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
#
########################################################################


_SCREAMING_SNAKE_CASE     =     16
_SCREAMING_SNAKE_CASE     =     32
def 					__lowercase						(    lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  return int(x / 2**20       )







class        UpperCAmelCase_					:




    def __enter__(       self	: Dict )						->			Optional[Any]:
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      a__  		=  torch.cuda.memory_allocated()
      a__  		=  psutil.Process()

      a__  		=  self.cpu_mem_used()
      a__  		=  True
      a__  		=  threading.Thread(target=self.peak_monitor_func )
      a__  		=  True
      peak_monitor_thread.start()
      return self




    def     __lowerCamelCase			(       self	: Optional[int] )						->			Tuple:
      return self.process.memory_info().rss




    def     __lowerCamelCase			(       self	: str )						->			Dict:
      a__  		=  -1

      while True:
        a__  		=  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	: int   ,   *lowerCAmelCase	: Any )						->			int:
      a__  		=  False

      gc.collect()
      torch.cuda.empty_cache()
      a__  		=  torch.cuda.memory_allocated()
      a__  		=  torch.cuda.max_memory_allocated()
      a__  		=  bamb(self.end - self.begin )
      a__  		=  bamb(self.peak - self.begin )

      a__  		=  self.cpu_mem_used()
      a__  		=  bamb(self.cpu_end - self.cpu_begin )
      a__  		=  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

     _SCREAMING_SNAKE_CASE     =     mocked_dataloaders  # noqa: F811
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Any:

  '''simple docstring'''




  if os.environ.get("TESTING_MOCKED_DATALOADERS"			,					_UpperCamelCase       ) == "1":
    a__  		=  2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  a__  		=  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:
    a__  		=  Accelerator(
        cpu=args.cpu			,					mixed_precision=args.mixed_precision			,					log_with="wandb"			,					project_dir=args.logging_dir			,					fsdp_plugin=_UpperCamelCase			,					)
  else:
    a__  		=  Accelerator(fsdp_plugin=_UpperCamelCase       )
  accelerator.print(accelerator.distributed_type       )

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

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    a__  		=  vars(_UpperCamelCase       )
    accelerator.init_trackers("fsdp_glue_no_trainer"			,					_UpperCamelCase       )

  a__  		=  AutoTokenizer.from_pretrained(args.model_name_or_path       )
  a__  		=  load_dataset("glue"			,					"mrpc"       )
  a__  		=  evaluate.load("glue"			,					"mrpc"       )

  def tokenize_function(lowerCamelCase__       ):
    # max_length=None => use the model max length (it's actually the default)
    a__  		=  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():
    a__  		=  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
  a__  		=  tokenized_datasets.rename_column("label"			,					"labels"       )

  # If the batch size is too big we use gradient accumulation
  a__  		=  1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a__  		=  batch_size // MAX_GPU_BATCH_SIZE
    a__  		=  MAX_GPU_BATCH_SIZE

  def collate_fn(lowerCamelCase__       ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__  		=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      a__  		=  16
    elif accelerator.mixed_precision != "no":
      a__  		=  8
    else:
      a__  		=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding="longest"			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors="pt"			,					)

  # Instantiate dataloaders.
  a__  		=  DataLoader(
      tokenized_datasets["train"]			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase       )
  a__  		=  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)
  a__  		=  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
  a__  		=  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
  a__  		=  torch.optim.AdamW(params=model.parameters()			,					lr=_UpperCamelCase			,					weight_decay=2E-4       )

  # Instantiate scheduler
  a__  		=  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.
  a__		,a__		,a__		,a__  		=  accelerator.prepare(
      _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase       )

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

    if "epoch" in training_difference:
      num_epochs -= int(training_difference.replace("epoch_"			,					""       )       )
      a__  		=  None
    else:
      a__  		=  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
      a__  		=  (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:
        a__  		=  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       )
        a__  		=  model(**_UpperCamelCase       )
        a__  		=  outputs.loss
        a__  		=  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       ):
          a__  		=  f"step_{overall_step}"
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              a__  		=  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():
          a__  		=  model(**_UpperCamelCase       )
        a__  		=  outputs.logits.argmax(dim=-1       )
        a__		,a__  		=  accelerator.gather_for_metrics((predictions, batch["labels"])       )
        metric.add_batch(
            predictions=_UpperCamelCase			,					references=_UpperCamelCase			,					)

      a__  		=  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":
        a__  		=  f"epoch_{epoch}"
        if args.output_dir is not None:
          a__  		=  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 					__lowercase						(    )					->List[str]:

