




import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase__    :							List[Any]       		=     16
UpperCAmelCase__    :							str       		=     32


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Union[str, Any]:





  """simple docstring"""





  return int(x / 2**20				)

class   UpperCAmelCase    :






   def __enter__(						self :							Union[str, Any]  )->      Union[str, Any]:
     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
     _lowercase:			Optional[int]      =     torch.cuda.memory_allocated()
     _lowercase:			List[str]      =     psutil.Process()

     _lowercase:			Dict      =     self.cpu_mem_used()
     _lowercase:			List[Any]      =     True
     _lowercase:			Optional[Any]      =     threading.Thread(target=self.peak_monitor_func  )
     _lowercase:			List[str]      =     True
     peak_monitor_thread.start()
     return self






   def 			_a    (						self :							Tuple  )->      Tuple:
     return self.process.memory_info().rss






   def 			_a    (						self :							Union[str, Any]  )->      Dict:
     _lowercase:			Optional[int]      =     -1

     while True:
       _lowercase:			Optional[int]      =     max(self.cpu_mem_used()    ,       self.cpu_peak  )

       # can't sleep or will not catch the peak right (this comment is here on purpose)
       # time.sleep(0.001) # 1msec

       if not self.peak_monitoring:
         break






   def __exit__(						self :							Union[str, Any]    ,       *UpperCamelCase_ :							str  )->      Dict:
     _lowercase:			Dict      =     False

     gc.collect()
     torch.cuda.empty_cache()
     _lowercase:			Optional[Any]      =     torch.cuda.memory_allocated()
     _lowercase:			str      =     torch.cuda.max_memory_allocated()
     _lowercase:			Dict      =     bamb(self.end - self.begin  )
     _lowercase:			Dict      =     bamb(self.peak - self.begin  )

     _lowercase:			Optional[Any]      =     self.cpu_mem_used()
     _lowercase:			List[str]      =     bamb(self.cpu_end - self.cpu_begin  )
     _lowercase:			List[str]      =     bamb(self.cpu_peak - self.cpu_begin  )
     # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   UpperCAmelCase__    :							List[Any]       		=     mocked_dataloaders  # noqa: F811


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,  UpperCamelCase				) == "1":
    _lowercase:			List[Any]      =     2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  _lowercase:			Optional[int]      =     FullyShardedDataParallelPlugin(
      state_dict_config=FullStateDictConfig(offload_to_cpu=UpperCamelCase       ,  ranka_only=UpperCamelCase				)       ,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=UpperCamelCase       ,  ranka_only=UpperCamelCase				)       ,  )

  # Initialize accelerator
  if args.with_tracking:
    _lowercase:			Dict      =     Accelerator(
        cpu=args.cpu       ,  mixed_precision=args.mixed_precision       ,  log_with='wandb'       ,  project_dir=args.logging_dir       ,  fsdp_plugin=UpperCamelCase       ,  )
  else:
    _lowercase:			Optional[Any]      =     Accelerator(fsdp_plugin=UpperCamelCase				)
  accelerator.print(accelerator.distributed_type				)

  if hasattr(args.checkpointing_steps       ,  'isdigit'				):
    if args.checkpointing_steps == "epoch":
      _lowercase:			Optional[int]      =     args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowercase:			Dict      =     int(args.checkpointing_steps				)
    else:
      raise ValueError(
          F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'				)
  else:
    _lowercase:			Optional[int]      =     None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:			List[Any]      =     config['lr']
  _lowercase:			List[Any]      =     int(config['num_epochs']				)
  _lowercase:			List[str]      =     int(config['seed']				)
  _lowercase:			str      =     int(config['batch_size']				)

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    _lowercase:			Union[str, Any]      =     vars(UpperCamelCase				)
    accelerator.init_trackers('fsdp_glue_no_trainer'       ,  UpperCamelCase				)

  _lowercase:			int      =     AutoTokenizer.from_pretrained(args.model_name_or_path				)
  _lowercase:			str      =     load_dataset('glue'       ,  'mrpc'				)
  _lowercase:			Optional[int]      =     evaluate.load('glue'       ,  'mrpc'				)

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

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    _lowercase:			Optional[int]      =     datasets.map(
        UpperCamelCase       ,  batched=UpperCamelCase       ,  remove_columns=['idx', 'sentence1', 'sentence2']       ,  )

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

  # If the batch size is too big we use gradient accumulation
  _lowercase:			Dict      =     1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _lowercase:			Dict      =     batch_size // MAX_GPU_BATCH_SIZE
    _lowercase:			Optional[int]      =     MAX_GPU_BATCH_SIZE

  def collate_fn(UpperCamelCase				):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowercase:			List[str]      =     128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowercase:			Optional[int]      =     16
    elif accelerator.mixed_precision != "no":
      _lowercase:			Union[str, Any]      =     8
    else:
      _lowercase:			str      =     None

    return tokenizer.pad(
        UpperCamelCase       ,  padding='longest'       ,  max_length=UpperCamelCase       ,  pad_to_multiple_of=UpperCamelCase       ,  return_tensors='pt'       ,  )

