






'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


SCREAMING_SNAKE_CASE__     =       'CompVis/stable-diffusion-v1-1'
SCREAMING_SNAKE_CASE__     =       'CompVis/stable-diffusion-v1-2'
SCREAMING_SNAKE_CASE__     =       'CompVis/stable-diffusion-v1-3'
SCREAMING_SNAKE_CASE__     =       'CompVis/stable-diffusion-v1-4'






class     a_						(      lowerCamelCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = True      ,		)  ->				int:



        """simple docstring"""






        super()._init_()

        UpperCamelCase      = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = StableDiffusionPipeline(
            vae=_SCREAMING_SNAKE_CASE      ,		text_encoder=_SCREAMING_SNAKE_CASE      ,		tokenizer=_SCREAMING_SNAKE_CASE      ,		unet=_SCREAMING_SNAKE_CASE      ,		scheduler=_SCREAMING_SNAKE_CASE      ,		safety_checker=_SCREAMING_SNAKE_CASE      ,		feature_extractor=_SCREAMING_SNAKE_CASE      ,		requires_safety_checker=_SCREAMING_SNAKE_CASE      ,		)

        self.register_modules(pipelinea=self.pipea      ,		pipelinea=self.pipea      ,		pipelinea=self.pipea      ,		pipelinea=self.pipea				)

  @property
  def   A__						(				self				)  ->				Dict[str, Any]:



        """simple docstring"""






        return {k: getattr(self      ,		_SCREAMING_SNAKE_CASE				) for k in self.config.keys() if not k.startswith("""_"""				)}

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE = "auto"				)  ->				Any:



        """simple docstring"""






        if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              UpperCamelCase      = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        self.enable_attention_slicing(_SCREAMING_SNAKE_CASE				)

  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 50      ,		_SCREAMING_SNAKE_CASE = 7.5      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = 0.0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "pil"      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				List[Any]:



        """simple docstring"""






        return self.pipea(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 50      ,		_SCREAMING_SNAKE_CASE = 7.5      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = 0.0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "pil"      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				str:



        """simple docstring"""






        return self.pipea(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 50      ,		_SCREAMING_SNAKE_CASE = 7.5      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = 0.0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "pil"      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        return self.pipea(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 50      ,		_SCREAMING_SNAKE_CASE = 7.5      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = 0.0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "pil"      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Union[str, Any]:



        """simple docstring"""






        return self.pipea(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)


  @torch.no_grad()
  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 512      ,		_SCREAMING_SNAKE_CASE = 50      ,		_SCREAMING_SNAKE_CASE = 7.5      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		_SCREAMING_SNAKE_CASE = 0.0      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = "pil"      ,		_SCREAMING_SNAKE_CASE = True      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = 1      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				int:



        """simple docstring"""






        UpperCamelCase      = """cuda""" if torch.cuda.is_available() else """cpu"""
        self.to(_SCREAMING_SNAKE_CASE				)

        # Checks if the height and width are divisible by 8 or not
        if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F"`height` and `width` must be divisible by 8 but are {height} and {width}."				)

        # Get first result from Stable Diffusion Checkpoint v1.1
        UpperCamelCase      = self.textaimg_sda_a(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        # Get first result from Stable Diffusion Checkpoint v1.2
        UpperCamelCase      = self.textaimg_sda_a(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        # Get first result from Stable Diffusion Checkpoint v1.3
        UpperCamelCase      = self.textaimg_sda_a(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        # Get first result from Stable Diffusion Checkpoint v1.4
        UpperCamelCase      = self.textaimg_sda_a(
            prompt=_SCREAMING_SNAKE_CASE      ,		height=_SCREAMING_SNAKE_CASE      ,		width=_SCREAMING_SNAKE_CASE      ,		num_inference_steps=_SCREAMING_SNAKE_CASE      ,		guidance_scale=_SCREAMING_SNAKE_CASE      ,		negative_prompt=_SCREAMING_SNAKE_CASE      ,		num_images_per_prompt=_SCREAMING_SNAKE_CASE      ,		eta=_SCREAMING_SNAKE_CASE      ,		generator=_SCREAMING_SNAKE_CASE      ,		latents=_SCREAMING_SNAKE_CASE      ,		output_type=_SCREAMING_SNAKE_CASE      ,		return_dict=_SCREAMING_SNAKE_CASE      ,		callback=_SCREAMING_SNAKE_CASE      ,		callback_steps=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)

        # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
        return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]				)








'''simple docstring'''

import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				)->			Tuple:
      # Get the sagemaker specific mp parameters from smp_options variable.
      UpperCamelCase      = os.getenv("""SM_HP_MP_PARAMETERS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "partitions" is included, it is required for model parallel.
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if "partitions" not in smp_options:
                  return False
      except json.JSONDecodeError:
            return False

      # Get the sagemaker specific framework parameters from mpi_options variable.
      UpperCamelCase      = os.getenv("""SM_FRAMEWORK_PARAMS"""		,						"""{}"""   )
      try:
            # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
            UpperCamelCase      = json.loads(__UpperCamelCase   )
            if not mpi_options.get("""sagemaker_mpi_enabled"""		,						__UpperCamelCase   ):
                  return False
      except json.JSONDecodeError:
            return False
      # Lastly, check if the `smdistributed` module is present.
      return importlib.util.find_spec("""smdistributed"""   ) is not None


if is_sagemaker_model_parallel_available():
     import smdistributed.modelparallel.torch as smp

     smp.init()






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				field(
      default=""""""     ,	metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}     ,	)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        super().__post_init__()
        warnings.warn(
            """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
            """`TrainingArguments` instead."""      ,		_SCREAMING_SNAKE_CASE      ,		)

  @cached_property
  def   A__						(				self				)  ->				"torch.device":



        """simple docstring"""






        logger.info("""PyTorch: setting up devices"""				)
        if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
              logger.warning(
                  """torch.distributed process group is initialized, but local_rank == -1. """
                  """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""				)
        if self.no_cuda:
              UpperCamelCase      = torch.device("""cpu"""				)
              UpperCamelCase      = 0
        elif is_sagemaker_model_parallel_available():
              UpperCamelCase      = smp.local_rank()
              UpperCamelCase      = torch.device("""cuda"""      ,		_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = 1
        elif is_sagemaker_dp_enabled():
              import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

              torch.distributed.init_process_group(backend="""smddp"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""				)				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1
        elif self.local_rank == -1:
              # if n_gpu is > 1 we'll use nn.DataParallel.
              # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
              # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
              # trigger an error that a device index is missing. Index 0 takes into account the
              # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
              # will use the first GPU in that env, i.e. GPU#1
              UpperCamelCase      = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""				)
              # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
              # the default value.
              UpperCamelCase      = torch.cuda.device_count()
        else:
              # Here, we'll use torch.distributed.
              # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
              if not torch.distributed.is_initialized():
                    torch.distributed.init_process_group(backend="""nccl"""      ,		timeout=self.ddp_timeout_delta				)
              UpperCamelCase      = torch.device("""cuda"""      ,		self.local_rank				)
              UpperCamelCase      = 1

        if device.type == "cuda":
              torch.cuda.set_device(_SCREAMING_SNAKE_CASE				)

        return device

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        if is_sagemaker_model_parallel_available():
              return smp.dp_size()

        return super().world_size

  @property
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        return not is_sagemaker_model_parallel_available()


  @property
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        return False
