





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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       __A     (						a    ,					unittest.TestCase    ):
				__A      =				KandinskyVaaImgaImgPipeline
				__A      =				["""image_embeds""", """negative_image_embeds""", """image"""]
				__A      =				[
				    """image_embeds""",
				    """negative_image_embeds""",
				    """image""",
				]
				__A      =				[
				    """generator""",
				    """height""",
				    """width""",
				    """strength""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				__A      =				False


				@property
				def _snake_case       (		self							):
								return 32


				@property
				def _snake_case       (		self							):
								return 32


				@property
				def _snake_case       (		self							):
								return self.time_input_dim


				@property
				def _snake_case       (		self							):
								return self.time_input_dim * 4


				@property
				def _snake_case       (		self							):
								return 100


				@property
				def _snake_case       (		self							):
								torch.manual_seed(0							)

								lowerCamelCase	 ={
								    """in_channels""": 4,
								    # Out channels is double in channels because predicts mean and variance
								    """out_channels""": 8,
								    """addition_embed_type""": """image""",
								    """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,
								}

								lowerCamelCase	 =UNetaDConditionModel(**UpperCAmelCase_							)
								return model


				@property
				def _snake_case       (		self							):
								return {
								    "block_out_channels": [32, 64],
								    "down_block_types": ["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",
								    ],
								    "vq_embed_dim": 4,
								}


				@property
				def _snake_case       (		self							):
								torch.manual_seed(0							)
								lowerCamelCase	 =VQModel(**self.dummy_movq_kwargs							)
								return model


				def _snake_case       (		self							):
								lowerCamelCase	 =self.dummy_unet
								lowerCamelCase	 =self.dummy_movq

								lowerCamelCase	 ={
								    """num_train_timesteps""": 1000,
								    """beta_schedule""": """linear""",
								    """beta_start""": 0.0_0_0_8_5,
								    """beta_end""": 0.0_1_2,
								    """clip_sample""": False,
								    """set_alpha_to_one""": False,
								    """steps_offset""": 0,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								}

								lowerCamelCase	 =DDIMScheduler(**UpperCAmelCase_							)

								lowerCamelCase	 ={
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=0							):
								lowerCamelCase	 =floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase	 =floats_tensor((1, self.text_embedder_hidden_size)   , rng=random.Random(seed + 1							)							).to(
								    UpperCAmelCase_							)
								# create init_image
								lowerCamelCase	 =floats_tensor((1, 3, 64, 64)   , rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase	 =image.cpu().permute(0   , 2   , 3   , 1							)[0]
								lowerCamelCase	 =Image.fromarray(np.uinta(UpperCAmelCase_							)							).convert("""RGB"""							).resize((256, 256)							)

								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase	 =torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase	 =torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)
								lowerCamelCase	 ={
								    """image""": init_image,
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """num_inference_steps""": 10,
								    """guidance_scale""": 7.0,
								    """strength""": 0.2,
								    """output_type""": """np""",
								}
								return inputs


				def _snake_case       (		self							):
								lowerCamelCase	 ="""cpu"""

								lowerCamelCase	 =self.get_dummy_components()

								lowerCamelCase	 =self.pipeline_class(**UpperCAmelCase_							)
								lowerCamelCase	 =pipe.to(UpperCAmelCase_							)

								pipe.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase	 =pipe(**self.get_dummy_inputs(UpperCAmelCase_							)							)
								lowerCamelCase	 =output.images

								lowerCamelCase	 =pipe(
								    **self.get_dummy_inputs(UpperCAmelCase_							)   , return_dict=UpperCAmelCase_   , )[0]

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

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

								lowerCamelCase	 =np.array(
								    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]							)
								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       __A     (						unittest.TestCase    ):


				def _snake_case       (		self							):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def _snake_case       (		self							):
								lowerCamelCase	 =load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""							)

								lowerCamelCase	 =load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
								lowerCamelCase	 ="""A red cartoon frog, 4k"""

								lowerCamelCase	 =KandinskyVaaPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-prior"""   , torch_dtype=torch.floataa							)
								pipe_prior.to(UpperCAmelCase_							)

								lowerCamelCase	 =KandinskyVaaImgaImgPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-2-decoder"""   , torch_dtype=torch.floataa							)
								lowerCamelCase	 =pipeline.to(UpperCAmelCase_							)

								pipeline.set_progress_bar_config(disable=UpperCAmelCase_							)

								lowerCamelCase	 =torch.Generator(device="""cpu"""							).manual_seed(0							)
								lowerCamelCase ,  lowerCamelCase	 =pipe_prior(
								    UpperCAmelCase_   , generator=UpperCAmelCase_   , num_inference_steps=5   , negative_prompt=""""""   , ).to_tuple()

								lowerCamelCase	 =pipeline(
								    image=UpperCAmelCase_   , image_embeds=UpperCAmelCase_   , negative_image_embeds=UpperCAmelCase_   , generator=UpperCAmelCase_   , num_inference_steps=100   , height=768   , width=768   , strength=0.2   , output_type="""np"""   , )

								lowerCamelCase	 =output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(UpperCAmelCase_   , UpperCAmelCase_							)












