





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   KandinskyImgaImgPipeline
   _UpperCamelCase       		=   ["prompt", "image_embeds", "negative_image_embeds", "image"]
   _UpperCamelCase       		=   [
       "prompt",
       "negative_prompt",
       "image_embeds",
       "negative_image_embeds",
       "image",
   ]
   _UpperCamelCase       		=   [
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   _UpperCamelCase       		=   False


   @property
   def 					snake_case				( self					):
       return 32


   @property
   def 					snake_case				( self					):
       return 32


   @property
   def 					snake_case				( self					):
       return self.time_input_dim


   @property
   def 					snake_case				( self					):
       return self.time_input_dim * 4


   @property
   def 					snake_case				( self					):
       return 100


   @property
   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
       return tokenizer


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)
       A       :       Dict							=						MCLIPConfig(
           numDims=self.cross_attention_dim    ,   transformerDimensions=self.text_embedder_hidden_size    ,   hidden_size=self.text_embedder_hidden_size    ,   intermediate_size=37    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   vocab_size=1_005    ,   )

       A       :       int							=						MultilingualCLIP(UpperCamelCase__					)
       A       :       List[Any]							=						text_encoder.eval()

       return text_encoder


   @property
   def 					snake_case				( self					):
       torch.manual_seed(0					)

       A       :       Union[str, Any]							=						{
           '''in_channels''': 4,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''text_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''': '''text_image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       A       :       str							=						UNetaDConditionModel(**UpperCamelCase__					)
       return model


   @property
   def 					snake_case				( self					):
       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 					snake_case				( self					):
       torch.manual_seed(0					)
       A       :       List[str]							=						VQModel(**self.dummy_movq_kwargs					)
       return model


   def 					snake_case				( self					):
       A       :       Tuple							=						self.dummy_text_encoder
       A       :       Union[str, Any]							=						self.dummy_tokenizer
       A       :       Dict							=						self.dummy_unet
       A       :       str							=						self.dummy_movq

       A       :       Optional[Any]							=						{
           '''num_train_timesteps''': 1_000,
           '''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,
       }

       A       :       Optional[int]							=						DDIMScheduler(**UpperCamelCase__					)

       A       :       Tuple							=						{
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=0					):
       A       :       int							=						floats_tensor((1, self.cross_attention_dim)    ,   rng=random.Random(UpperCamelCase__					)					).to(UpperCamelCase__					)
       A       :       Union[str, Any]							=						floats_tensor((1, self.cross_attention_dim)    ,   rng=random.Random(seed + 1					)					).to(UpperCamelCase__					)
       # create init_image
       A       :       Dict							=						floats_tensor((1, 3, 64, 64)    ,   rng=random.Random(UpperCamelCase__					)					).to(UpperCamelCase__					)
       A       :       Any							=						image.cpu().permute(0    ,   2    ,   3    ,   1					)[0]
       A       :       Dict							=						Image.fromarray(np.uinta(UpperCamelCase__					)					).convert('''RGB'''					).resize((256, 256)					)

       if str(UpperCamelCase__					).startswith('''mps'''					):
           A       :       Dict							=						torch.manual_seed(UpperCamelCase__					)
       else:
           A       :       int							=						torch.Generator(device=UpperCamelCase__					).manual_seed(UpperCamelCase__					)
       A       :       List[str]							=						{
           '''prompt''': '''horse''',
           '''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 					snake_case				( self					):
       A       :       Any							=						'''cpu'''

       A       :       Tuple							=						self.get_dummy_components()

       A       :       Dict							=						self.pipeline_class(**UpperCamelCase__					)
       A       :       Optional[int]							=						pipe.to(UpperCamelCase__					)

       pipe.set_progress_bar_config(disable=UpperCamelCase__					)

       A       :       List[str]							=						pipe(**self.get_dummy_inputs(UpperCamelCase__					)					)
       A       :       Optional[int]							=						output.images

       A       :       Any							=						pipe(
           **self.get_dummy_inputs(UpperCamelCase__					)    ,   return_dict=UpperCamelCase__    ,   )[0]

       A       :       str							=						image[0, -3:, -3:, -1]
       A       :       Optional[Any]							=						image_from_tuple[0, -3:, -3:, -1]