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

  set_seed(UpperCamelCase				)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _lowercase:			int      =     AutoModelForSequenceClassification.from_pretrained(
      args.model_name_or_path       ,  return_dict=UpperCamelCase       ,  low_cpu_mem_usage=UpperCamelCase				)

  # New Code #
  # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  _lowercase:			Union[str, Any]      =     accelerator.prepare(UpperCamelCase				)
  accelerator.print(UpperCamelCase				)

  # Instantiate optimizer
  # New Code #
  # For FSDP feature, at present it doesn't support multiple parameter groups,
  # so we need to create a single parameter group for the whole model
  _lowercase:			Optional[Any]      =     torch.optim.AdamW(params=model.parameters()       ,  lr=UpperCamelCase       ,  weight_decay=2E-4				)

  # Instantiate scheduler
  _lowercase:			Union[str, Any]      =     get_linear_schedule_with_warmup(
      optimizer=UpperCamelCase       ,  num_warmup_steps=10       ,  num_training_steps=(len(UpperCamelCase				) * num_epochs) // gradient_accumulation_steps       ,  )

  # New Code #
  # For FSDP feature, prepare everything except the model as we have already prepared the model
  # before creating the optimizer
  # 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:			int      =     accelerator.prepare(
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

  _lowercase:			Union[str, Any]      =     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				)
      _lowercase:			Optional[Any]      =     os.path.basename(args.resume_from_checkpoint				)
    else:
      # Get the most recent checkpoint
      _lowercase:			int      =     [f.name for f in os.scandir(os.getcwd()				) if f.is_dir()]
      dirs.sort(key=os.path.getctime				)
      _lowercase:			List[str]      =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowercase:			Union[str, Any]      =     os.path.splitext(UpperCamelCase				)[0]

    if "epoch" in training_difference:
      num_epochs -= int(training_difference.replace('epoch_'       ,  ''				)				)
      _lowercase:			Any      =     None
    else:
      _lowercase:			int      =     int(training_difference.replace('step_'       ,  ''				)				)
      num_epochs -= resume_step // len(UpperCamelCase				)
      # If resuming by step, we also need to know exactly how far into the DataLoader we went
      _lowercase:			Optional[int]      =     (num_epochs * len(UpperCamelCase				)) - resume_step

    # Now we train the model
  for epoch in range(UpperCamelCase				):
    # New Code #
    # context manager to track the peak memory usage during the training epoch
    with TorchTracemalloc() as tracemalloc:
      model.train()
      if args.with_tracking:
        _lowercase:			Optional[Any]      =     0
      for step, batch in enumerate(UpperCamelCase				):
        # We need to skip steps until we reach the resumed step
        if args.resume_from_checkpoint and epoch == 0:
          if resume_step is not None and step < resume_step:
            pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device				)
        _lowercase:			int      =     model(**UpperCamelCase				)
        _lowercase:			Optional[Any]      =     outputs.loss
        _lowercase:			Union[str, Any]      =     loss / gradient_accumulation_steps
        # We keep track of the loss at each epoch
        if args.with_tracking:
          total_loss += loss.detach().float()
        accelerator.backward(UpperCamelCase				)
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          # accelerator.print(lr_scheduler.get_lr())

        overall_step += 1

        if isinstance(UpperCamelCase       ,  UpperCamelCase				):
          _lowercase:			Optional[int]      =     F'step_{overall_step}'
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              _lowercase:			Optional[int]      =     os.path.join(args.output_dir       ,  UpperCamelCase				)
            accelerator.save_state(UpperCamelCase				)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin				)				)				)
    accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used				)				)
    accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked				)				)
    accelerator.print(
        'Total Peak Memory consumed during the train (max): {}'.format(
            tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin				),
          }       ,  step=UpperCamelCase       ,  )

    # New Code #
    # context manager to track the peak memory usage during the evaluation
    with TorchTracemalloc() as tracemalloc:
      model.eval()
      for step, batch in enumerate(UpperCamelCase				):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device				)
        with torch.no_grad():
          _lowercase:			Any      =     model(**UpperCamelCase				)
        _lowercase:			Optional[Any]      =     outputs.logits.argmax(dim=-1				)
        _lowercase  ,       _lowercase:			Dict      =     accelerator.gather_for_metrics((predictions, batch['labels'])				)
        metric.add_batch(
            predictions=UpperCamelCase       ,  references=UpperCamelCase       ,  )

      _lowercase:			str      =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'epoch {epoch}:'       ,  UpperCamelCase				)
      if args.with_tracking:
        accelerator.log(
            {
                'accuracy': eval_metric['accuracy'],
                'f1': eval_metric['f1'],
                'train_loss': total_loss.item() / len(UpperCamelCase				),
            }       ,  step=UpperCamelCase       ,  )

      if checkpointing_steps == "epoch":
        _lowercase:			int      =     F'epoch_{epoch}'
        if args.output_dir is not None:
          _lowercase:			List[str]      =     os.path.join(args.output_dir       ,  UpperCamelCase				)
        accelerator.save_state(UpperCamelCase				)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin				)				)				)
    accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used				)				)
    accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked				)				)
    accelerator.print(
        'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin				)				)				)
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin				),
          }       ,  step=UpperCamelCase       ,  )

  if args.with_tracking:
    accelerator.end_training()


def 		_lowerCamelCase					(       )      ->		List[str]:





  """simple docstring"""





  _lowercase:			Optional[Any]      =     argparse.ArgumentParser(description='Simple example of training script.'				)
  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(
      '--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(
      '--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(
      '--logging_dir'       ,  type=UpperCamelCase       ,  default='logs'       ,  help='Location on where to store experiment tracking logs`'       ,  )
  parser.add_argument(
      '--model_name_or_path'       ,  type=UpperCamelCase       ,  help='Path to pretrained model or model identifier from huggingface.co/models.'       ,  required=UpperCamelCase       ,  )
  _lowercase:			int      =     parser.parse_args()
  _lowercase:			Dict      =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(UpperCamelCase       ,  UpperCamelCase				)


if __name__ == "__main__":
   main()






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				int      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				Union[str, Any]      					=       [
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       [
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __SCREAMING_SNAKE_CASE		:				Dict      					=       False






   @property
   def 			_a    (						self :							str  )->      Dict:
     return 32






   @property
   def 			_a    (						self :							Tuple  )->      Any:
     return 32






   @property
   def 			_a    (						self :							Union[str, Any]  )->      List[str]:
     return self.time_input_dim






   @property
   def 			_a    (						self :							Optional[Any]  )->      Tuple:
     return self.time_input_dim * 4






   @property
   def 			_a    (						self :							int  )->      Dict:
     return 1_00






   @property
   def 			_a    (						self :							Tuple  )->      Tuple:
     _lowercase:			List[Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							List[str]  )->      Optional[int]:
     torch.manual_seed(0  )
     _lowercase:			int      =     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=10_05    ,       )

     _lowercase:			Optional[Any]      =     MultilingualCLIP(UpperCamelCase_  )
     _lowercase:			int      =     text_encoder.eval()

     return text_encoder






   @property
   def 			_a    (						self :							Optional[int]  )->      Optional[Any]:
     torch.manual_seed(0  )

     _lowercase:			Optional[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,
     }

     _lowercase:			str      =     UNetaDConditionModel(**UpperCamelCase_  )
     return model






   @property
   def 			_a    (						self :							Dict  )->      Optional[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 			_a    (						self :							List[str]  )->      int:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     VQModel(**self.dummy_movq_kwargs  )
     return model






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			List[str]      =     self.dummy_text_encoder
     _lowercase:			List[Any]      =     self.dummy_tokenizer
     _lowercase:			Optional[Any]      =     self.dummy_unet
     _lowercase:			List[str]      =     self.dummy_movq

     _lowercase:			Optional[Any]      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=UpperCamelCase_    ,       )

     _lowercase:			List[Any]      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							str    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[int]=0  )->      Union[str, Any]:
     _lowercase:			str      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(UpperCamelCase_  )  ).to(UpperCamelCase_  )
     _lowercase:			Optional[int]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(UpperCamelCase_  )
     if str(UpperCamelCase_  ).startswith('mps'  ):
       _lowercase:			Any      =     torch.manual_seed(UpperCamelCase_  )
     else:
       _lowercase:			Tuple      =     torch.Generator(device=UpperCamelCase_  ).manual_seed(UpperCamelCase_  )
     _lowercase:			List[str]      =     {
         'prompt': 'horse',
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs






   def 			_a    (						self :							List[str]  )->      List[str]:
     _lowercase:			str      =     'cpu'

     _lowercase:			Optional[Any]      =     self.get_dummy_components()

     _lowercase:			int      =     self.pipeline_class(**UpperCamelCase_  )
     _lowercase:			List[str]      =     pipe.to(UpperCamelCase_  )

     pipe.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Tuple      =     pipe(**self.get_dummy_inputs(UpperCamelCase_  )  )
     _lowercase:			Union[str, Any]      =     output.images

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

     _lowercase:			int      =     image[0, -3:, -3:, -1]
     _lowercase:			List[str]      =     image_from_tuple[0, -3:, -3:, -1]

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

     _lowercase:			Union[str, Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     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   UpperCAmelCase    (      unittest.TestCase ):






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






   def 			_a    (						self :							List[Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			List[Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(UpperCamelCase_  )

     _lowercase:			str      =     KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,       torch_dtype=torch.floataa  )
     _lowercase:			List[str]      =     pipeline.to(UpperCamelCase_  )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_  )

     _lowercase:			Dict      =     'red cat, 4k photo'

     _lowercase:			List[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			Dict      =     pipe_prior(
         UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			str      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Optional[int]      =     pipeline(
         UpperCamelCase_    ,       image_embeds=UpperCamelCase_    ,       negative_image_embeds=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Optional[int]      =     output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(UpperCamelCase_    ,       UpperCamelCase_  )
