

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      KandinskyVaaControlnetPipeline
  a     =      ["image_embeds", "negative_image_embeds", "hint"]
  a     =      ["image_embeds", "negative_image_embeds", "hint"]
  a     =      [
      "generator",
      "height",
      "width",
      "latents",
      "guidance_scale",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  a     =      False






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Any:
       return 32






  @property
  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       return 32






  @property
  def    lowercase_	(							self		:	Any      )	->							Tuple:
       return self.time_input_dim






  @property
  def    lowercase_	(							self		:	int      )	->							Union[str, Any]:
       return self.time_input_dim * 4






  @property
  def    lowercase_	(							self		:	int      )	->							Optional[int]:
       return 100






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					{
           '''in_channels''': 8,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image_hint''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       SCREAMING_SNAKE_CASE__					=					UNetaDConditionModel(**__lowerCamelCase      )
       return model






  @property
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "AttnDownEncoderBlock2D",
           ],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Tuple:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					VQModel(**self.dummy_movq_kwargs      )
       return model






  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.dummy_unet
       SCREAMING_SNAKE_CASE__					=					self.dummy_movq

       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(
           num_train_timesteps=1000			,     beta_schedule='''linear'''			,     beta_start=0.00085			,     beta_end=0.012			,     clip_sample=__lowerCamelCase			,     set_alpha_to_one=__lowerCamelCase			,     steps_offset=1			,     prediction_type='''epsilon'''			,     thresholding=__lowerCamelCase			,     )

       SCREAMING_SNAKE_CASE__					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }
       return components






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	str=0      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(seed + 1      )      ).to(
           __lowerCamelCase      )

       # create hint
       SCREAMING_SNAKE_CASE__					=					floats_tensor((1, 3, 64, 64)			,     rng=random.Random(__lowerCamelCase      )      ).to(__lowerCamelCase      )

       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''hint''': hint,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs







  def    lowercase_	(							self		:	Tuple      )	->							int:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()

       SCREAMING_SNAKE_CASE__					=					self.pipeline_class(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					pipe.to(__lowerCamelCase      )

       pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					pipe(**self.get_dummy_inputs(__lowerCamelCase      )      )
       SCREAMING_SNAKE_CASE__					=					output.images

       SCREAMING_SNAKE_CASE__					=					pipe(
           **self.get_dummy_inputs(__lowerCamelCase      )			,     return_dict=__lowerCamelCase			,     )[0]

       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__					=					image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]      )

       assert (
           np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






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






  """simple docstring"""


  def    lowercase_	(							self		:	List[str]      )	->							List[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__					=					load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''      )

       SCREAMING_SNAKE_CASE__					=					load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/hint_image_cat.png'''      )
       SCREAMING_SNAKE_CASE__					=					torch.from_numpy(np.array(__lowerCamelCase      )      ).float() / 255.0
       SCREAMING_SNAKE_CASE__					=					hint.permute(2			,     0			,     1      ).unsqueeze(0      )

       SCREAMING_SNAKE_CASE__					=					KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''			,     torch_dtype=torch.floataa      )
       pipe_prior.to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					KandinskyVaaControlnetPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-controlnet-depth'''			,     torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__					=					pipeline.to(__lowerCamelCase      )
       pipeline.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A robot, 4k photo'''

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device='''cuda'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					pipe_prior(
           __lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=5			,     negative_prompt=''''''			,     ).to_tuple()

       SCREAMING_SNAKE_CASE__					=					torch.Generator(device='''cuda'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					pipeline(
           image_embeds=__lowerCamelCase			,     negative_image_embeds=__lowerCamelCase			,     hint=__lowerCamelCase			,     generator=__lowerCamelCase			,     num_inference_steps=100			,     output_type='''np'''			,     )

       SCREAMING_SNAKE_CASE__					=					output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(__lowerCamelCase			,     __lowerCamelCase      )









