





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor





class  lowerCAmelCase							:







  def __init__(      self  :Tuple   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :Dict=3   ,							_lowercase  :List[Any]=32   ,							_lowercase  :List[Any]=3   ,							_lowercase  :str=10   ,							_lowercase  :int=[10, 20, 30, 40]   ,							_lowercase  :Any=[1, 1, 2, 1]   ,							_lowercase  :List[Any]=True   ,							_lowercase  :Union[str, Any]=True   ,							_lowercase  :Optional[int]="relu"   ,							_lowercase  :str=3   ,							_lowercase  :int=None   ,							):






     '''simple docstring'''





     lowercase__			       =  parent
     lowercase__			       =  batch_size
     lowercase__			       =  image_size
     lowercase__			       =  num_channels
     lowercase__			       =  embeddings_size
     lowercase__			       =  hidden_sizes
     lowercase__			       =  depths
     lowercase__			       =  is_training
     lowercase__			       =  use_labels
     lowercase__			       =  hidden_act
     lowercase__			       =  num_labels
     lowercase__			       =  scope
     lowercase__			       =  len(lowerCamelCase__ )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     lowercase__			       =  None
     if self.use_labels:
        lowercase__			       =  ids_tensor([self.batch_size]   ,							self.num_labels )

     lowercase__			       =  self.get_config()
     return config, pixel_values, labels







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     return RegNetConfig(
         num_channels=self.num_channels   ,							embeddings_size=self.embeddings_size   ,							hidden_sizes=self.hidden_sizes   ,							depths=self.depths   ,							hidden_act=self.hidden_act   ,							num_labels=self.num_labels   ,							)







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :str   ,							_lowercase  :List[Any]   ,							_lowercase  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  TFRegNetModel(config=lowerCamelCase__ )
     lowercase__			       =  model(lowerCamelCase__   ,							training=lowerCamelCase__ )
     # expected last hidden states: B, C, H // 32, W // 32
     self.parent.assertEqual(
         result.last_hidden_state.shape   ,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,							)







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :int   ,							_lowercase  :Dict   ,							_lowercase  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.num_labels
     lowercase__			       =  TFRegNetForImageClassification(lowerCamelCase__ )
     lowercase__			       =  model(lowerCamelCase__   ,							labels=lowerCamelCase__   ,							training=lowerCamelCase__ )
     self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels) )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  self.prepare_config_and_inputs()
     lowercase__			       =  config_and_inputs
     lowercase__			       =  {'''pixel_values''': pixel_values}
     return config, inputs_dict





@require_tf
class  lowerCAmelCase							(       __lowerCAmelCase							,							__lowerCAmelCase							,							unittest.TestCase ):
  __lowerCamelCase		=      (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
  __lowerCamelCase		=      (
      {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
      if is_tf_available()
      else {}
  )

  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False
  __lowerCamelCase		=      False







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  TFRegNetModelTester(self )
     lowercase__			       =  ConfigTester(self   ,							config_class=lowerCamelCase__   ,							has_text_modality=lowerCamelCase__ )







  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     return







  @unittest.skip(reason="RegNet does not use inputs_embeds" )
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     pass







  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0   ,							reason="TF does not support backprop for grouped convolutions on CPU."   ,							)
  @slow
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     super().test_keras_fit()







  @unittest.skip(reason="RegNet does not support input and output embeddings" )
  def   UpperCAmelCase					(      self  :Union[str, Any] ):






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        lowercase__			       =  model_class(lowerCamelCase__ )
        lowercase__			       =  inspect.signature(model.call )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase__			       =  [*signature.parameters.keys()]

        lowercase__			       =  ['''pixel_values''']
        self.assertListEqual(arg_names[:1]   ,							lowerCamelCase__ )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCamelCase__ )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     def check_hidden_states_output(_lowercase  :Dict   ,							_lowercase  :str   ,							_lowercase  :List[str] ):
        lowercase__			       =  model_class(lowerCamelCase__ )
        lowercase__			       =  model(**self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__ )   ,							training=lowerCamelCase__ )

        lowercase__			       =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        lowercase__			       =  self.model_tester.num_stages
        self.assertEqual(len(lowerCamelCase__ )   ,							expected_num_stages + 1 )

        # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:] )   ,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,							)

     lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__			       =  ['''basic''', '''bottleneck''']
     for model_class in self.all_model_classes:
        for layer_type in layers_type:
           lowercase__			       =  layer_type
           lowercase__			       =  True
           check_hidden_states_output(lowerCamelCase__   ,							lowerCamelCase__   ,							lowerCamelCase__ )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           lowercase__			       =  True

           check_hidden_states_output(lowerCamelCase__   ,							lowerCamelCase__   ,							lowerCamelCase__ )