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

       A       :       List[str]							=						np.array(
           [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]					)
       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   _lowercase						(							unittest.TestCase				):


   def 					snake_case				( self					):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def 					snake_case				( self					):
       A       :       int							=						load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinsky/kandinsky_img2img_frog.npy'''					)

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

       A       :       Optional[int]							=						KandinskyPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1-prior'''    ,   torch_dtype=torch.floataa					)
       pipe_prior.to(UpperCamelCase__					)

       A       :       Dict							=						KandinskyImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-1'''    ,   torch_dtype=torch.floataa					)
       A       :       Dict							=						pipeline.to(UpperCamelCase__					)

       pipeline.set_progress_bar_config(disable=UpperCamelCase__					)

       A       :       Any							=						torch.Generator(device='''cpu'''					).manual_seed(0					)
       A,	A       :       Optional[int]							=						pipe_prior(
           UpperCamelCase__    ,   generator=UpperCamelCase__    ,   num_inference_steps=5    ,   negative_prompt=''''''    ,   ).to_tuple()

       A       :       Optional[int]							=						pipeline(
           UpperCamelCase__    ,   image=UpperCamelCase__    ,   image_embeds=UpperCamelCase__    ,   negative_image_embeds=UpperCamelCase__    ,   generator=UpperCamelCase__    ,   num_inference_steps=100    ,   height=768    ,   width=768    ,   strength=0.2    ,   output_type='''np'''    ,   )

       A       :       List[Any]							=						output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(UpperCamelCase__    ,   UpperCamelCase__					)







'''simple docstring'''






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator
def 							_lowerCamelCase(  UpperCamelCase__    : Dict		)		->  Union[str, Any]:
    A       :       Optional[Any]							=						fname.split(os.path.sep		)[-1]
    return re.search(R'''^(.*)_\d+\.jpg$'''    ,       UpperCamelCase__		).groups()[0]


class   _lowercase						(							a				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       A       :       str							=						file_names
       A       :       Optional[int]							=						image_transform
       A       :       str							=						label_to_id


   def __len__( self					):
       return len(self.file_names					)


   def __getitem__( self    ,   _UpperCAmelCase					):
       A       :       int							=						self.file_names[idx]
       A       :       int							=						PIL.Image.open(_UpperCAmelCase					)
       A       :       str							=						raw_image.convert('''RGB'''					)
       if self.image_transform is not None:
           A       :       Dict							=						self.image_transform(_UpperCAmelCase					)
       A       :       Tuple							=						extract_label(_UpperCAmelCase					)
       if self.label_to_id is not None:
           A       :       Optional[Any]							=						self.label_to_id[label]
       return {"image": image, "label": label}







def 							_lowerCamelCase(  UpperCamelCase__    : List[Any]    ,       UpperCamelCase__    : int		)		->  Any:
    # Initialize accelerator
    if args.with_tracking:
        A       :       Union[str, Any]							=						Accelerator(
            cpu=args.cpu    ,       mixed_precision=args.mixed_precision    ,       log_with='''all'''    ,       project_dir=args.project_dir		)
    else:
        A       :       Dict							=						Accelerator(cpu=args.cpu    ,       mixed_precision=args.mixed_precision		)

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A       :       List[str]							=						config['''lr''']
    A       :       int							=						int(config['''num_epochs''']		)
    A       :       List[str]							=						int(config['''seed''']		)
    A       :       Any							=						int(config['''batch_size''']		)
    A       :       List[str]							=						config['''image_size''']
    if not isinstance(UpperCamelCase__    ,       (list, tuple)		):
        A       :       List[str]							=						(image_size, image_size)

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps    ,       '''isdigit'''		):
        if args.checkpointing_steps == "epoch":
            A       :       List[Any]							=						args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            A       :       Optional[Any]							=						int(args.checkpointing_steps		)
        else:
            raise ValueError(
                F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''		)
    else:
        A       :       Optional[Any]							=						None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        A       :       Any							=						os.path.split(UpperCamelCase__		)[-1].split('''.'''		)[0]
        accelerator.init_trackers(UpperCamelCase__    ,       UpperCamelCase__		)

