


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 		_A		(		_lowercase )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			fname.split(os.path.sep )[-1]
     return re.search(r'^(.*)_\d+\.jpg$'					,  _lowercase ).groups()[0]



class 	__lowerCamelCase			(_a						):







       def __init__(			self: Optional[int],A_: Tuple,A_: Optional[Any]=None,A_: Dict=None					):






            '''simple docstring'''




            __UpperCamelCase =			file_names
            __UpperCamelCase =			image_transform
            __UpperCamelCase =			label_to_id







       def __len__(			self: Any					):






            '''simple docstring'''




            return len(self.file_names					)







       def __getitem__(			self: Dict,A_: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.file_names[idx]
            __UpperCamelCase =			PIL.Image.open(A_					)
            __UpperCamelCase =			raw_image.convert('RGB'					)
            if self.image_transform is not None:
                 __UpperCamelCase =			self.image_transform(A_					)
            __UpperCamelCase =			extract_label(A_					)
            if self.label_to_id is not None:
                 __UpperCamelCase =			self.label_to_id[label]
            return {"image": image, "label": label}






def 		_A		(		_lowercase					,  _lowercase )					-> Tuple:


     """simple docstring"""


     if args.with_tracking:
          __UpperCamelCase =			Accelerator(
              cpu=args.cpu					,  mixed_precision=args.mixed_precision					,  log_with='all'					,  project_dir=args.project_dir )
     else:
          __UpperCamelCase =			Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision )

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __UpperCamelCase =			config['lr']
     __UpperCamelCase =			int(config['num_epochs'] )
     __UpperCamelCase =			int(config['seed'] )
     __UpperCamelCase =			int(config['batch_size'] )
     __UpperCamelCase =			config['image_size']
     if not isinstance(_lowercase					,  (list, tuple) ):
          __UpperCamelCase =			(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":
               __UpperCamelCase =			args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               __UpperCamelCase =			int(args.checkpointing_steps )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
     else:
          __UpperCamelCase =			None

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

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

     # Build the label correspondences
     __UpperCamelCase =			[extract_label(_lowercase ) for fname in file_names]
     __UpperCamelCase =			list(set(_lowercase ) )
     id_to_label.sort()
     __UpperCamelCase =			{lbl: i for i, lbl in enumerate(_lowercase )}

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

     # Split our filenames between train and validation
     __UpperCamelCase =			np.random.permutation(len(_lowercase ) )
     __UpperCamelCase =			int(0.8 * len(_lowercase ) )
     __UpperCamelCase =			random_perm[:cut]
     __UpperCamelCase =			random_perm[cut:]

     # For training we use a simple RandomResizedCrop
     __UpperCamelCase =			Compose([RandomResizedCrop(_lowercase					,  scale=(0.5, 1.0) ), ToTensor()] )
     __UpperCamelCase =			PetsDataset(
         [file_names[i] for i in train_split]					,  image_transform=_lowercase					,  label_to_id=_lowercase )

     # For evaluation, we use a deterministic Resize
     __UpperCamelCase =			Compose([Resize(_lowercase ), ToTensor()] )
     __UpperCamelCase =			PetsDataset([file_names[i] for i in eval_split]					,  image_transform=_lowercase					,  label_to_id=_lowercase )

     # Instantiate dataloaders.
     __UpperCamelCase =			DataLoader(_lowercase					,  shuffle=_lowercase					,  batch_size=_lowercase					,  num_workers=4 )
     __UpperCamelCase =			DataLoader(_lowercase					,  shuffle=_lowercase					,  batch_size=_lowercase					,  num_workers=4 )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __UpperCamelCase =			create_model('resnet50d'					,  pretrained=_lowercase					,  num_classes=len(_lowercase ) )

     # 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).
     __UpperCamelCase =			model.to(accelerator.device )

     # Freezing the base model
     for param in model.parameters():
          __UpperCamelCase =			False
     for param in model.get_classifier().parameters():
          __UpperCamelCase =			True

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

     # Instantiate optimizer
     __UpperCamelCase =			torch.optim.Adam(params=model.parameters()					,  lr=lr / 25 )

     # Instantiate learning rate scheduler
     __UpperCamelCase =			OneCycleLR(optimizer=_lowercase					,  max_lr=_lowercase					,  epochs=_lowercase					,  steps_per_epoch=len(_lowercase ) )

     # 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.
     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase =			accelerator.prepare(
         _lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )
     # We need to keep track of how many total steps we have iterated over
     __UpperCamelCase =			0
     # We also need to keep track of the starting epoch so files are named properly
     __UpperCamelCase =			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 )
               __UpperCamelCase =			os.path.basename(args.resume_from_checkpoint )
          else:
               # Get the most recent checkpoint
               __UpperCamelCase =			[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
               dirs.sort(key=os.path.getctime )
               __UpperCamelCase =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          __UpperCamelCase =			os.path.splitext(_lowercase )[0]

          if "epoch" in training_difference:
               __UpperCamelCase =			int(training_difference.replace('epoch_'					,  '' ) ) + 1
               __UpperCamelCase =			None
          else:
               __UpperCamelCase =			int(training_difference.replace('step_'					,  '' ) )
               __UpperCamelCase =			resume_step // len(_lowercase )
               resume_step -= starting_epoch * len(_lowercase )