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs_for_common()

     def check_equivalence(_lowercase  :Optional[Any]   ,							_lowercase  :Optional[Any]   ,							_lowercase  :List[str]   ,							_lowercase  :Any={} ):
        lowercase__			       =  model(lowerCamelCase__   ,							return_dict=lowerCamelCase__   ,							**lowerCamelCase__ )
        lowercase__			       =  model(lowerCamelCase__   ,							return_dict=lowerCamelCase__   ,							**lowerCamelCase__ ).to_tuple()

        def recursive_check(_lowercase  :Optional[int]   ,							_lowercase  :Tuple ):
           if isinstance(lowerCamelCase__   ,							(List, Tuple) ):
              for tuple_iterable_value, dict_iterable_value in zip(lowerCamelCase__   ,							lowerCamelCase__ ):
                 recursive_check(lowerCamelCase__   ,							lowerCamelCase__ )
           elif tuple_object is None:
              return
           else:
              self.assertTrue(
                  all(tf.equal(lowerCamelCase__   ,							lowerCamelCase__ ) )   ,							msg=(
                      "Tuple and dict output are not equal. Difference:"
                      f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'''
                  )   ,							)

           recursive_check(lowerCamelCase__   ,							lowerCamelCase__ )

     for model_class in self.all_model_classes:
        lowercase__			       =  model_class(lowerCamelCase__ )

        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__ )
        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__ )
        check_equivalence(lowerCamelCase__   ,							lowerCamelCase__   ,							lowerCamelCase__ )

        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__   ,							return_labels=lowerCamelCase__ )
        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__   ,							return_labels=lowerCamelCase__ )
        check_equivalence(lowerCamelCase__   ,							lowerCamelCase__   ,							lowerCamelCase__ )

        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__ )
        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__ )
        check_equivalence(lowerCamelCase__   ,							lowerCamelCase__   ,							lowerCamelCase__   ,							{"output_hidden_states": True} )

        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__   ,							return_labels=lowerCamelCase__ )
        lowercase__			       =  self._prepare_for_class(lowerCamelCase__   ,							lowerCamelCase__   ,							return_labels=lowerCamelCase__ )
        check_equivalence(lowerCamelCase__   ,							lowerCamelCase__   ,							lowerCamelCase__   ,							{"output_hidden_states": True} )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     lowercase__			       =  self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )







  @slow
  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase__			       =  TFRegNetModel.from_pretrained(lowerCamelCase__ )
        self.assertIsNotNone(lowerCamelCase__ )


def _A       (   ):
   lowercase__			       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
   return image







@require_tf
@require_vision
class  lowerCAmelCase							(       unittest.TestCase ):







  @cached_property
  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     return (
         AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
         if is_vision_available()
         else None
     )







  @slow
  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     lowercase__			       =  TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

     lowercase__			       =  self.default_image_processor
     lowercase__			       =  prepare_img()
     lowercase__			       =  image_processor(images=lowerCamelCase__   ,							return_tensors="tf" )

     # forward pass
     lowercase__			       =  model(**lowerCamelCase__   ,							training=lowerCamelCase__ )

     # verify the logits
     lowercase__			       =  tf.TensorShape((1, 10_00) )
     self.assertEqual(outputs.logits.shape   ,							lowerCamelCase__ )

     lowercase__			       =  tf.constant([-0.4180, -1.5051, -3.4836] )

     tf.debugging.assert_near(outputs.logits[0, :3]   ,							lowerCamelCase__   ,							atol=1e-4 )













import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


def _A       (   __magic_name__	):
   lowercase__			       =  [tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]	)





class  lowerCAmelCase							(       lowercase_							,							lowercase_							,							lowercase_							,							unittest.TestCase ):
  __lowerCamelCase		=      StableDiffusionLatentUpscalePipeline
  __lowerCamelCase		=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      'height',
      'width',
      'cross_attention_kwargs',
      'negative_prompt_embeds',
      'prompt_embeds',
  }
  __lowerCamelCase		=      PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
  __lowerCamelCase		=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  __lowerCamelCase		=      frozenset(
      [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  __lowerCamelCase		=      frozenset([] )

  __lowerCamelCase		=      True







  @property
  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  1
     lowercase__			       =  4
     lowercase__			       =  (16, 16)

     lowercase__			       =  floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0 ) ).to(_lowercase )
     return image







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     torch.manual_seed(0 )
     lowercase__			       =  UNetaDConditionModel(
         act_fn="gelu"   ,							attention_head_dim=8   ,							norm_num_groups=_lowercase   ,							block_out_channels=[32, 32, 64, 64]   ,							time_cond_proj_dim=1_60   ,							conv_in_kernel=1   ,							conv_out_kernel=1   ,							cross_attention_dim=32   ,							down_block_types=(
             "KDownBlock2D",
             "KCrossAttnDownBlock2D",
             "KCrossAttnDownBlock2D",
             "KCrossAttnDownBlock2D",
         )   ,							in_channels=8   ,							mid_block_type=_lowercase   ,							only_cross_attention=_lowercase   ,							out_channels=5   ,							resnet_time_scale_shift="scale_shift"   ,							time_embedding_type="fourier"   ,							timestep_post_act="gelu"   ,							up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")   ,							)
     lowercase__			       =  AutoencoderKL(
         block_out_channels=[32, 32, 64, 64]   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=[
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
         ]   ,							up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]   ,							latent_channels=4   ,							)
     lowercase__			       =  EulerDiscreteScheduler(prediction_type="sample" )
     lowercase__			       =  CLIPTextConfig(
         bos_token_id=0   ,							eos_token_id=2   ,							hidden_size=32   ,							intermediate_size=37   ,							layer_norm_eps=1e-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=10_00   ,							hidden_act="quick_gelu"   ,							projection_dim=5_12   ,							)
     lowercase__			       =  CLIPTextModel(_lowercase )
     lowercase__			       =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