  '''simple docstring'''




  a__  		=  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			,					)
  a__  		=  parser.parse_args()
  a__  		=  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase       )


if __name__ == "__main__":
     main()





"""simple docstring"""







from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


__a      			= getLogger(__name__)


__a      			= 4
__a      			= 7

__a      			= {
    "float16": torch.floataa,
    "float32": torch.floataa,
}






def        a__			(							_UpperCamelCase			):





						"""simple docstring"""

						if isinstance(_UpperCamelCase						,      torch.Tensor			):
												return image
						elif isinstance(_UpperCamelCase						,      PIL.Image.Image			):
												UpperCAmelCase__												=     [image]

						if isinstance(image[0]						,      PIL.Image.Image			):
												UpperCAmelCase__						,					UpperCAmelCase__												=     image[0].size
												UpperCAmelCase__						,					UpperCAmelCase__												=     (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

												UpperCAmelCase__												=     [np.array(i.resize((w, h)			)			)[None, :] for i in image]
												UpperCAmelCase__												=     np.concatenate(_UpperCamelCase						,      axis=0			)
												UpperCAmelCase__												=     np.array(_UpperCamelCase			).astype(np.floataa			) / 255.0
												UpperCAmelCase__												=     image.transpose(0						,      3						,      1						,      2			)
												UpperCAmelCase__												=     2.0 * image - 1.0
												UpperCAmelCase__												=     torch.from_numpy(_UpperCamelCase			)
						elif isinstance(image[0]						,      torch.Tensor			):
												UpperCAmelCase__												=     torch.cat(_UpperCamelCase						,      dim=0			)

						return image


class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE			):


							"""simple docstring"""






							def __init__(    self			:  Optional[int]		,			lowerCamelCase_			:  OnnxRuntimeModel		,			lowerCamelCase_			:  OnnxRuntimeModel		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  OnnxRuntimeModel		,			lowerCamelCase_			:  DDPMScheduler		,			lowerCamelCase_			:  Any		,			lowerCamelCase_			:  int = 350		,			):
													super().__init__(
													    vae=lowerCamelCase_		,			text_encoder=lowerCamelCase_		,			tokenizer=lowerCamelCase_		,			unet=lowerCamelCase_		,			low_res_scheduler=lowerCamelCase_		,			scheduler=lowerCamelCase_		,			safety_checker=lowerCamelCase_		,			feature_extractor=lowerCamelCase_		,			watermarker=lowerCamelCase_		,			max_noise_level=lowerCamelCase_		,			)






							def __call__(    self			:  Optional[int]		,			lowerCamelCase_			:  Union[str, List[str]]		,			lowerCamelCase_			:  Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]		,			lowerCamelCase_			:  int = 75		,			lowerCamelCase_			:  float = 9.0		,			lowerCamelCase_			:  int = 20		,			lowerCamelCase_			:  Optional[Union[str, List[str]]] = None		,			lowerCamelCase_			:  Optional[int] = 1		,			lowerCamelCase_			:  float = 0.0		,			lowerCamelCase_			:  Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowerCamelCase_			:  Optional[torch.FloatTensor] = None		,			lowerCamelCase_			:  Optional[np.ndarray] = None		,			lowerCamelCase_			:  Optional[np.ndarray] = None		,			lowerCamelCase_			:  Optional[str] = "pil"		,			lowerCamelCase_			:  bool = True		,			lowerCamelCase_			:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,			lowerCamelCase_			:  Optional[int] = 1		,			):
													self.check_inputs(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

													# 2. Define call parameters
													if prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     1
													elif prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     len(lowerCamelCase_					)
													else:
																			UpperCAmelCase__												=     prompt_embeds.shape[0]

													UpperCAmelCase__												=     self._execution_device
													# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
													# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
													# corresponds to doing no classifier free guidance.
													UpperCAmelCase__												=     guidance_scale > 1.0

													# 3. Encode input prompt
													UpperCAmelCase__												=     self._encode_prompt(
													    lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			prompt_embeds=lowerCamelCase_		,			negative_prompt_embeds=lowerCamelCase_		,			)

													UpperCAmelCase__												=     ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]

													# 4. Preprocess image
													UpperCAmelCase__												=     preprocess(lowerCamelCase_					)
													UpperCAmelCase__												=     image.cpu()

