'''simple docstring'''


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						StableDiffusionModelEditingPipeline
  lowerCamelCase_						:List[str]		       =						TEXT_TO_IMAGE_PARAMS
  lowerCamelCase_						:Any		       =						TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCamelCase_						:Union[str, Any]		       =						TEXT_TO_IMAGE_IMAGE_PARAMS
  lowerCamelCase_						:List[str]		       =						TEXT_TO_IMAGE_IMAGE_PARAMS


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)
       UpperCAmelCase_				:  str  = UNetaDConditionModel(
           block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=4 ,						out_channels=4 ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,						cross_attention_dim=3_2 ,						)
       UpperCAmelCase_				:  List[str]  = DDIMScheduler()
       torch.manual_seed(0		)
       UpperCAmelCase_				:  List[str]  = AutoencoderKL(
           block_out_channels=[3_2, 6_4] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,						latent_channels=4 ,						)
       torch.manual_seed(0		)
       UpperCAmelCase_				:  int  = CLIPTextConfig(
           bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=3_2 ,						intermediate_size=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_0_0_0 ,						)
       UpperCAmelCase_				:  int  = CLIPTextModel(snake_case_		)
       UpperCAmelCase_				:  Any  = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

       UpperCAmelCase_				:  str  = {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_=0		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = torch.manual_seed(snake_case_		)
       UpperCAmelCase_				:  int  = {
           'prompt': 'A field of roses',
           'generator': generator,
           # Setting height and width to None to prevent OOMs on CPU.
           'height': None,
           'width': None,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Optional[int]  = self.get_dummy_components()
       UpperCAmelCase_				:  List[str]  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Union[str, Any]  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Any  = sd_pipe(**snake_case_		).images
       UpperCAmelCase_				:  Dict  = image[0, -3:, -3:, -1]
       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  str  = np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  int  = self.get_dummy_components()
       UpperCAmelCase_				:  Dict  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Tuple  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Any  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Any  = 'french fries'
       UpperCAmelCase_				:  Tuple  = sd_pipe(**snake_case_ ,						negative_prompt=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = output.images
       UpperCAmelCase_				:  List[Any]  = image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  List[Any]  = np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Tuple  = self.get_dummy_components()
       UpperCAmelCase_				:  Optional[int]  = EulerAncestralDiscreteScheduler(
           beta_start=0.0_00_85 ,						beta_end=0.0_12 ,						beta_schedule='scaled_linear'		)
       UpperCAmelCase_				:  Optional[int]  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Optional[Any]  = self.get_dummy_inputs(snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = sd_pipe(**snake_case_		).images
       UpperCAmelCase_				:  Optional[Any]  = image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  List[Any]  = np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = 'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:  Optional[int]  = self.get_dummy_components()
       UpperCAmelCase_				:  Optional[Any]  = PNDMScheduler()
       UpperCAmelCase_				:  List[str]  = StableDiffusionModelEditingPipeline(**snake_case_		)
       UpperCAmelCase_				:  Tuple  = sd_pipe.to(snake_case_		)
       sd_pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Dict  = self.get_dummy_inputs(snake_case_		)
       # the pipeline does not expect pndm so test if it raises error.
       with self.assertRaises(snake_case_		):
            UpperCAmelCase_				:  str  = sd_pipe(**snake_case_		).images


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_inference_batch_single_identical(expected_max_diff=5E-3		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)







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







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_=0		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = torch.manual_seed(snake_case_		)
       UpperCAmelCase_				:  Any  = {
           'prompt': 'A field of roses',
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = 'CompVis/stable-diffusion-v1-4'
       UpperCAmelCase_				:  Any  = StableDiffusionModelEditingPipeline.from_pretrained(snake_case_ ,						safety_checker=snake_case_		)
       pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing()

       UpperCAmelCase_				:  str  = self.get_inputs()
       UpperCAmelCase_				:  List[str]  = pipe(**snake_case_		).images
       UpperCAmelCase_				:  Any  = image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_1_2, 5_1_2, 3)

       UpperCAmelCase_				:  str  = np.array(
           [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]		)

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

       # make sure image changes after editing
       pipe.edit_model('A pack of roses' ,						'A pack of blue roses'		)

       UpperCAmelCase_				:  Union[str, Any]  = pipe(**snake_case_		).images
       UpperCAmelCase_				:  Optional[int]  = image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 5_1_2, 5_1_2, 3)

       assert np.abs(expected_slice - image_slice		).max() > 1E-1






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       UpperCAmelCase_				:  Optional[Any]  = 'CompVis/stable-diffusion-v1-4'
       UpperCAmelCase_				:  str  = DDIMScheduler.from_pretrained(snake_case_ ,						subfolder='scheduler'		)
       UpperCAmelCase_				:  Dict  = StableDiffusionModelEditingPipeline.from_pretrained(
           snake_case_ ,						scheduler=snake_case_ ,						safety_checker=snake_case_		)
       UpperCAmelCase_				:  List[Any]  = pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing(1		)
       pipe.enable_sequential_cpu_offload()

       UpperCAmelCase_				:  Optional[int]  = self.get_inputs()
       UpperCAmelCase_				:  Any  = pipe(**snake_case_		)

       UpperCAmelCase_				:  str  = torch.cuda.max_memory_allocated()
       # make sure that less than 4.4 GB is allocated
       assert mem_bytes < 4.4 * 1_0**9







