





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_  							=				logging.get_logger()






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							nn.Module
   a		:							List[nn.Module]     	=	field(default_factory=__lowerCamelCase       )
   a		:							list     	=	field(default_factory=__lowerCamelCase       )




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   len(list(m.modules()							)							) == 1 or isinstance(__magic_name__,	nn.Convad							) or isinstance(__magic_name__,	nn.BatchNormad							)
       if has_not_submodules:
           self.traced.append(__magic_name__							)




   def __call__(					self,	__magic_name__							)				->   Optional[Any]:






       """simple docstring"""
       for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook							)							)
       self.module(__magic_name__							)
       [x.remove() for x in self.handles]
       return self




   @property
   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda __magic_name__							: len(list(x.state_dict().keys()							)							) > 0,	self.traced							)							)






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							nn.Module
   a		:							nn.Module
   a		:							int     	=	1
   a		:							List     	=	field(default_factory=__lowerCamelCase       )
   a		:							List     	=	field(default_factory=__lowerCamelCase       )
   a		:							bool     	=	True




   def __call__(					self,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Any      =   Tracker(self.dest							)(__magic_name__							).parametrized
       UpperCamelCase__							:       Optional[Any]      =   Tracker(self.src							)(__magic_name__							).parametrized

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

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

       for dest_m, src_m in zip(__magic_name__,	__magic_name__							):
           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,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       super().__init__()

       UpperCamelCase__							:       List[Tuple[str, nn.Module]]      =   []
       # - 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__							:       List[Any]      =   len(__magic_name__							) + 1
           feature_blocks.append((f"res{block_index}", v)							)

       UpperCamelCase__							:       List[str]      =   nn.ModuleDict(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   str:






       """simple docstring"""
       return get_trunk_forward_outputs(
           __magic_name__,	out_feat_keys=__magic_name__,	feature_blocks=self._feature_blocks,	)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self,	__magic_name__							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   x.split('''-'''							)
       return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)




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






       """simple docstring"""
       # default to timm!
       if x not in self:
           UpperCamelCase__							:       Optional[int]      =   self.convert_name_to_timm(__magic_name__							)
           UpperCamelCase__							:       Optional[int]      =   partial(lambda: (timm.create_model(__magic_name__,	pretrained=__magic_name__							).eval(), None)							)

       else:
           UpperCamelCase__							:       str      =   super().__getitem__(__magic_name__							)

       return val






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


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






       """simple docstring"""
       if "seer" in x and "in1k" not in x:
           UpperCamelCase__							:       Tuple      =   RegNetModel
       else:
           UpperCamelCase__							:       Optional[Any]      =   RegNetForImageClassification
       return val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[Tuple[str, str]]   )   ->			Optional[int]:
    for from_key, to_key in keys:
        UpperCamelCase__							:       Dict      =   from_state_dict[from_key].clone()
        print(f"Copied key={from_key} to={to_key}"   )
    return to_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    Callable[[], nn.Module]	,     __UpperCAmelCase:    Callable[[], nn.Module]	,     __UpperCAmelCase:    RegNetConfig	,     __UpperCAmelCase:    Path	,     __UpperCAmelCase:    bool = True	,     )   ->			Optional[int]:
    print(f"Converting {name}..."   )
    with torch.no_grad():
        UpperCamelCase__     ,UpperCamelCase__							:       Optional[int]      =   from_model_func()
        UpperCamelCase__							:       Union[str, Any]      =   our_model_func(__UpperCAmelCase   ).eval()
        UpperCamelCase__							:       Optional[int]      =   ModuleTransfer(src=__UpperCAmelCase	,     dest=__UpperCAmelCase	,     raise_if_mismatch=__UpperCAmelCase   )
        UpperCamelCase__							:       int      =   torch.randn((1, 3, 224, 224)   )
        module_transfer(__UpperCAmelCase   )

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

    UpperCamelCase__							:       Optional[int]      =   our_model(__UpperCAmelCase	,     output_hidden_states=__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   (
        our_outputs.logits if isinstance(__UpperCAmelCase	,     __UpperCAmelCase   ) else our_outputs.last_hidden_state
    )

    UpperCamelCase__							:       Dict      =   from_model(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   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__							:       Any      =   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__							:       Tuple      =   224 if '''seer''' not in name else 384
        # we can use the convnext one
        UpperCamelCase__							:       Optional[Any]      =   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 	lowerCAmelCase_					(						__UpperCAmelCase:    Path	,     __UpperCAmelCase:    str = None	,     __UpperCAmelCase:    bool = True   )   ->			Optional[int]:
    UpperCamelCase__							:       Dict      =   '''imagenet-1k-id2label.json'''
    UpperCamelCase__							:       Tuple      =   1000
    UpperCamelCase__							:       str      =   (1, num_labels)

    UpperCamelCase__							:       Optional[Any]      =   '''huggingface/label-files'''
    UpperCamelCase__							:       str      =   num_labels
    UpperCamelCase__							:       List[str]      =   json.load(open(cached_download(hf_hub_url(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )   )	,     '''r'''   )   )
    UpperCamelCase__							:       Union[str, Any]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}

    UpperCamelCase__							:       int      =   idalabel
    UpperCamelCase__							:       Dict      =   {v: k for k, v in idalabel.items()}

    UpperCamelCase__							:       Any      =   partial(__UpperCAmelCase	,     num_labels=__UpperCAmelCase	,     idalabel=__UpperCAmelCase	,     labelaid=__UpperCAmelCase   )

    UpperCamelCase__							:       List[str]      =   {
        '''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, 1_1110, 2_8280]	,     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, 1_1110, 2_8280]	,     groups_width=1010   ),
    }

    UpperCamelCase__							:       str      =   NameToOurModelFuncMap()
    UpperCamelCase__							:       Optional[int]      =   NameToFromModelFuncMap()
    # add seer weights logic

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

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

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

    UpperCamelCase__							:       int      =   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__							:       List[str]      =   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__							:       List[Any]      =   partial(
        __UpperCAmelCase	,     '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,     lambda: FakeRegNetVisslWrapper(RegNetYaagf()   )	,     )

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

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

    UpperCamelCase__							:       Union[str, Any]      =   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__":
  UpperCAmelCase_  							=				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_  							=				parser.parse_args()

  UpperCAmelCase_  							=				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)








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCAmelCase_  							=				False






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   pass






@nightly
@require_torch_gpu
class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




   def    UpperCamelCase__						(					self							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       int      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       int      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(__magic_name__							)
           UpperCamelCase__							:       Union[str, Any]      =   VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[Any]      =   generator.manual_seed(0							)
       UpperCamelCase__							:       str      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=2,	output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       str      =   VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion''',	torch_dtype=torch.floataa							)
       pipe.to(__magic_name__							)
       pipe.set_progress_bar_config(disable=__magic_name__							)

       UpperCamelCase__							:       Optional[int]      =   '''A painting of a squirrel eating a burger '''
       UpperCamelCase__							:       str      =   torch.manual_seed(0							)
       UpperCamelCase__							:       Optional[int]      =   pipe(
           prompt=__magic_name__,	generator=__magic_name__,	guidance_scale=7.5,	num_inference_steps=50,	output_type='''numpy'''							).images

       UpperCamelCase__							:       int      =   image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__							:       int      =   np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2

