'''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'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_UpperCAmelCase    :     List[Any]         =       False







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   pass







@slow
@require_torch_gpu
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'     )
        pipe.to(UpperCamelCase__     )
        pipe.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'     )
        _A    		=  torch.manual_seed(0     )
        _A    		=  pipe(
            image=UpperCamelCase__, generator=UpperCamelCase__, guidance_scale=7.5, num_inference_steps=50, output_type='numpy', ).images

        _A    		=  image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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