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

          __UpperCamelCase =			accurate.item() / num_elems
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}''' )
          if args.with_tracking:
               accelerator.log(
                   {
                       'accuracy': 1_00 * eval_metric,
                       'train_loss': total_loss.item() / len(_lowercase ),
                       'epoch': epoch,
                   }					,  step=_lowercase					,  )
          if checkpointing_steps == "epoch":
               __UpperCamelCase =			f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    __UpperCamelCase =			os.path.join(args.output_dir					,  _lowercase )
               accelerator.save_state(_lowercase )

     if args.with_tracking:
          accelerator.end_training()






def 		_A		(		)					-> Dict:


     """simple docstring"""


     __UpperCamelCase =			argparse.ArgumentParser(description='Simple example of training script.' )
     parser.add_argument('--data_dir'					,  required=_lowercase					,  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=_lowercase					,  default=_lowercase					,  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=_lowercase					,  default=_lowercase					,  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=_lowercase					,  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=_lowercase					,  default=_lowercase					,  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=_lowercase					,  default='logs'					,  help='Location on where to store experiment tracking logs` and relevent project information'					,  )
     __UpperCamelCase =			parser.parse_args()
     __UpperCamelCase =			{'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 2_24}
     training_function(_lowercase					,  _lowercase )


if __name__ == "__main__":
    main()






import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     KandinskyInpaintPipeline
       _lowercase											=     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
       _lowercase											=     [
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
           """mask_image""",
       ]
       _lowercase											=     [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       _lowercase											=     False







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            return self.time_input_dim







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return self.time_input_dim * 4







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return 100







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
            return tokenizer







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			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=1005,)

            __UpperCamelCase =			MultilingualCLIP(A_					)
            __UpperCamelCase =			text_encoder.eval()

            return text_encoder







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)

            __UpperCamelCase =			{
                'in_channels': 9,
                # 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,
            }

            __UpperCamelCase =			UNetaDConditionModel(**A_					)
            return model







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            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: str					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.dummy_text_encoder
            __UpperCamelCase =			self.dummy_tokenizer
            __UpperCamelCase =			self.dummy_unet
            __UpperCamelCase =			self.dummy_movq

            __UpperCamelCase =			DDIMScheduler(
                num_train_timesteps=1000,beta_schedule='linear',beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,clip_sample=A_,set_alpha_to_one=A_,steps_offset=1,prediction_type='epsilon',thresholding=A_,)

            __UpperCamelCase =			{
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components







       def 		snake_case_	(			self: Tuple,A_: Optional[int],A_: Dict=0					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1					)					).to(A_					)
            # create init_image
            __UpperCamelCase =			floats_tensor((1, 3, 64, 64),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			image.cpu().permute(0,2,3,1					)[0]
            __UpperCamelCase =			Image.fromarray(np.uinta(A_					)					).convert('RGB'					).resize((256, 256)					)
            # create mask
            __UpperCamelCase =			np.ones((64, 64),dtype=np.floataa					)
            __UpperCamelCase =			0

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'horse',
                'image': init_image,
                'mask_image': mask,
                'image_embeds': image_embeds,
                'negative_image_embeds': negative_image_embeds,
                'generator': generator,
                'height': 64,
                'width': 64,
                'num_inference_steps': 2,
                'guidance_scale': 4.0,
                'output_type': 'np',
            }
            return inputs







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'

            __UpperCamelCase =			self.get_dummy_components()

            __UpperCamelCase =			self.pipeline_class(**A_					)
            __UpperCamelCase =			pipe.to(A_					)

            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			pipe(**self.get_dummy_inputs(A_					)					)
            __UpperCamelCase =			output.images

            __UpperCamelCase =			pipe(
                **self.get_dummy_inputs(A_					),return_dict=A_,)[0]

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

            print(F'''image.shape {image.shape}'''					)

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

            __UpperCamelCase =			np.array(
                [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]					)

            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()}'''







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

            __UpperCamelCase =			load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
            __UpperCamelCase =			np.ones((768, 768),dtype=np.floataa					)
            __UpperCamelCase =			0

            __UpperCamelCase =			'a hat'

            __UpperCamelCase =			KandinskyPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-prior',torch_dtype=torch.floataa					)
            pipe_prior.to(A_					)

            __UpperCamelCase =			KandinskyInpaintPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-inpaint',torch_dtype=torch.floataa					)
            __UpperCamelCase =			pipeline.to(A_					)
            pipeline.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			torch.Generator(device='cpu'					).manual_seed(0					)
            __UpperCamelCase,    __UpperCamelCase =			pipe_prior(
                A_,generator=A_,num_inference_steps=5,negative_prompt='',).to_tuple()

            __UpperCamelCase =			pipeline(
                A_,image=A_,mask_image=A_,image_embeds=A_,negative_image_embeds=A_,generator=A_,num_inference_steps=100,height=768,width=768,output_type='np',)

            __UpperCamelCase =			output.images[0]

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

            assert_mean_pixel_difference(A_,A_					)


