


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   A__					(    snake_case__       ,					unittest.TestCase	):







    """simple docstring"""

    __magic_name__			  =		KandinskyVaaImgaImgPipeline
    __magic_name__			  =		['image_embeds', 'negative_image_embeds', 'image']
    __magic_name__			  =		[
        'image_embeds',
        'negative_image_embeds',
        'image',
    ]
    __magic_name__			  =		[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    __magic_name__			  =		False
    @property
    def 		a_			(       self    ):
         return 3_2
    @property
    def 		a_			(       self    ):
         return 3_2
    @property
    def 		a_			(       self    ):
         return self.time_input_dim
    @property
    def 		a_			(       self    ):
         return self.time_input_dim * 4
    @property
    def 		a_			(       self    ):
         return 1_0_0
    @property
    def 		a_			(       self    ):
         torch.manual_seed(0    )

         snake_case		 =				{
             '''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,
         }

         snake_case		 =				UNetaDConditionModel(**__snake_case    )
         return model
    @property
    def 		a_			(       self    ):
         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 		a_			(       self    ):
         torch.manual_seed(0    )
         snake_case		 =				VQModel(**self.dummy_movq_kwargs    )
         return model
    def 		a_			(       self    ):
         snake_case		 =				self.dummy_unet
         snake_case		 =				self.dummy_movq

         snake_case		 =				{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_schedule''': '''linear''',
             '''beta_start''': 0.0_0085,
             '''beta_end''': 0.012,
             '''clip_sample''': False,
             '''set_alpha_to_one''': False,
             '''steps_offset''': 0,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
         }

         snake_case		 =				DDIMScheduler(**__snake_case    )

         snake_case		 =				{
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components
    def 		a_			(       self			,					__snake_case			,					__snake_case=0    ):
         snake_case		 =				floats_tensor((1, self.text_embedder_hidden_size)			,					rng=random.Random(__snake_case    )    ).to(__snake_case    )
         snake_case		 =				floats_tensor((1, self.text_embedder_hidden_size)			,					rng=random.Random(seed + 1    )    ).to(
             __snake_case    )
         # create init_image
         snake_case		 =				floats_tensor((1, 3, 6_4, 6_4)			,					rng=random.Random(__snake_case    )    ).to(__snake_case    )
         snake_case		 =				image.cpu().permute(0			,					2			,					3			,					1    )[0]
         snake_case		 =				Image.fromarray(np.uinta(__snake_case    )    ).convert('''RGB'''    ).resize((2_5_6, 2_5_6)    )

         if str(__snake_case    ).startswith('''mps'''    ):
              snake_case		 =				torch.manual_seed(__snake_case    )
         else:
              snake_case		 =				torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
         snake_case		 =				{
             '''image''': init_image,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 6_4,
             '''width''': 6_4,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 7.0,
             '''strength''': 0.2,
             '''output_type''': '''np''',
         }
         return inputs




    def 		a_			(       self    ):
         snake_case		 =				'''cpu'''

         snake_case		 =				self.get_dummy_components()

         snake_case		 =				self.pipeline_class(**__snake_case    )
         snake_case		 =				pipe.to(__snake_case    )

         pipe.set_progress_bar_config(disable=__snake_case    )

         snake_case		 =				pipe(**self.get_dummy_inputs(__snake_case    )    )
         snake_case		 =				output.images

         snake_case		 =				pipe(
             **self.get_dummy_inputs(__snake_case    )			,					return_dict=__snake_case			,					)[0]

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

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

         snake_case		 =				np.array(
             [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]    )
         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   A__					(    unittest.TestCase	):







    """simple docstring"""

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




    def 		a_			(       self    ):
         snake_case		 =				load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''    )

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

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

         snake_case		 =				KandinskyVaaImgaImgPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-2-decoder'''			,					torch_dtype=torch.floataa    )
         snake_case		 =				pipeline.to(__snake_case    )

         pipeline.set_progress_bar_config(disable=__snake_case    )

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

         snake_case		 =				pipeline(
             image=__snake_case			,					image_embeds=__snake_case			,					negative_image_embeds=__snake_case			,					generator=__snake_case			,					num_inference_steps=1_0_0			,					height=7_6_8			,					width=7_6_8			,					strength=0.2			,					output_type='''np'''			,					)

         snake_case		 =				output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(__snake_case			,					__snake_case    )







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase_									=   1_6
lowercase_									=   3_2
def      a      (			A__	:   Accelerator      ,   A__	:   int = 16      ,   A__	:   str = "bert-base-cased"     )    ->      Optional[int]:





      """simple docstring"""



      _lowercase	       =AutoTokenizer.from_pretrained(A__     )
      _lowercase	       =load_dataset('glue'      ,   'mrpc'     )

      def tokenize_function(A__	:   Optional[int]     ):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase	       =tokenizer(examples['sentence1']      ,   examples['sentence2']      ,   truncation=A__      ,   max_length=A__     )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      _lowercase	       =datasets.map(
          A__      ,   batched=A__      ,   remove_columns=['idx', 'sentence1', 'sentence2']      ,   load_from_cache_file=A__     )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      _lowercase	       =tokenized_datasets.rename_column('label'      ,   'labels'     )

      def collate_fn(A__	:   List[str]     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(A__      ,   padding='max_length'      ,   max_length=128      ,   return_tensors='pt'     )
            return tokenizer.pad(A__      ,   padding='longest'      ,   return_tensors='pt'     )