													# 5. set timesteps
													self.scheduler.set_timesteps(lowerCamelCase_		,			device=lowerCamelCase_					)
													UpperCAmelCase__												=     self.scheduler.timesteps

													# 5. Add noise to image
													UpperCAmelCase__												=     torch.tensor([noise_level]		,			dtype=torch.long		,			device=lowerCamelCase_					)
													UpperCAmelCase__												=     torch.randn(image.shape		,			generator=lowerCamelCase_		,			device=lowerCamelCase_		,			dtype=lowerCamelCase_					)
													UpperCAmelCase__												=     self.low_res_scheduler.add_noise(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

													UpperCAmelCase__												=     2 if do_classifier_free_guidance else 1
													UpperCAmelCase__												=     np.concatenate([image] * batch_multiplier * num_images_per_prompt					)
													UpperCAmelCase__												=     np.concatenate([noise_level] * image.shape[0]					)

													# 6. Prepare latent variables
													UpperCAmelCase__						,					UpperCAmelCase__												=     image.shape[2:]
													UpperCAmelCase__												=     self.prepare_latents(
													    batch_size * num_images_per_prompt		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_		,			)

													# 7. Check that sizes of image and latents match
													UpperCAmelCase__												=     image.shape[1]
													if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
																			raise ValueError(
																			    '''Incorrect configuration settings! The config of `pipeline.unet` expects'''
																			    f""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
																			    f""" `num_channels_image`: {num_channels_image} """
																			    f""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
																			    ''' `pipeline.unet` or your `image` input.'''					)

													# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
													UpperCAmelCase__												=     self.prepare_extra_step_kwargs(lowerCamelCase_		,			lowerCamelCase_					)

													UpperCAmelCase__												=     next(
													    (input.type for input in self.unet.model.get_inputs() if input.name == '''timestep''')		,			'''tensor(float)'''					)
													UpperCAmelCase__												=     ORT_TO_NP_TYPE[timestep_dtype]

													# 9. Denoising loop
													UpperCAmelCase__												=     len(lowerCamelCase_					) - num_inference_steps * self.scheduler.order
													with self.progress_bar(total=lowerCamelCase_					) as progress_bar:
																			for i, t in enumerate(lowerCamelCase_					):
																									# expand the latents if we are doing classifier free guidance
																									UpperCAmelCase__												=     np.concatenate([latents] * 2					) if do_classifier_free_guidance else latents

																									# concat latents, mask, masked_image_latents in the channel dimension
																									UpperCAmelCase__												=     self.scheduler.scale_model_input(lowerCamelCase_		,			lowerCamelCase_					)
																									UpperCAmelCase__												=     np.concatenate([latent_model_input, image]		,			axis=1					)

																									# timestep to tensor
																									UpperCAmelCase__												=     np.array([t]		,			dtype=lowerCamelCase_					)

																									# predict the noise residual
																									UpperCAmelCase__												=     self.unet(
																									    sample=lowerCamelCase_		,			timestep=lowerCamelCase_		,			encoder_hidden_states=lowerCamelCase_		,			class_labels=noise_level.astype(np.intaa					)		,			)[0]

																									# perform guidance
																									if do_classifier_free_guidance:
																															UpperCAmelCase__						,					UpperCAmelCase__												=     np.split(lowerCamelCase_		,			2					)
																									UpperCAmelCase__												=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																									# compute the previous noisy sample x_t -> x_t-1
																									UpperCAmelCase__												=     self.scheduler.step(
																									    torch.from_numpy(lowerCamelCase_					)		,			lowerCamelCase_		,			lowerCamelCase_		,			**lowerCamelCase_					).prev_sample

																									# call the callback, if provided
																									if i == len(lowerCamelCase_					) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
																															progress_bar.update()
																															if callback is not None and i % callback_steps == 0:
																																					callback(lowerCamelCase_		,			lowerCamelCase_		,			lowerCamelCase_					)

