'''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()
_UpperCAmelCase    :     Union[str, Any]         =       logging.get_logger()







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   field(default_factory=_UpperCamelCase			)
   __lowerCAmelCase         =   field(default_factory=_UpperCamelCase			)
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Union[str, Any], UpperCamelCase__      :			Tensor, UpperCamelCase__      :			Tensor     )  ->			Any:
        _A    		=  len(list(m.modules()     )     ) == 1 or isinstance(UpperCamelCase__, nn.Convad     ) or isinstance(UpperCamelCase__, nn.BatchNormad     )
        if has_not_submodules:
             self.traced.append(UpperCamelCase__     )
   def __call__(      self      :			List[str], UpperCamelCase__      :			Tensor     )  ->			Union[str, Any]:
        for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook     )     )
        self.module(UpperCamelCase__     )
        [x.remove() for x in self.handles]
        return self

   @property
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[Any]:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda UpperCamelCase__     : len(list(x.state_dict().keys()     )     ) > 0, self.traced     )     )







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   42
   __lowerCAmelCase         =   1
   __lowerCAmelCase         =   field(default_factory=_UpperCamelCase			)
   __lowerCAmelCase         =   field(default_factory=_UpperCamelCase			)
   __lowerCAmelCase         =   True
   def __call__(      self      :			List[Any], UpperCamelCase__      :			Tensor     )  ->			int:
        _A    		=  Tracker(self.dest     )(UpperCamelCase__     ).parametrized
        _A    		=  Tracker(self.src     )(UpperCamelCase__     ).parametrized

        _A    		=  list(filter(lambda UpperCamelCase__     : type(UpperCamelCase__     ) not in self.src_skip, UpperCamelCase__     )     )
        _A    		=  list(filter(lambda UpperCamelCase__     : type(UpperCamelCase__     ) not in self.dest_skip, UpperCamelCase__     )     )

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

        for dest_m, src_m in zip(UpperCamelCase__, UpperCamelCase__     ):
             dest_m.load_state_dict(src_m.state_dict()     )
             if self.verbose == 1:
                  print(f'Transfered from={src_m} to={dest_m}'     )







class lowercase_       (		nn.Module			):







   """simple docstring"""

   def __init__(      self      :			Any, UpperCamelCase__      :			nn.Module     )  ->			Dict:
        super().__init__()

        _A    		=  []
        # - 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}'
             _A    		=  len(UpperCamelCase__     ) + 1
             feature_blocks.append((f'res{block_index}', v)     )

        _A    		=  nn.ModuleDict(UpperCamelCase__     )

   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			Tensor     )  ->			Tuple:
        return get_trunk_forward_outputs(
            UpperCamelCase__, out_feat_keys=UpperCamelCase__, feature_blocks=self._feature_blocks, )







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			Tuple, UpperCamelCase__      :			str     )  ->			str:
        _A    		=  x.split('-'     )
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]     )

   def __getitem__(      self      :			List[str], UpperCamelCase__      :			str     )  ->			Callable[[], Tuple[nn.Module, Dict]]:
        # default to timm!
        if x not in self:
             _A    		=  self.convert_name_to_timm(UpperCamelCase__     )
             _A    		=  partial(lambda: (timm.create_model(UpperCamelCase__, pretrained=UpperCamelCase__     ).eval(), None)     )

        else:
             _A    		=  super().__getitem__(UpperCamelCase__     )

        return val







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __getitem__(      self      :			Optional[int], UpperCamelCase__      :			str     )  ->			Callable[[], nn.Module]:
        if "seer" in x and "in1k" not in x:
             _A    		=  RegNetModel
        else:
             _A    		=  RegNetForImageClassification
        return val






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]   ,		__snake_case							:			Optional[Any]   ,		__snake_case							:			List[Tuple[str, str]]				):
     for from_key, to_key in keys:
          _A    		=  from_state_dict[from_key].clone()
          print(F'Copied key={from_key} to={to_key}'				)
     return to_state_dict