'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=3 ,						snake_case_=3_2 ,						snake_case_=3 ,						snake_case_=1_0 ,						snake_case_=[1_0, 2_0, 3_0, 4_0] ,						snake_case_=[1, 1, 2, 1] ,						snake_case_=True ,						snake_case_=True ,						snake_case_="relu" ,						snake_case_=3 ,						snake_case_=None ,						snake_case_=["stage2", "stage3", "stage4"] ,						snake_case_=[2, 3, 4] ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = parent
       UpperCAmelCase_				:  int  = batch_size
       UpperCAmelCase_				:  Optional[int]  = image_size
       UpperCAmelCase_				:  List[str]  = num_channels
       UpperCAmelCase_				:  Dict  = embeddings_size
       UpperCAmelCase_				:  List[str]  = hidden_sizes
       UpperCAmelCase_				:  int  = depths
       UpperCAmelCase_				:  Dict  = is_training
       UpperCAmelCase_				:  Optional[int]  = use_labels
       UpperCAmelCase_				:  Union[str, Any]  = hidden_act
       UpperCAmelCase_				:  Optional[Any]  = num_labels
       UpperCAmelCase_				:  List[Any]  = scope
       UpperCAmelCase_				:  Tuple  = len(snake_case_		)
       UpperCAmelCase_				:  Dict  = out_features
       UpperCAmelCase_				:  Dict  = out_indices


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       UpperCAmelCase_				:  List[str]  = None
       if self.use_labels:
            UpperCAmelCase_				:  str  = ids_tensor([self.batch_size] ,						self.num_labels		)

       UpperCAmelCase_				:  List[str]  = self.get_config()

       return config, pixel_values, labels


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return ResNetConfig(
           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 ,						out_features=self.out_features ,						out_indices=self.out_indices ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModel(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)
       # 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 // 3_2, self.image_size // 3_2) ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.num_labels
       UpperCAmelCase_				:  Optional[Any]  = ResNetForImageClassification(snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Optional[int]  = model(snake_case_ ,						labels=snake_case_		)
       self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Dict  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[1], 4, 4]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(model.channels ,						config.hidden_sizes[1:]		)

       # verify backbone works with out_features=None
       UpperCAmelCase_				:  Tuple  = None
       UpperCAmelCase_				:  Tuple  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						1		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						1		)
       self.parent.assertListEqual(model.channels ,						[config.hidden_sizes[-1]]		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = self.prepare_config_and_inputs()
       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = config_and_inputs
       UpperCAmelCase_				:  str  = {'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase_						:List[str]		       =						(
      {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCamelCase_						:int		       =						True
  lowerCamelCase_						:Optional[Any]		       =						False
  lowerCamelCase_						:Union[str, Any]		       =						False
  lowerCamelCase_						:int		       =						False
  lowerCamelCase_						:str		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModelTester(self		)
       UpperCAmelCase_				:  int  = ConfigTester(self ,						config_class=snake_case_ ,						has_text_modality=snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return


  @unittest.skip(reason='ResNet does not use inputs_embeds'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  @unittest.skip(reason='ResNet does not support input and output embeddings'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  Optional[int]  = model_class(snake_case_		)
            UpperCAmelCase_				:  Any  = inspect.signature(model.forward		)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase_				:  Any  = [*signature.parameters.keys()]

            UpperCAmelCase_				:  List[str]  = ['pixel_values']
            self.assertListEqual(arg_names[:1] ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  List[Any]  = model_class(config=snake_case_		)
            for name, module in model.named_modules():
                 if isinstance(snake_case_ ,						(nn.BatchNormad, nn.GroupNorm)		):
                      self.assertTrue(
                          torch.all(module.weight == 1		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)
                      self.assertTrue(
                          torch.all(module.bias == 0		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  int  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

            with torch.no_grad():
                 UpperCAmelCase_				:  str  = model(**self._prepare_for_class(snake_case_ ,						snake_case_		)		)

            UpperCAmelCase_				:  Union[str, Any]  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase_				:  str  = self.model_tester.num_stages
            self.assertEqual(len(snake_case_		) ,						expected_num_stages + 1		)

            # ResNet'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 // 4, self.model_tester.image_size // 4] ,						)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  Optional[Any]  = ['basic', 'bottleneck']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 UpperCAmelCase_				:  Any  = layer_type
                 UpperCAmelCase_				:  str  = True
                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 UpperCAmelCase_				:  List[Any]  = True

                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)


  @unittest.skip(reason='ResNet does not use feedforward chunking'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_		)






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_				:  str  = ResNetModel.from_pretrained(snake_case_		)
            self.assertIsNotNone(snake_case_		)







def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
     return image






@require_torch
@require_vision
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  @cached_property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(snake_case_		)

       UpperCAmelCase_				:  str  = self.default_image_processor
       UpperCAmelCase_				:  Union[str, Any]  = prepare_img()
       UpperCAmelCase_				:  Union[str, Any]  = image_processor(images=snake_case_ ,						return_tensors='pt'		).to(snake_case_		)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_				:  Union[str, Any]  = model(**snake_case_		)

       # verify the logits
       UpperCAmelCase_				:  Union[str, Any]  = torch.Size((1, 1_0_0_0)		)
       self.assertEqual(outputs.logits.shape ,						snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = torch.tensor([-11.10_69, -9.78_77, -8.37_77]		).to(snake_case_		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						snake_case_ ,						atol=1E-4		)		)







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						(ResNetBackbone,) if is_torch_available() else ()
  lowerCamelCase_						:List[str]		       =						False
  lowerCamelCase_						:Optional[Any]		       =						ResNetConfig


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetModelTester(self		)