import inspect
import unittest

from transformers import BitConfig
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
       from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Optional[Any]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Union[str, Any]=3			,     __lowerCamelCase		:	int=32			,     __lowerCamelCase		:	Tuple=3			,     __lowerCamelCase		:	Optional[int]=10			,     __lowerCamelCase		:	List[Any]=[8, 16, 32, 64]			,     __lowerCamelCase		:	Dict=[1, 1, 2, 1]			,     __lowerCamelCase		:	int=True			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	Optional[int]="relu"			,     __lowerCamelCase		:	Optional[Any]=3			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	Optional[int]=["stage2", "stage3", "stage4"]			,     __lowerCamelCase		:	Tuple=[2, 3, 4]			,     __lowerCamelCase		:	List[Any]=1			,     )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					image_size
       SCREAMING_SNAKE_CASE__					=					num_channels
       SCREAMING_SNAKE_CASE__					=					embeddings_size
       SCREAMING_SNAKE_CASE__					=					hidden_sizes
       SCREAMING_SNAKE_CASE__					=					depths
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					num_labels
       SCREAMING_SNAKE_CASE__					=					scope
       SCREAMING_SNAKE_CASE__					=					len(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					out_features
       SCREAMING_SNAKE_CASE__					=					out_indices
       SCREAMING_SNAKE_CASE__					=					num_groups






  def    lowercase_	(							self		:	Optional[Any]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

       SCREAMING_SNAKE_CASE__					=					self.get_config()

       return config, pixel_values, labels






  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       return BitConfig(
           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			,     num_groups=self.num_groups			,     )






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Union[str, Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					BitModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )
       self.parent.assertEqual(
           result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,     )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     labels=__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Union[str, Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # 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
       SCREAMING_SNAKE_CASE__					=					None
       SCREAMING_SNAKE_CASE__					=					BitBackbone(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )

       # 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    lowercase_	(							self		:	Tuple      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
  a     =      (
      {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
      if is_torch_available()
      else {}
  )

  a     =      False
  a     =      False
  a     =      False
  a     =      False
  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     has_text_modality=__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							str:
       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    lowercase_	(							self		:	str      )	->							Dict:
       return






  @unittest.skip(reason='''Bit does not output attentions'''      )
  def    lowercase_	(							self		:	Any      )	->							Dict:
       pass






  @unittest.skip(reason='''Bit does not use inputs_embeds'''      )
  def    lowercase_	(							self		:	Tuple      )	->							Optional[int]:
       pass






  @unittest.skip(reason='''Bit does not support input and output embeddings'''      )
  def    lowercase_	(							self		:	Optional[int]      )	->							str:
       pass






  def    lowercase_	(							self		:	str      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__					=					['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,     __lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__					=					model_class(config=__lowerCamelCase      )
            for name, module in model.named_modules():
                 if isinstance(__lowerCamelCase			,     (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    lowercase_	(							self		:	str      )	->							Optional[Any]:
       def check_hidden_states_output(__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]      ):
            SCREAMING_SNAKE_CASE__					=					model_class(__lowerCamelCase      )
            model.to(__lowerCamelCase      )
            model.eval()

            with torch.no_grad():
                 SCREAMING_SNAKE_CASE__					=					model(**self._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase      )      )

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

            SCREAMING_SNAKE_CASE__					=					self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase      )			,     expected_num_stages + 1      )

            # Bit'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]			,     )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__					=					['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 SCREAMING_SNAKE_CASE__					=					layer_type
                 SCREAMING_SNAKE_CASE__					=					True
                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )

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

                 check_hidden_states_output(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )






  @unittest.skip(reason='''Bit does not use feedforward chunking'''      )
  def    lowercase_	(							self		:	List[str]      )	->							Dict:
       pass






  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase      )







  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					=					BitModel.from_pretrained(__lowerCamelCase      )
            self.assertIsNotNone(__lowerCamelCase      )



def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
     return image






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






  """simple docstring"""


  @cached_property
  def    lowercase_	(							self		:	List[Any]      )	->							List[Any]:
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ) if is_vision_available() else None
       )







  @slow
  def    lowercase_	(							self		:	str      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.default_image_processor
       SCREAMING_SNAKE_CASE__					=					prepare_img()
       SCREAMING_SNAKE_CASE__					=					image_processor(images=__lowerCamelCase			,     return_tensors='''pt'''      ).to(__lowerCamelCase      )

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					=					model(**__lowerCamelCase      )

       # verify the logits
       SCREAMING_SNAKE_CASE__					=					torch.Size((1, 1000)      )
       self.assertEqual(outputs.logits.shape			,     __lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					torch.tensor([[-0.6526, -0.5263, -1.4398]]      ).to(__lowerCamelCase      )

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






@require_torch
class  UpperCAmelCase__	( A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (BitBackbone,) if is_torch_available() else ()
  a     =      BitConfig

  a     =      False






  def    lowercase_	(							self		:	Optional[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					BitModelTester(self      )