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Callable[[], nn.Module]   ,		__snake_case							:			Callable[[], nn.Module]   ,		__snake_case							:			RegNetConfig   ,		__snake_case							:			Path   ,		__snake_case							:			bool = True   ,		):
     print(F'Converting {name}...'				)
     with torch.no_grad():
          _A		,       _A    		=  from_model_func()
          _A    		=  our_model_func(__snake_case				).eval()
          _A    		=  ModuleTransfer(src=__snake_case   ,		dest=__snake_case   ,		raise_if_mismatch=__snake_case				)
          _A    		=  torch.randn((1, 3, 2_2_4, 2_2_4)				)
          module_transfer(__snake_case				)

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

     _A    		=  our_model(__snake_case   ,		output_hidden_states=__snake_case				)
     _A    		=  (
         our_outputs.logits if isinstance(__snake_case   ,		__snake_case				) else our_outputs.last_hidden_state
     )

     _A    		=  from_model(__snake_case				)
     _A    		=  from_output[-1] if type(__snake_case				) 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:
          _A    		=  our_outputs.hidden_states[-1]

     assert torch.allclose(__snake_case   ,		__snake_case				), "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=__snake_case   ,		)

          _A    		=  2_2_4 if 'seer' not in name else 3_8_4
          # we can use the convnext one
          _A    		=  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'   ,		size=__snake_case				)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name   ,		commit_message='Add image processor'   ,		use_temp_dir=__snake_case   ,		)

          print(F'Pushed {name}'				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Path   ,		__snake_case							:			str = None   ,		__snake_case							:			bool = True				):
     _A    		=  'imagenet-1k-id2label.json'
     _A    		=  1_0_0_0
     _A    		=  (1, num_labels)

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

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

     _A    		=  partial(__snake_case   ,		num_labels=__snake_case   ,		idalabel=__snake_case   ,		labelaid=__snake_case				)

     _A    		=  {
         'regnet-x-002': ImageNetPreTrainedConfig(
             depths=[1, 1, 4, 7]   ,		hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]   ,		groups_width=8   ,		layer_type='x'				),
         'regnet-x-004': ImageNetPreTrainedConfig(
             depths=[1, 2, 7, 1_2]   ,		hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]   ,		groups_width=1_6   ,		layer_type='x'				),
         'regnet-x-006': ImageNetPreTrainedConfig(
             depths=[1, 3, 5, 7]   ,		hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]   ,		groups_width=2_4   ,		layer_type='x'				),
         'regnet-x-008': ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 5]   ,		hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]   ,		groups_width=1_6   ,		layer_type='x'				),
         'regnet-x-016': ImageNetPreTrainedConfig(
             depths=[2, 4, 1_0, 2]   ,		hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]   ,		groups_width=2_4   ,		layer_type='x'				),
         'regnet-x-032': ImageNetPreTrainedConfig(
             depths=[2, 6, 1_5, 2]   ,		hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]   ,		groups_width=4_8   ,		layer_type='x'				),
         'regnet-x-040': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_4, 2]   ,		hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]   ,		groups_width=4_0   ,		layer_type='x'				),
         'regnet-x-064': ImageNetPreTrainedConfig(
             depths=[2, 4, 1_0, 1]   ,		hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]   ,		groups_width=5_6   ,		layer_type='x'				),
         'regnet-x-080': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_5, 1]   ,		hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]   ,		groups_width=1_2_0   ,		layer_type='x'				),
         'regnet-x-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_1, 1]   ,		hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]   ,		groups_width=1_1_2   ,		layer_type='x'				),
         'regnet-x-160': ImageNetPreTrainedConfig(
             depths=[2, 6, 1_3, 1]   ,		hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]   ,		groups_width=1_2_8   ,		layer_type='x'				),
         'regnet-x-320': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_3, 1]   ,		hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]   ,		groups_width=1_6_8   ,		layer_type='x'				),
         # y variant
         'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]   ,		hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]   ,		groups_width=8				),
         'regnet-y-004': ImageNetPreTrainedConfig(
             depths=[1, 3, 6, 6]   ,		hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]   ,		groups_width=8				),
         'regnet-y-006': ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 4]   ,		hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]   ,		groups_width=1_6				),
         'regnet-y-008': ImageNetPreTrainedConfig(
             depths=[1, 3, 8, 2]   ,		hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]   ,		groups_width=1_6				),
         'regnet-y-016': ImageNetPreTrainedConfig(
             depths=[2, 6, 1_7, 2]   ,		hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]   ,		groups_width=2_4				),
         'regnet-y-032': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_3, 1]   ,		hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]   ,		groups_width=2_4				),
         'regnet-y-040': ImageNetPreTrainedConfig(
             depths=[2, 6, 1_2, 2]   ,		hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]   ,		groups_width=6_4				),
         'regnet-y-064': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_4, 2]   ,		hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]   ,		groups_width=7_2				),
         'regnet-y-080': ImageNetPreTrainedConfig(
             depths=[2, 4, 1_0, 1]   ,		hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]   ,		groups_width=5_6				),
         'regnet-y-120': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_1, 1]   ,		hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]   ,		groups_width=1_1_2				),
         'regnet-y-160': ImageNetPreTrainedConfig(
             depths=[2, 4, 1_1, 1]   ,		hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]   ,		groups_width=1_1_2				),
         'regnet-y-320': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_2, 1]   ,		hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]   ,		groups_width=2_3_2				),
         # models created by SEER -> https://arxiv.org/abs/2202.08360
         'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 1_2, 1]   ,		hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]   ,		groups_width=2_3_2				),
         'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 1_2, 1]   ,		hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]   ,		groups_width=3_2_8				),
         'regnet-y-1280-seer': RegNetConfig(
             depths=[2, 7, 1_7, 1]   ,		hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]   ,		groups_width=2_6_4				),
         'regnet-y-2560-seer': RegNetConfig(
             depths=[3, 7, 1_6, 1]   ,		hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]   ,		groups_width=6_4_0				),
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_7, 1]   ,		hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]   ,		groups_width=1_0_1_0				),
         # finetuned on imagenet
         'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_2, 1]   ,		hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]   ,		groups_width=2_3_2				),
         'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 5, 1_2, 1]   ,		hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]   ,		groups_width=3_2_8				),
         'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_7, 1]   ,		hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]   ,		groups_width=2_6_4				),
         'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
             depths=[3, 7, 1_6, 1]   ,		hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]   ,		groups_width=6_4_0				),
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 1_7, 1]   ,		hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]   ,		groups_width=1_0_1_0				),
     }

