






"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 							lowerCAmelCase__				(			UpperCAmelCase__				,		UpperCAmelCase__				,		unittest.TestCase ):






      '''simple docstring'''

      __UpperCamelCase	 =  CycleDiffusionPipeline
      __UpperCamelCase	 =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          "negative_prompt",
          "height",
          "width",
          "negative_prompt_embeds",
      }
      __UpperCamelCase	 =  PipelineTesterMixin.required_optional_params - {"latents"}
      __UpperCamelCase	 =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
      __UpperCamelCase	 =  IMAGE_TO_IMAGE_IMAGE_PARAMS
      __UpperCamelCase	 =  IMAGE_TO_IMAGE_IMAGE_PARAMS
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		UNetaDConditionModel(
              block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=4						,	out_channels=4						,	down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')						,	up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')						,	cross_attention_dim=32						,	)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		DDIMScheduler(
              beta_start=0.0_00_85						,	beta_end=0.0_12						,	beta_schedule='''scaled_linear'''						,	num_train_timesteps=1000						,	clip_sample=lowercase_						,	set_alpha_to_one=lowercase_						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		AutoencoderKL(
              block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']						,	up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']						,	latent_channels=4						,	)
          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		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=1000						,	)
          SCREAMING_SNAKE_CASE_      :  int    						=		CLIPTextModel(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

          SCREAMING_SNAKE_CASE_      :  Dict    						=		{
              '''unet''': unet,
              '''scheduler''': scheduler,
              '''vae''': vae,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
              '''safety_checker''': None,
              '''feature_extractor''': None,
          }
          return components
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Optional[Any]=0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		floats_tensor((1, 3, 32, 32)						,	rng=random.Random(lowercase_)).to(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		image / 2 + 0.5
          if str(lowercase_).startswith('''mps'''):
              SCREAMING_SNAKE_CASE_      :  int    						=		torch.manual_seed(lowercase_)
          else:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.Generator(device=lowercase_).manual_seed(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		{
              '''prompt''': '''An astronaut riding an elephant''',
              '''source_prompt''': '''An astronaut riding a horse''',
              '''image''': image,
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''eta''': 0.1,
              '''strength''': 0.8,
              '''guidance_scale''': 3,
              '''source_guidance_scale''': 1,
              '''output_type''': '''numpy''',
          }
          return inputs
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.get_dummy_components()
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		CycleDiffusionPipeline(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Any    						=		self.get_dummy_inputs(lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		pipe(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		output.images

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		images[0, -3:, -3:, -1]

          assert images.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_      :  Dict    						=		np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      @unittest.skipIf(torch_device != '''cuda'''						,	'''This test requires a GPU''')
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.get_dummy_components()
          for name, module in components.items():
              if hasattr(lowercase_						,	'''half'''):
                  SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		module.half()
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		CycleDiffusionPipeline(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		self.get_dummy_inputs(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		pipe(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		output.images

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		images[0, -3:, -3:, -1]

          assert images.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_      :  int    						=		np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          return super().test_save_load_local()
      @unittest.skip('''non-deterministic pipeline''')
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          return super().test_inference_batch_single_identical()
      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          return super().test_save_load_optional_components()



      @skip_mps
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          return super().test_attention_slicing_forward_pass()




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






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/cycle-diffusion/black_colored_car.png''')
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''')
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		init_image.resize((512, 512))

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''CompVis/stable-diffusion-v1-4'''
          SCREAMING_SNAKE_CASE_      :  int    						=		DDIMScheduler.from_pretrained(lowercase_						,	subfolder='''scheduler''')
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		CycleDiffusionPipeline.from_pretrained(
              lowercase_						,	scheduler=lowercase_						,	safety_checker=lowercase_						,	torch_dtype=torch.floataa						,	revision='''fp16''')

          pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)
          pipe.enable_attention_slicing()

          SCREAMING_SNAKE_CASE_      :  Any    						=		'''A black colored car'''
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''A blue colored car'''

          SCREAMING_SNAKE_CASE_      :  str    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		pipe(
              prompt=lowercase_						,	source_prompt=lowercase_						,	image=lowercase_						,	num_inference_steps=100						,	eta=0.1						,	strength=0.85						,	guidance_scale=3						,	source_guidance_scale=1						,	generator=lowercase_						,	output_type='''np'''						,	)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		output.images

          # the values aren't exactly equal, but the images look the same visually
          assert np.abs(image - expected_image).max() < 5e-1