      # Instantiate dataloaders.
      _lowercase	       =DataLoader(
          tokenized_datasets['train']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__     )
      _lowercase	       =DataLoader(
          tokenized_datasets['validation']      ,   shuffle=A__      ,   collate_fn=A__      ,   batch_size=A__     )

      return train_dataloader, eval_dataloader
def      a      (			A__	:   Optional[Any]      ,   A__	:   Optional[int]      ,   A__	:   List[str]      ,   A__	:   Dict     )    ->      Dict:





      """simple docstring"""



      model.eval()
      _lowercase	       =0
      for step, batch in enumerate(A__     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            with torch.no_grad():
                  _lowercase	       =model(**A__     )
            _lowercase	       =outputs.logits.argmax(dim=-1     )
            # It is slightly faster to call this once, than multiple times
            _lowercase       ,       _lowercase	       =accelerator.gather(
                (predictions, batch['labels'])     )  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(A__     ) - 1:
                        _lowercase	       =predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                        _lowercase	       =references[: len(eval_dataloader.dataset     ) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=A__      ,   references=A__      ,   )

      _lowercase	       =metric.compute()
      return eval_metric["accuracy"]
def      a      (			A__	:   str      ,   A__	:   Optional[Any]     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      _lowercase	       =config['lr']
      _lowercase	       =int(config['num_epochs']     )
      _lowercase	       =int(config['seed']     )
      _lowercase	       =int(config['batch_size']     )
      _lowercase	       =args.model_name_or_path

      set_seed(A__     )
      _lowercase       ,       _lowercase	       =get_dataloaders(A__      ,   A__      ,   A__     )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _lowercase	       =AutoModelForSequenceClassification.from_pretrained(A__      ,   return_dict=A__     )

      # Instantiate optimizer
      _lowercase	       =(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      _lowercase	       =optimizer_cls(params=model.parameters()      ,   lr=A__     )

      if accelerator.state.deepspeed_plugin is not None:
            _lowercase	       =accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            _lowercase	       =1
      _lowercase	       =(len(A__     ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            _lowercase	       =get_linear_schedule_with_warmup(
                optimizer=A__      ,   num_warmup_steps=0      ,   num_training_steps=A__      ,   )
      else:
            _lowercase	       =DummyScheduler(A__      ,   total_num_steps=A__      ,   warmup_num_steps=0     )

      # 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.
      _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase       ,       _lowercase	       =accelerator.prepare(
          A__      ,   A__      ,   A__      ,   A__      ,   A__     )

      # We need to keep track of how many total steps we have iterated over
      _lowercase	       =0
      # We also need to keep track of the stating epoch so files are named properly
      _lowercase	       =0
      _lowercase	       =evaluate.load('glue'      ,   'mrpc'     )
      _lowercase	       =num_epochs

      if args.partial_train_epoch is not None:
            _lowercase	       =args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint     )
            _lowercase	       =args.resume_from_checkpoint.split('epoch_'     )[1]
            _lowercase	       =''
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            _lowercase	       =int(A__     ) + 1
            _lowercase	       =evaluation_loop(A__      ,   A__      ,   A__      ,   A__     )
            accelerator.print('resumed checkpoint performance:'      ,   A__     )
            accelerator.print('resumed checkpoint\'s scheduler\'s lr:'      ,   lr_scheduler.get_lr()[0]     )
            accelerator.print('resumed optimizers\'s lr:'      ,   optimizer.param_groups[0]['lr']     )
            with open(os.path.join(args.output_dir      ,   F'''state_{starting_epoch-1}.json'''     )      ,   'r'     ) as f:
                  _lowercase	       =json.load(A__     )
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      _lowercase	       ={}
      for epoch in range(A__      ,   A__     ):
            model.train()
            for step, batch in enumerate(A__     ):
                  _lowercase	       =model(**A__     )
                  _lowercase	       =outputs.loss
                  _lowercase	       =loss / gradient_accumulation_steps
                  accelerator.backward(A__     )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            _lowercase	       =F'''epoch_{epoch}'''
            _lowercase	       =os.path.join(args.output_dir      ,   A__     )
            accelerator.save_state(A__     )
            _lowercase	       =evaluation_loop(A__      ,   A__      ,   A__      ,   A__     )
            _lowercase	       =accuracy
            _lowercase	       =lr_scheduler.get_lr()[0]
            _lowercase	       =optimizer.param_groups[0]['lr']
            _lowercase	       =epoch
            _lowercase	       =overall_step
            accelerator.print(F'''epoch {epoch}:'''      ,   A__     )

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir      ,   F'''state_{epoch}.json'''     )      ,   'w'     ) as f:
                        json.dump(A__      ,   A__     )
def      a      (			)    ->      Tuple:





      """simple docstring"""



      _lowercase	       =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
      parser.add_argument(
          '--model_name_or_path'      ,   type=A__      ,   default='bert-base-cased'      ,   help='Path to pretrained model or model identifier from huggingface.co/models.'      ,   required=A__      ,   )
      parser.add_argument(
          '--output_dir'      ,   type=A__      ,   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=A__      ,   default=A__      ,   help='If the training should continue from a checkpoint folder.'      ,   )
      parser.add_argument(
          '--partial_train_epoch'      ,   type=A__      ,   default=A__      ,   help='If passed, the training will stop after this number of epochs.'      ,   )
      parser.add_argument(
          '--num_epochs'      ,   type=A__      ,   default=2      ,   help='Number of train epochs.'      ,   )
      _lowercase	       =parser.parse_args()
      _lowercase	       ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

      training_function(A__      ,   A__     )


if __name__ == "__main__":
  main()