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

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

     # pretrained
     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'   ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,		)

     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'   ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,		)

     _A    		=  partial(
         __snake_case   ,		'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()				)   ,		)

     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'   ,		lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=2_7   ,		group_width=1_0_1_0   ,		w_a=1_7_4_4   ,		w_a=6_20.83   ,		w_m=2.52				)				)				)   ,		)

     # IN1K finetuned
     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,		)

     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,		lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)   ,		)

     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'   ,		lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)   ,		)

     _A    		=  partial(
         __snake_case   ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'   ,		lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=2_7   ,		group_width=1_0_1_0   ,		w_a=1_7_4_4   ,		w_a=6_20.83   ,		w_m=2.52				)				)				)   ,		)

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


if __name__ == "__main__":
   _UpperCAmelCase    :     Optional[int]         =       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.''',
   )

   _UpperCAmelCase    :     Union[str, Any]         =       parser.parse_args()

   _UpperCAmelCase    :     Path         =       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 Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_UpperCamelCase											=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
          f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
          'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
          ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
          ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
          ' file'
      )
      deprecate('steps_offset!=1'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          f'The configuration file of this scheduler: {scheduler} has not set the configuration'
          ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
          ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
          ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
          ' Hub, it would be very nice if you could open a Pull request for the'
          ' `scheduler/scheduler_config.json` file'
      )
      deprecate('skip_prk_steps not set'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    if safety_checker is None:
      logger.warning(
          f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
          ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
          ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
          ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
          ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
          ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'  )

    self.register_modules(
        segmentation_model=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'  )

    __lowerCamelCase			:			Dict       			=					torch.device('cuda'  )

    for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
      if cpu_offloaded_model is not None:
        cpu_offload(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_hf_hook'  )
          and hasattr(module._hf_hook     ,      'execution_device'  )
          and module._hf_hook.execution_device is not None
      ):
        return torch.device(module._hf_hook.execution_device  )
    return self.device




  @torch.no_grad()
  def __call__(					self :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					StableDiffusionInpaintPipeline(
        vae=self.vae     ,      text_encoder=self.text_encoder     ,      tokenizer=self.tokenizer     ,      unet=self.unet     ,      scheduler=self.scheduler     ,      safety_checker=self.safety_checker     ,      feature_extractor=self.feature_extractor     ,      )
    return inpainting_pipeline(
        prompt=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