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/cycle-diffusion/black_colored_car.png''')
          SCREAMING_SNAKE_CASE_      :  Any    						=		load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''')
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		init_image.resize((512, 512))

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		'''CompVis/stable-diffusion-v1-4'''
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		DDIMScheduler.from_pretrained(lowercase_						,	subfolder='''scheduler''')
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		CycleDiffusionPipeline.from_pretrained(lowercase_						,	scheduler=lowercase_						,	safety_checker=lowercase_)

          pipe.to(lowercase_)
          pipe.set_progress_bar_config(disable=lowercase_)
          pipe.enable_attention_slicing()

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		'''A black colored car'''
          SCREAMING_SNAKE_CASE_      :  Dict    						=		'''A blue colored car'''

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		pipe(
              prompt=lowercase_						,	source_prompt=lowercase_						,	image=lowercase_						,	num_inference_steps=100						,	eta=0.1						,	strength=0.85						,	guidance_scale=3						,	source_guidance_scale=1						,	generator=lowercase_						,	output_type='''np'''						,	)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		output.images

          assert np.abs(image - expected_image).max() < 2e-2










"""simple docstring"""






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      LevitForImageClassification,
      LevitForImageClassificationWithTeacher,
      LevitModel,
  )
  from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase_						,	'''hidden_sizes'''))
          self.parent.assertTrue(hasattr(lowercase_						,	'''num_attention_heads'''))




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  str						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  List[Any]=13						,	lowercase_     :  Dict=64						,	lowercase_     :  Dict=3						,	lowercase_     :  Optional[Any]=3						,	lowercase_     :  List[Any]=2						,	lowercase_     :  Any=1						,	lowercase_     :  List[Any]=16						,	lowercase_     :  int=[128, 256, 384]						,	lowercase_     :  str=[4, 6, 8]						,	lowercase_     :  Optional[Any]=[2, 3, 4]						,	lowercase_     :  Union[str, Any]=[16, 16, 16]						,	lowercase_     :  Optional[Any]=0						,	lowercase_     :  Optional[int]=[2, 2, 2]						,	lowercase_     :  Any=[2, 2, 2]						,	lowercase_     :  List[str]=0.02						,	lowercase_     :  Any=True						,	lowercase_     :  Union[str, Any]=True						,	lowercase_     :  Optional[int]=2						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		parent
          SCREAMING_SNAKE_CASE_      :  Any    						=		batch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		image_size
          SCREAMING_SNAKE_CASE_      :  int    						=		num_channels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kernel_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		stride
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		padding
          SCREAMING_SNAKE_CASE_      :  int    						=		hidden_sizes
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  int    						=		depths
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		key_dim
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		drop_path_rate
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		patch_size
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		attention_ratio
          SCREAMING_SNAKE_CASE_      :  str    						=		mlp_ratio
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
              ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
          ]
          SCREAMING_SNAKE_CASE_      :  Any    						=		is_training
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		use_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		num_labels
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		initializer_range
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_config()
          return config, pixel_values, labels
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          return LevitConfig(
              image_size=self.image_size						,	num_channels=self.num_channels						,	kernel_size=self.kernel_size						,	stride=self.stride						,	padding=self.padding						,	patch_size=self.patch_size						,	hidden_sizes=self.hidden_sizes						,	num_attention_heads=self.num_attention_heads						,	depths=self.depths						,	key_dim=self.key_dim						,	drop_path_rate=self.drop_path_rate						,	mlp_ratio=self.mlp_ratio						,	attention_ratio=self.attention_ratio						,	initializer_range=self.initializer_range						,	down_ops=self.down_ops						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  Any						,	lowercase_     :  int						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		LevitModel(config=lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		(self.image_size, self.image_size)
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Tuple    						=		image_size[0], image_size[1]
          for _ in range(4):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
          self.parent.assertEqual(
              result.last_hidden_state.shape						,	(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  int						,	lowercase_     :  Union[str, Any]						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.num_labels
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitForImageClassification(lowercase_)
          model.to(lowercase_)
          model.eval()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	labels=lowercase_)
          self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels))



      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		config_and_inputs
          SCREAMING_SNAKE_CASE_      :  int    						=		{'''pixel_values''': pixel_values}
          return config, inputs_dict




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






      '''simple docstring'''

      __UpperCamelCase	 =  (
          (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
          if is_torch_available()
          else ()
      )
      __UpperCamelCase	 =  (
          {
              "feature-extraction": LevitModel,
              "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
          }
          if is_torch_available()
          else {}
      )

