






'''simple docstring'''

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger()






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = len(list(m.modules()				)				) == 1 or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.Convad				) or isinstance(_SCREAMING_SNAKE_CASE      ,		nn.BatchNormad				)
        if has_not_submodules:
              self.traced.append(_SCREAMING_SNAKE_CASE				)

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        for m in self.module.modules():
              self.handles.append(m.register_forward_hook(self._forward_hook				)				)
        self.module(_SCREAMING_SNAKE_CASE				)
        [x.remove() for x in self.handles]
        return self


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



        """simple docstring"""






        return list(filter(lambda _SCREAMING_SNAKE_CASE				: len(list(x.state_dict().keys()				)				) > 0      ,		self.traced				)				)






@dataclass
class     a_						:
  lowercase							=				42
  lowercase							=				42
  lowercase							=				1
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				field(default_factory=lowerCamelCase    )
  lowercase							=				True

  def __call__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = Tracker(self.dest				)(_SCREAMING_SNAKE_CASE				).parametrized
        UpperCamelCase      = Tracker(self.src				)(_SCREAMING_SNAKE_CASE				).parametrized

        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.src_skip      ,		_SCREAMING_SNAKE_CASE				)				)
        UpperCamelCase      = list(filter(lambda _SCREAMING_SNAKE_CASE				: type(_SCREAMING_SNAKE_CASE				) not in self.dest_skip      ,		_SCREAMING_SNAKE_CASE				)				)

        if len(_SCREAMING_SNAKE_CASE				) != len(_SCREAMING_SNAKE_CASE				) and self.raise_if_mismatch:
              raise Exception(
                  F"Numbers of operations are different. Source module has {len(_SCREAMING_SNAKE_CASE				)} operations while"
                  F" destination module has {len(_SCREAMING_SNAKE_CASE				)}."				)

        for dest_m, src_m in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
              dest_m.load_state_dict(src_m.state_dict()				)
              if self.verbose == 1:
                    print(F"Transfered from={src_m} to={dest_m}"				)






class     a_						(      nn.Module    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Tuple:



        """simple docstring"""






        super().__init__()

        UpperCamelCase      = []
        # - get the stem
        feature_blocks.append(("""conv1""", model.stem)				)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
              assert k.startswith("""block"""				), F"Unexpected layer name {k}"
              UpperCamelCase      = len(_SCREAMING_SNAKE_CASE				) + 1
              feature_blocks.append((F"res{block_index}", v)				)

        UpperCamelCase      = nn.ModuleDict(_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        return get_trunk_forward_outputs(
            _SCREAMING_SNAKE_CASE      ,		out_feat_keys=_SCREAMING_SNAKE_CASE      ,		feature_blocks=self._feature_blocks      ,		)






class     a_						(      lowerCamelCase    ):

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = x.split("""-"""				)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)


  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Callable[[], Tuple[nn.Module, Dict]]:



        """simple docstring"""






        if x not in self:
              UpperCamelCase      = self.convert_name_to_timm(_SCREAMING_SNAKE_CASE				)
              UpperCamelCase      = partial(lambda: (timm.create_model(_SCREAMING_SNAKE_CASE      ,		pretrained=_SCREAMING_SNAKE_CASE				).eval(), None)				)

        else:
              UpperCamelCase      = super().__getitem__(_SCREAMING_SNAKE_CASE				)

        return val






class     a_						(      lowerCamelCase    ):

  def __getitem__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Callable[[], nn.Module]:



        """simple docstring"""






        if "seer" in x and "in1k" not in x:
              UpperCamelCase      = RegNetModel
        else:
              UpperCamelCase      = RegNetForImageClassification
        return val







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Tuple:
      for from_key, to_key in keys:
            UpperCamelCase      = from_state_dict[from_key].clone()
            print(F"Copied key={from_key} to={to_key}"   )
      return to_state_dict







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = True		,						)->			List[Any]:
      print(F"Converting {name}..."   )
      with torch.no_grad():
            UpperCamelCase  ,UpperCamelCase      = from_model_func()
            UpperCamelCase      = our_model_func(__UpperCamelCase   ).eval()
            UpperCamelCase      = ModuleTransfer(src=__UpperCamelCase		,						dest=__UpperCamelCase		,						raise_if_mismatch=__UpperCamelCase   )
            UpperCamelCase      = torch.randn((1, 3, 224, 224)   )
            module_transfer(__UpperCamelCase   )

      if from_state_dict is not None:
            UpperCamelCase      = []
            # for seer - in1k finetuned we have to manually copy the head
            if "seer" in name and "in1k" in name:
                  UpperCamelCase      = [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
            UpperCamelCase      = manually_copy_vissl_head(__UpperCamelCase		,						our_model.state_dict()		,						__UpperCamelCase   )
            our_model.load_state_dict(__UpperCamelCase   )

      UpperCamelCase      = our_model(__UpperCamelCase		,						output_hidden_states=__UpperCamelCase   )
      UpperCamelCase      = (
          our_outputs.logits if isinstance(__UpperCamelCase		,						__UpperCamelCase   ) else our_outputs.last_hidden_state
      )

      UpperCamelCase      = from_model(__UpperCamelCase   )
      UpperCamelCase      = from_output[-1] if type(__UpperCamelCase   ) is list else from_output

      # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
      if "seer" in name and "in1k" in name:
            UpperCamelCase      = our_outputs.hidden_states[-1]

      assert torch.allclose(__UpperCamelCase		,						__UpperCamelCase   ), "The model logits don't match the original one."

