             __A    =VQModel(**self.dummy_movq_kwargs     )
             return model

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.dummy_unet
             __A    =self.dummy_movq

             __A    =DDIMScheduler(
                 num_train_timesteps=1_0_0_0 ,   beta_schedule='''linear''' ,   beta_start=0.0_0085 ,   beta_end=0.012 ,   clip_sample=lowercase__ ,   set_alpha_to_one=lowercase__ ,   steps_offset=1 ,   prediction_type='''epsilon''' ,   thresholding=lowercase__ ,   )

             __A    ={
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''movq''': movq,
             }
             return components

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__=0     ):







             '''simple docstring'''



             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(lowercase__     )     ).to(lowercase__     )
             __A    =floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1     )     ).to(
                 lowercase__     )
             if str(lowercase__     ).startswith('''mps'''     ):
                   __A    =torch.manual_seed(lowercase__     )
             else:
                   __A    =torch.Generator(device=lowercase__     ).manual_seed(lowercase__     )
             __A    ={
                 '''image_embeds''': image_embeds,
                 '''negative_image_embeds''': negative_image_embeds,
                 '''generator''': generator,
                 '''height''': 6_4,
                 '''width''': 6_4,
                 '''guidance_scale''': 4.0,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''np''',
             }
             return inputs


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''cpu'''

             __A    =self.get_dummy_components()

             __A    =self.pipeline_class(**lowercase__     )
             __A    =pipe.to(lowercase__     )

             pipe.set_progress_bar_config(disable=lowercase__     )

             __A    =pipe(**self.get_dummy_inputs(lowercase__     )     )
             __A    =output.images

             __A    =pipe(
                 **self.get_dummy_inputs(lowercase__     ) ,   return_dict=lowercase__ ,   )[0]

             __A    =image[0, -3:, -3:, -1]
             __A    =image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             __A    =np.array(
                 [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]     )

             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        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''     )

             __A    =KandinskyVaaPriorPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-prior''' ,   torch_dtype=torch.floataa     )
             pipe_prior.to(lowercase__     )

             __A    =KandinskyVaaPipeline.from_pretrained(
                 '''kandinsky-community/kandinsky-2-2-decoder''' ,   torch_dtype=torch.floataa     )
             __A    =pipeline.to(lowercase__     )
             pipeline.set_progress_bar_config(disable=lowercase__     )

             __A    ='''red cat, 4k photo'''

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    ,		__A    =pipe_prior(
                 lowercase__ ,   generator=lowercase__ ,   num_inference_steps=5 ,   negative_prompt='''''' ,   ).to_tuple()

             __A    =torch.Generator(device='''cuda'''     ).manual_seed(0     )
             __A    =pipeline(
                 image_embeds=lowercase__ ,   negative_image_embeds=lowercase__ ,   generator=lowercase__ ,   num_inference_steps=1_0_0 ,   output_type='''np''' ,   )

             __A    =output.images[0]

             assert image.shape == (5_1_2, 5_1_2, 3)

             assert_mean_pixel_difference(lowercase__ ,   lowercase__     )