     lowercase__			       =  {
         "unet": model.eval(),
         "vae": vae.eval(),
         "scheduler": scheduler,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
     }

     return components







  def   UpperCAmelCase					(      self  :Dict   ,							_lowercase  :Union[str, Any]   ,							_lowercase  :int=0 ):






     '''simple docstring'''





     if str(_lowercase ).startswith("mps" ):
        lowercase__			       =  torch.manual_seed(_lowercase )
     else:
        lowercase__			       =  torch.Generator(device=_lowercase ).manual_seed(_lowercase )
     lowercase__			       =  {
         "prompt": "A painting of a squirrel eating a burger",
         "image": self.dummy_image.cpu(),
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "numpy",
     }
     return inputs







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  "cpu"

     lowercase__			       =  self.get_dummy_components()
     lowercase__			       =  self.pipeline_class(**_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )

     lowercase__			       =  self.get_dummy_inputs(_lowercase )
     lowercase__			       =  pipe(**_lowercase ).images
     lowercase__			       =  image[0, -3:, -3:, -1]

     self.assertEqual(image.shape   ,							(1, 2_56, 2_56, 3) )
     lowercase__			       =  np.array(
         [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] )
     lowercase__			       =  np.abs(image_slice.flatten() - expected_slice ).max()
     self.assertLessEqual(_lowercase   ,							1e-3 )







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     super().test_attention_slicing_forward_pass(expected_max_diff=7e-3 )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     super().test_cpu_offload_forward_pass(expected_max_diff=3e-3 )







  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     super().test_inference_batch_single_identical(expected_max_diff=7e-3 )







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3 )







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     super().test_save_load_local(expected_max_difference=3e-3 )







  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     super().test_save_load_optional_components(expected_max_difference=3e-3 )







  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     lowercase__			       =  [
         "DDIMScheduler",
         "DDPMScheduler",
         "PNDMScheduler",
         "HeunDiscreteScheduler",
         "EulerAncestralDiscreteScheduler",
         "KDPM2DiscreteScheduler",
         "KDPM2AncestralDiscreteScheduler",
         "DPMSolverSDEScheduler",
     ]
     lowercase__			       =  self.get_dummy_components()
     lowercase__			       =  self.pipeline_class(**_lowercase )

     # make sure that PNDM does not need warm-up
     pipe.scheduler.register_to_config(skip_prk_steps=_lowercase )

     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     lowercase__			       =  self.get_dummy_inputs(_lowercase )
     lowercase__			       =  2

     lowercase__			       =  []
     for scheduler_enum in KarrasDiffusionSchedulers:
        if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

        lowercase__			       =  getattr(_lowercase   ,							scheduler_enum.name )
        lowercase__			       =  scheduler_cls.from_config(pipe.scheduler.config )
        lowercase__			       =  pipe(**_lowercase )[0]
        outputs.append(_lowercase )

     assert check_same_shape(_lowercase )





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







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     lowercase__			       =  torch.manual_seed(33 )

     lowercase__			       =  StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"   ,							torch_dtype=torch.floataa )
     pipe.to("cuda" )

     lowercase__			       =  StableDiffusionLatentUpscalePipeline.from_pretrained(
         "stabilityai/sd-x2-latent-upscaler"   ,							torch_dtype=torch.floataa )
     upscaler.to("cuda" )

     lowercase__			       =  "a photo of an astronaut high resolution, unreal engine, ultra realistic"

     lowercase__			       =  pipe(_lowercase   ,							generator=_lowercase   ,							output_type="latent" ).images

     lowercase__			       =  upscaler(
         prompt=_lowercase   ,							image=_lowercase   ,							num_inference_steps=20   ,							guidance_scale=0   ,							generator=_lowercase   ,							output_type="np"   ,							).images[0]

     lowercase__			       =  load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy" )
     assert np.abs((expected_image - image).mean() ) < 5e-2







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  torch.manual_seed(33 )

     lowercase__			       =  StableDiffusionLatentUpscalePipeline.from_pretrained(
         "stabilityai/sd-x2-latent-upscaler"   ,							torch_dtype=torch.floataa )
     upscaler.to("cuda" )

     lowercase__			       =  "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

     lowercase__			       =  load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png" )

     lowercase__			       =  upscaler(
         prompt=_lowercase   ,							image=_lowercase   ,							num_inference_steps=20   ,							guidance_scale=0   ,							generator=_lowercase   ,							output_type="np"   ,							).images[0]

     lowercase__			       =  load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy" )
     assert np.abs((expected_image - image).max() ) < 5e-2