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / name		,						commit_message="""Add model"""		,						use_temp_dir=__UpperCamelCase		,						)

            UpperCamelCase      = 224 if """seer""" not in name else 384
            # we can use the convnext one
            UpperCamelCase      = AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""		,						size=__UpperCamelCase   )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / name		,						commit_message="""Add image processor"""		,						use_temp_dir=__UpperCamelCase		,						)

            print(F"Pushed {name}"   )







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase = None		,						__UpperCamelCase = True   )->			int:
      UpperCamelCase      = """imagenet-1k-id2label.json"""
      UpperCamelCase      = 1000
      UpperCamelCase      = (1, num_labels)

      UpperCamelCase      = """huggingface/label-files"""
      UpperCamelCase      = num_labels
      UpperCamelCase      = json.load(open(cached_download(hf_hub_url(__UpperCamelCase		,						__UpperCamelCase		,						repo_type="""dataset"""   )   )		,						"""r"""   )   )
      UpperCamelCase      = {int(__UpperCamelCase   ): v for k, v in idalabel.items()}

      UpperCamelCase      = idalabel
      UpperCamelCase      = {v: k for k, v in idalabel.items()}

      UpperCamelCase      = partial(__UpperCamelCase		,						num_labels=__UpperCamelCase		,						idalabel=__UpperCamelCase		,						labelaid=__UpperCamelCase   )

      UpperCamelCase      = {
          """regnet-x-002""": ImageNetPreTrainedConfig(
              depths=[1, 1, 4, 7]		,						hidden_sizes=[24, 56, 152, 368]		,						groups_width=8		,						layer_type="""x"""   ),
          """regnet-x-004""": ImageNetPreTrainedConfig(
              depths=[1, 2, 7, 12]		,						hidden_sizes=[32, 64, 160, 384]		,						groups_width=16		,						layer_type="""x"""   ),
          """regnet-x-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 5, 7]		,						hidden_sizes=[48, 96, 240, 528]		,						groups_width=24		,						layer_type="""x"""   ),
          """regnet-x-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 5]		,						hidden_sizes=[64, 128, 288, 672]		,						groups_width=16		,						layer_type="""x"""   ),
          """regnet-x-016""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 2]		,						hidden_sizes=[72, 168, 408, 912]		,						groups_width=24		,						layer_type="""x"""   ),
          """regnet-x-032""": ImageNetPreTrainedConfig(
              depths=[2, 6, 15, 2]		,						hidden_sizes=[96, 192, 432, 1008]		,						groups_width=48		,						layer_type="""x"""   ),
          """regnet-x-040""": ImageNetPreTrainedConfig(
              depths=[2, 5, 14, 2]		,						hidden_sizes=[80, 240, 560, 1360]		,						groups_width=40		,						layer_type="""x"""   ),
          """regnet-x-064""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]		,						hidden_sizes=[168, 392, 784, 1624]		,						groups_width=56		,						layer_type="""x"""   ),
          """regnet-x-080""": ImageNetPreTrainedConfig(
              depths=[2, 5, 15, 1]		,						hidden_sizes=[80, 240, 720, 1920]		,						groups_width=120		,						layer_type="""x"""   ),
          """regnet-x-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]		,						hidden_sizes=[224, 448, 896, 2240]		,						groups_width=112		,						layer_type="""x"""   ),
          """regnet-x-160""": ImageNetPreTrainedConfig(
              depths=[2, 6, 13, 1]		,						hidden_sizes=[256, 512, 896, 2048]		,						groups_width=128		,						layer_type="""x"""   ),
          """regnet-x-320""": ImageNetPreTrainedConfig(
              depths=[2, 7, 13, 1]		,						hidden_sizes=[336, 672, 1344, 2520]		,						groups_width=168		,						layer_type="""x"""   ),
          # y variant
          """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]		,						hidden_sizes=[24, 56, 152, 368]		,						groups_width=8   ),
          """regnet-y-004""": ImageNetPreTrainedConfig(
              depths=[1, 3, 6, 6]		,						hidden_sizes=[48, 104, 208, 440]		,						groups_width=8   ),
          """regnet-y-006""": ImageNetPreTrainedConfig(
              depths=[1, 3, 7, 4]		,						hidden_sizes=[48, 112, 256, 608]		,						groups_width=16   ),
          """regnet-y-008""": ImageNetPreTrainedConfig(
              depths=[1, 3, 8, 2]		,						hidden_sizes=[64, 128, 320, 768]		,						groups_width=16   ),
          """regnet-y-016""": ImageNetPreTrainedConfig(
              depths=[2, 6, 17, 2]		,						hidden_sizes=[48, 120, 336, 888]		,						groups_width=24   ),
          """regnet-y-032""": ImageNetPreTrainedConfig(
              depths=[2, 5, 13, 1]		,						hidden_sizes=[72, 216, 576, 1512]		,						groups_width=24   ),
          """regnet-y-040""": ImageNetPreTrainedConfig(
              depths=[2, 6, 12, 2]		,						hidden_sizes=[128, 192, 512, 1088]		,						groups_width=64   ),
          """regnet-y-064""": ImageNetPreTrainedConfig(
              depths=[2, 7, 14, 2]		,						hidden_sizes=[144, 288, 576, 1296]		,						groups_width=72   ),
          """regnet-y-080""": ImageNetPreTrainedConfig(
              depths=[2, 4, 10, 1]		,						hidden_sizes=[168, 448, 896, 2016]		,						groups_width=56   ),
          """regnet-y-120""": ImageNetPreTrainedConfig(
              depths=[2, 5, 11, 1]		,						hidden_sizes=[224, 448, 896, 2240]		,						groups_width=112   ),
          """regnet-y-160""": ImageNetPreTrainedConfig(
              depths=[2, 4, 11, 1]		,						hidden_sizes=[224, 448, 1232, 3024]		,						groups_width=112   ),
          """regnet-y-320""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]		,						hidden_sizes=[232, 696, 1392, 3712]		,						groups_width=232   ),
          # models created by SEER -> https://arxiv.org/abs/2202.08360
          """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1]		,						hidden_sizes=[232, 696, 1392, 3712]		,						groups_width=232   ),
          """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1]		,						hidden_sizes=[328, 984, 1968, 4920]		,						groups_width=328   ),
          """regnet-y-1280-seer""": RegNetConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[528, 1056, 2904, 7392]		,						groups_width=264   ),
          """regnet-y-2560-seer""": RegNetConfig(
              depths=[3, 7, 16, 1]		,						hidden_sizes=[640, 1696, 2544, 5088]		,						groups_width=640   ),
          """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[2020, 4040, 11110, 28280]		,						groups_width=1010   ),
          # finetuned on imagenet
          """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]		,						hidden_sizes=[232, 696, 1392, 3712]		,						groups_width=232   ),
          """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 5, 12, 1]		,						hidden_sizes=[328, 984, 1968, 4920]		,						groups_width=328   ),
          """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[528, 1056, 2904, 7392]		,						groups_width=264   ),
          """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[3, 7, 16, 1]		,						hidden_sizes=[640, 1696, 2544, 5088]		,						groups_width=640   ),
          """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
              depths=[2, 7, 17, 1]		,						hidden_sizes=[2020, 4040, 11110, 28280]		,						groups_width=1010   ),
      }