    # Grab all the image filenames
    A       :       int							=						[os.path.join(args.data_dir    ,       UpperCamelCase__		) for fname in os.listdir(args.data_dir		) if fname.endswith('''.jpg'''		)]

    # Build the label correspondences
    A       :       int							=						[extract_label(UpperCamelCase__		) for fname in file_names]
    A       :       str							=						list(set(UpperCamelCase__		)		)
    id_to_label.sort()
    A       :       Dict							=						{lbl: i for i, lbl in enumerate(UpperCamelCase__		)}

    # Set the seed before splitting the data.
    np.random.seed(UpperCamelCase__		)
    torch.manual_seed(UpperCamelCase__		)
    torch.cuda.manual_seed_all(UpperCamelCase__		)

    # Split our filenames between train and validation
    A       :       Dict							=						np.random.permutation(len(UpperCamelCase__		)		)
    A       :       str							=						int(0.8 * len(UpperCamelCase__		)		)
    A       :       Tuple							=						random_perm[:cut]
    A       :       List[Any]							=						random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    A       :       Any							=						Compose([RandomResizedCrop(UpperCamelCase__    ,       scale=(0.5, 1.0)		), ToTensor()]		)
    A       :       List[Any]							=						PetsDataset(
        [file_names[i] for i in train_split]    ,       image_transform=UpperCamelCase__    ,       label_to_id=UpperCamelCase__		)

    # For evaluation, we use a deterministic Resize
    A       :       Optional[Any]							=						Compose([Resize(UpperCamelCase__		), ToTensor()]		)
    A       :       List[Any]							=						PetsDataset([file_names[i] for i in eval_split]    ,       image_transform=UpperCamelCase__    ,       label_to_id=UpperCamelCase__		)

    # Instantiate dataloaders.
    A       :       List[str]							=						DataLoader(UpperCamelCase__    ,       shuffle=UpperCamelCase__    ,       batch_size=UpperCamelCase__    ,       num_workers=4		)
    A       :       Tuple							=						DataLoader(UpperCamelCase__    ,       shuffle=UpperCamelCase__    ,       batch_size=UpperCamelCase__    ,       num_workers=4		)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    A       :       Union[str, Any]							=						create_model('''resnet50d'''    ,       pretrained=UpperCamelCase__    ,       num_classes=len(UpperCamelCase__		)		)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    A       :       Optional[int]							=						model.to(accelerator.device		)

    # Freezing the base model
    for param in model.parameters():
        A       :       Union[str, Any]							=						False
    for param in model.get_classifier().parameters():
        A       :       Any							=						True

    # We normalize the batches of images to be a bit faster.
    A       :       Dict							=						torch.tensor(model.default_cfg['''mean''']		)[None, :, None, None].to(accelerator.device		)
    A       :       str							=						torch.tensor(model.default_cfg['''std''']		)[None, :, None, None].to(accelerator.device		)

    # Instantiate optimizer
    A       :       List[Any]							=						torch.optim.Adam(params=model.parameters()    ,       lr=lr / 25		)

    # Instantiate learning rate scheduler
    A       :       int							=						OneCycleLR(optimizer=UpperCamelCase__    ,       max_lr=UpperCamelCase__    ,       epochs=UpperCamelCase__    ,       steps_per_epoch=len(UpperCamelCase__		)		)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    A,	A,	A,	A,	A       :       Optional[int]							=						accelerator.prepare(
        UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    # We need to keep track of how many total steps we have iterated over
    A       :       Dict							=						0
    # We also need to keep track of the starting epoch so files are named properly
    A       :       str							=						0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''		)
            accelerator.load_state(args.resume_from_checkpoint		)
            A       :       Optional[int]							=						os.path.basename(args.resume_from_checkpoint		)
        else:
            # Get the most recent checkpoint
            A       :       Dict							=						[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
            dirs.sort(key=os.path.getctime		)
            A       :       Optional[int]							=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        A       :       Optional[int]							=						os.path.splitext(UpperCamelCase__		)[0]

        if "epoch" in training_difference:
            A       :       Tuple							=						int(training_difference.replace('''epoch_'''    ,       ''''''		)		) + 1
            A       :       Union[str, Any]							=						None
        else:
            A       :       int							=						int(training_difference.replace('''step_'''    ,       ''''''		)		)
            A       :       str							=						resume_step // len(UpperCamelCase__		)
            resume_step -= starting_epoch * len(UpperCamelCase__		)