        # 10. Post-processing
													UpperCAmelCase__												=     self.decode_latents(latents.float()					)

													# 11. Convert to PIL
													if output_type == "pil":
																			UpperCAmelCase__												=     self.numpy_to_pil(lowerCamelCase_					)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  Tuple		,			lowerCamelCase_			:  List[Any]					):
													UpperCAmelCase__												=     1 / 0.08333 * latents
													UpperCAmelCase__												=     self.vae(latent_sample=lowerCamelCase_					)[0]
													UpperCAmelCase__												=     np.clip(image / 2 + 0.5		,			0		,			1					)
													UpperCAmelCase__												=     image.transpose((0, 2, 3, 1)					)
													return image






							def        lowerCAmelCase  (    self			:  Optional[Any]		,			lowerCamelCase_			:  Union[str, List[str]]		,			lowerCamelCase_			:  Union[str, Any]		,			lowerCamelCase_			:  Optional[int]		,			lowerCamelCase_			:  bool		,			lowerCamelCase_			:  Optional[str]		,			lowerCamelCase_			:  Optional[torch.FloatTensor] = None		,			lowerCamelCase_			:  Optional[torch.FloatTensor] = None		,			):
													if prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     1
													elif prompt is not None and isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																			UpperCAmelCase__												=     len(lowerCamelCase_					)
													else:
																			UpperCAmelCase__												=     prompt_embeds.shape[0]

													if prompt_embeds is None:
																			UpperCAmelCase__												=     self.tokenizer(
																			    lowerCamelCase_		,			padding='''max_length'''		,			max_length=self.tokenizer.model_max_length		,			truncation=lowerCamelCase_		,			return_tensors='''pt'''		,			)
																			UpperCAmelCase__												=     text_inputs.input_ids
																			UpperCAmelCase__												=     self.tokenizer(lowerCamelCase_		,			padding='''longest'''		,			return_tensors='''pt'''					).input_ids

																			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																			    lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     self.tokenizer.batch_decode(
																									    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
																									logger.warning(
																									    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)

																			# no positional arguments to text_encoder
																			UpperCAmelCase__												=     self.text_encoder(
																			    input_ids=text_input_ids.int().to(lowerCamelCase_					)		,			)
																			UpperCAmelCase__												=     prompt_embeds[0]

													UpperCAmelCase__						,					UpperCAmelCase__						,					UpperCAmelCase__												=     prompt_embeds.shape
													# duplicate text embeddings for each generation per prompt, using mps friendly method
													UpperCAmelCase__												=     prompt_embeds.repeat(1		,			lowerCamelCase_					)
													UpperCAmelCase__												=     prompt_embeds.reshape(bs_embed * num_images_per_prompt		,			lowerCamelCase_		,			-1					)

													# get unconditional embeddings for classifier free guidance
													if do_classifier_free_guidance and negative_prompt_embeds is None:
																			UpperCAmelCase__												=     42
																			if negative_prompt is None:
																									UpperCAmelCase__												=     [''''''] * batch_size
																			elif type(lowerCamelCase_					) is not type(lowerCamelCase_					):
																									raise TypeError(
																									    f"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_					)} !="""
																									    f""" {type(lowerCamelCase_					)}."""					)
																			elif isinstance(lowerCamelCase_		,			lowerCamelCase_					):
																									UpperCAmelCase__												=     [negative_prompt]
																			elif batch_size != len(lowerCamelCase_					):
																									raise ValueError(
																									    f"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_					)}, but `prompt`:"""
																									    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																									    ''' the batch size of `prompt`.'''					)
																			else:
																									UpperCAmelCase__												=     negative_prompt

																			UpperCAmelCase__												=     text_input_ids.shape[-1]
																			UpperCAmelCase__												=     self.tokenizer(
																			    lowerCamelCase_		,			padding='''max_length'''		,			max_length=lowerCamelCase_		,			truncation=lowerCamelCase_		,			return_tensors='''pt'''		,			)

																			# if hasattr(uncond_input, "attention_mask"):
																			#     attention_mask = uncond_input.attention_mask.to(device)
																			# else:
																			#     attention_mask = None

																			UpperCAmelCase__												=     self.text_encoder(
																			    input_ids=uncond_input.input_ids.int().to(lowerCamelCase_					)		,			)
																			UpperCAmelCase__												=     uncond_embeddings[0]

													if do_classifier_free_guidance:
																			UpperCAmelCase__												=     uncond_embeddings.shape[1]
																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																			UpperCAmelCase__												=     uncond_embeddings.repeat(1		,			lowerCamelCase_					)
																			UpperCAmelCase__												=     uncond_embeddings.reshape(batch_size * num_images_per_prompt		,			lowerCamelCase_		,			-1					)

																			# For classifier free guidance, we need to do two forward passes.
																			# Here we concatenate the unconditional and text embeddings into a single batch
																			# to avoid doing two forward passes
																			UpperCAmelCase__												=     np.concatenate([uncond_embeddings, prompt_embeds]					)

													return prompt_embeds