      UpperCamelCase      = NameToOurModelFuncMap()
      UpperCamelCase      = NameToFromModelFuncMap()
      # add seer weights logic

      def load_using_classy_vision(__UpperCamelCase		,						__UpperCamelCase   ) -> Tuple[nn.Module, Dict]:
            UpperCamelCase      = torch.hub.load_state_dict_from_url(__UpperCamelCase		,						model_dir=str(__UpperCamelCase   )		,						map_location="""cpu"""   )
            UpperCamelCase      = model_func()
            # check if we have a head, if yes add it
            UpperCamelCase      = files["""classy_state_dict"""]["""base_model"""]["""model"""]
            UpperCamelCase      = model_state_dict["""trunk"""]
            model.load_state_dict(__UpperCamelCase   )
            return model.eval(), model_state_dict["heads"]

      # pretrained
      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""		,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""		,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"""		,						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""		,						lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27		,						group_width=1010		,						w_a=1744		,						w_a=620.83		,						w_m=2.52   )   )   )		,						)

      # IN1K finetuned
      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""		,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""		,						lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""		,						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()   )		,						)

      UpperCamelCase      = partial(
          __UpperCamelCase		,						"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""		,						lambda: FakeRegNetVisslWrapper(
              RegNet(RegNetParams(depth=27		,						group_width=1010		,						w_a=1744		,						w_a=620.83		,						w_m=2.52   )   )   )		,						)

      if model_name:
            convert_weight_and_push(
                __UpperCamelCase		,						names_to_from_model_map[model_name]		,						names_to_ours_model_map[model_name]		,						names_to_config[model_name]		,						__UpperCamelCase		,						__UpperCamelCase		,						)
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      __UpperCamelCase		,						names_to_from_model_map[model_name]		,						names_to_ours_model_map[model_name]		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						)
      return config, expected_shape


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default=None,
     type=str,
     help=(
         'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
         ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=Path,
     required=True,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub',
     default=True,
     type=bool,
     required=False,
     help='If True, push model and image processor to the hub.',
 )

 SCREAMING_SNAKE_CASE__     =       parser.parse_args()

 SCREAMING_SNAKE_CASE__     =       args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''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]]				)