    # Now we train the model
    for epoch in range(UpperCamelCase__    ,       UpperCamelCase__		):
        model.train()
        if args.with_tracking:
            A       :       int							=						0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            A       :       str							=						accelerator.skip_first_batches(UpperCamelCase__    ,       UpperCamelCase__		)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            A       :       int							=						train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            A       :       Any							=						{k: v.to(accelerator.device		) for k, v in batch.items()}
            A       :       Optional[int]							=						(batch['''image'''] - mean) / std
            A       :       int							=						model(UpperCamelCase__		)
            A       :       List[Any]							=						torch.nn.functional.cross_entropy(UpperCamelCase__    ,       batch['''label''']		)
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(UpperCamelCase__		)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
                A       :       List[Any]							=						F'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        A       :       Dict							=						os.path.join(args.output_dir    ,       UpperCamelCase__		)
                    accelerator.save_state(UpperCamelCase__		)
        model.eval()
        A       :       Optional[int]							=						0
        A       :       int							=						0
        for step, batch in enumerate(UpperCamelCase__		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            A       :       Any							=						{k: v.to(accelerator.device		) for k, v in batch.items()}
            A       :       Any							=						(batch['''image'''] - mean) / std
            with torch.no_grad():
                A       :       Union[str, Any]							=						model(UpperCamelCase__		)
            A       :       Tuple							=						outputs.argmax(dim=-1		)
            A,	A       :       List[Any]							=						accelerator.gather_for_metrics((predictions, batch['''label'''])		)
            A       :       Any							=						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        A       :       str							=						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}: {100 * eval_metric:.2f}'''		)
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': 100 * eval_metric,
                    '''train_loss''': total_loss.item() / len(UpperCamelCase__		),
                    '''epoch''': epoch,
                }    ,       step=UpperCamelCase__    ,       )
        if checkpointing_steps == "epoch":
            A       :       Dict							=						F'''epoch_{epoch}'''
            if args.output_dir is not None:
                A       :       Dict							=						os.path.join(args.output_dir    ,       UpperCamelCase__		)
            accelerator.save_state(UpperCamelCase__		)

    if args.with_tracking:
        accelerator.end_training()
def 							_lowerCamelCase(  )		->  int:
    A       :       List[str]							=						argparse.ArgumentParser(description='''Simple example of training script.'''		)
    parser.add_argument('''--data_dir'''    ,       required=UpperCamelCase__    ,       help='''The data folder on disk.'''		)
    parser.add_argument('''--fp16'''    ,       action='''store_true'''    ,       help='''If passed, will use FP16 training.'''		)
    parser.add_argument(
        '''--mixed_precision'''    ,       type=UpperCamelCase__    ,       default=UpperCamelCase__    ,       choices=['''no''', '''fp16''', '''bf16''', '''fp8''']    ,       help='''Whether to use mixed precision. Choose'''
        '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
        '''and an Nvidia Ampere GPU.'''    ,       )
    parser.add_argument('''--cpu'''    ,       action='''store_true'''    ,       help='''If passed, will train on the CPU.'''		)
    parser.add_argument(
        '''--checkpointing_steps'''    ,       type=UpperCamelCase__    ,       default=UpperCamelCase__    ,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''    ,       )
    parser.add_argument(
        '''--output_dir'''    ,       type=UpperCamelCase__    ,       default='''.'''    ,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,       )
    parser.add_argument(
        '''--resume_from_checkpoint'''    ,       type=UpperCamelCase__    ,       default=UpperCamelCase__    ,       help='''If the training should continue from a checkpoint folder.'''    ,       )
    parser.add_argument(
        '''--with_tracking'''    ,       action='''store_true'''    ,       help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''    ,       )
    parser.add_argument(
        '''--project_dir'''    ,       type=UpperCamelCase__    ,       default='''logs'''    ,       help='''Location on where to store experiment tracking logs` and relevent project information'''    ,       )
    A       :       Tuple							=						parser.parse_args()
    A       :       List[str]							=						{'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
    training_function(UpperCamelCase__    ,       UpperCamelCase__		)


if __name__ == "__main__":
     main()