      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      __UpperCamelCase	 =  False
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		LevitModelTester(self)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		ConfigTester(self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''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 _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          return
      @unittest.skip(reason='''Levit does not use inputs_embeds''')
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not support input and output embeddings''')
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          pass
      @unittest.skip(reason='''Levit does not output attentions''')
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Any    						=		model_class(lowercase_)
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_      :  Dict    						=		[*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		['''pixel_values''']
              self.assertListEqual(arg_names[:1]						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          def check_hidden_states_output(lowercase_     :  List[str]						,	lowercase_     :  Optional[Any]						,	lowercase_     :  str):
              SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.eval()

              with torch.no_grad():
                  SCREAMING_SNAKE_CASE_      :  Tuple    						=		model(**self._prepare_for_class(lowercase_						,	lowercase_))

              SCREAMING_SNAKE_CASE_      :  str    						=		outputs.hidden_states

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		len(self.model_tester.depths) + 1
              self.assertEqual(len(lowercase_)						,	lowercase_)

              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(self.model_tester.image_size, self.model_tester.image_size)
              SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		image_size[0], image_size[1]
              for _ in range(4):
                  SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		floor(
                      (
                          (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
                  SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		floor(
                      (
                          (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                          / self.model_tester.stride
                      )
                      + 1)
              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:])						,	[
                      height * width,
                      self.model_tester.hidden_sizes[0],
                  ]						,	)

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

          for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		True
              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		True

              check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_)
      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]						,	lowercase_     :  Optional[int]						,	lowercase_     :  Tuple=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		super()._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

          if return_labels:
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  del inputs_dict["labels"]

          return inputs_dict
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          if not self.model_tester.is_training:
              return

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.model_tester.prepare_config_and_inputs_for_common()
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		True

          for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(lowercase_)
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		model_class(lowercase_)
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  int    						=		self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
              return

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

          for model_class in self.all_model_classes:
              if model_class in get_values(lowercase_) or not model_class.supports_gradient_checkpointing:
                  continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                  continue
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		model_class(lowercase_)
              model.gradient_checkpointing_enable()
              model.to(lowercase_)
              model.train()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(**lowercase_).loss
              loss.backward()
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  str    						=		self.model_tester.prepare_config_and_inputs_for_common()

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[
              {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
              {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
              {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
          ]

          for model_class in self.all_model_classes:
              if (
                  model_class
                  not in [
                      *get_values(lowercase_),
                  ]
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                  continue

              for problem_type in problem_types:
                  with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'):
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''title''']
                      SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		problem_type['''num_labels''']

                      SCREAMING_SNAKE_CASE_      :  str    						=		model_class(lowercase_)
                      model.to(lowercase_)
                      model.train()

                      SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self._prepare_for_class(lowercase_						,	lowercase_						,	return_labels=lowercase_)

                      if problem_type["num_labels"] > 1:
                          SCREAMING_SNAKE_CASE_      :  str    						=		inputs['''labels'''].unsqueeze(1).repeat(1						,	problem_type['''num_labels'''])

                      SCREAMING_SNAKE_CASE_      :  Any    						=		inputs['''labels'''].to(problem_type['''dtype'''])

                      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                      # they have the same size." which is a symptom something in wrong for the regression problem.
                      # See https://github.com/huggingface/transformers/issues/11780
                      with warnings.catch_warnings(record=lowercase_) as warning_list:
                          SCREAMING_SNAKE_CASE_      :  int    						=		model(**lowercase_).loss
                      for w in warning_list:
                          if "Using a target size that is different to the input size" in str(w.message):
                              raise ValueError(
                                  F'Something is going wrong in the regression problem: intercepted {w.message}')

                      loss.backward()



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		LevitModel.from_pretrained(lowercase_)
              self.assertIsNotNone(lowercase_)



def 					_A			() ->       Union[str, Any]:
    """simple docstring"""






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





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






      '''simple docstring'''

      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
              lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.default_image_processor
          SCREAMING_SNAKE_CASE_      :  str    						=		prepare_img()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		image_processor(images=lowercase_						,	return_tensors='''pt''').to(lowercase_)

          # forward pass
          with torch.no_grad():
              SCREAMING_SNAKE_CASE_      :  Any    						=		model(**lowercase_)

          # verify the logits
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		torch.tensor([1.04_48, -0.37_45, -1.83_17]).to(lowercase_)

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