import collections
import inspect
import unittest

from transformers import FocalNetConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    FocalNetBackbone,
							    FocalNetForImageClassification,
							    FocalNetForMaskedImageModeling,
							    FocalNetModel,
							)
							from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=13   , UpperCAmelCase_=32   , UpperCAmelCase_=2   , UpperCAmelCase_=3   , UpperCAmelCase_=16   , UpperCAmelCase_=[32, 64, 128]   , UpperCAmelCase_=[1, 2, 1]   , UpperCAmelCase_=[2, 2, 4]   , UpperCAmelCase_=2   , UpperCAmelCase_=2.0   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=False   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-5   , UpperCAmelCase_=True   , UpperCAmelCase_=None   , UpperCAmelCase_=True   , UpperCAmelCase_=10   , UpperCAmelCase_=8   , UpperCAmelCase_=["stage1", "stage2"]   , UpperCAmelCase_=[1, 2]   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =image_size
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =embed_dim
								lowerCamelCase	 =hidden_sizes
								lowerCamelCase	 =depths
								lowerCamelCase	 =num_heads
								lowerCamelCase	 =window_size
								lowerCamelCase	 =mlp_ratio
								lowerCamelCase	 =qkv_bias
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =drop_path_rate
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =use_absolute_embeddings
								lowerCamelCase	 =patch_norm
								lowerCamelCase	 =layer_norm_eps
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =is_training
								lowerCamelCase	 =scope
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =type_sequence_label_size
								lowerCamelCase	 =encoder_stride
								lowerCamelCase	 =out_features
								lowerCamelCase	 =out_indices


				def _snake_case       (		self							):
								lowerCamelCase	 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

								lowerCamelCase	 =None
								if self.use_labels:
												lowerCamelCase	 =ids_tensor([self.batch_size]   , self.type_sequence_label_size							)

								lowerCamelCase	 =self.get_config()

								return config, pixel_values, labels


				def _snake_case       (		self							):
								return FocalNetConfig(
								    image_size=self.image_size   , patch_size=self.patch_size   , num_channels=self.num_channels   , embed_dim=self.embed_dim   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , num_heads=self.num_heads   , window_size=self.window_size   , mlp_ratio=self.mlp_ratio   , qkv_bias=self.qkv_bias   , hidden_dropout_prob=self.hidden_dropout_prob   , attention_probs_dropout_prob=self.attention_probs_dropout_prob   , drop_path_rate=self.drop_path_rate   , hidden_act=self.hidden_act   , use_absolute_embeddings=self.use_absolute_embeddings   , path_norm=self.patch_norm   , layer_norm_eps=self.layer_norm_eps   , initializer_range=self.initializer_range   , encoder_stride=self.encoder_stride   , out_features=self.out_features   , out_indices=self.out_indices   , )


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								lowerCamelCase	 =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
								lowerCamelCase	 =int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, expected_seq_len, expected_dim)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetBackbone(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								# 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.image_size, 8, 8]							)

								# 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
								lowerCamelCase	 =None
								lowerCamelCase	 =FocalNetBackbone(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)

								# verify feature maps
								self.parent.assertEqual(len(result.feature_maps							)   , 1							)
								self.parent.assertListEqual(list(result.feature_maps[0].shape							)   , [self.batch_size, self.image_size * 2, 4, 4]							)

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


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =FocalNetForMaskedImageModeling(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(
								    result.reconstruction.shape   , (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

								# test greyscale images
								lowerCamelCase	 =1
								lowerCamelCase	 =FocalNetForMaskedImageModeling(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								lowerCamelCase	 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.reconstruction.shape   , (self.batch_size, 1, self.image_size, self.image_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =self.type_sequence_label_size
								lowerCamelCase	 =FocalNetForImageClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , labels=UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)							)

								# test greyscale images
								lowerCamelCase	 =1
								lowerCamelCase	 =FocalNetForImageClassification(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

								lowerCamelCase	 =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(
				    (
				        FocalNetModel,
				        FocalNetForImageClassification,
				        FocalNetForMaskedImageModeling,
				        FocalNetBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				__A      =				(
				    {"""feature-extraction""": FocalNetModel, """image-classification""": FocalNetForImageClassification}
				    if is_torch_available()
				    else {}
				)
				__A      =				False

				__A      =				False
				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , embed_dim=37   , has_text_modality=UpperCAmelCase_							)


				def _snake_case       (		self							):
								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 _snake_case       (		self							):
								return


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_							)


				@unittest.skip(reason="""FocalNet does not use inputs_embeds"""							)
				def _snake_case       (		self							):
								pass


				@unittest.skip(reason="""FocalNet does not use feedforward chunking"""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												self.assertIsInstance(model.get_input_embeddings()   , (nn.Module)							)
												lowerCamelCase	 =model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCAmelCase_   , nn.Linear							)							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												lowerCamelCase	 =inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowerCamelCase	 =[*signature.parameters.keys()]

												lowerCamelCase	 =["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   , UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =model_class(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()

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

								lowerCamelCase	 =outputs.hidden_states

								lowerCamelCase	 =getattr(
								    self.model_tester   , """expected_num_hidden_layers"""   , len(self.model_tester.depths							) + 1							)
								self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

								# FocalNet has a different seq_length
								lowerCamelCase	 =(
								    config.patch_size
								    if isinstance(config.patch_size   , collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase	 =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]							)   , [num_patches, self.model_tester.embed_dim]   , )

								lowerCamelCase	 =outputs.reshaped_hidden_states
								self.assertEqual(len(UpperCAmelCase_							)   , UpperCAmelCase_							)

								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =reshaped_hidden_states[0].shape
								lowerCamelCase	 =(
								    reshaped_hidden_states[0].view(UpperCAmelCase_   , UpperCAmelCase_   , height * width							).permute(0   , 2   , 1							)
								)
								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]							)   , [num_patches, self.model_tester.embed_dim]   , )


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   , collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)

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

												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase	 =3

								lowerCamelCase	 =(
								    self.model_tester.image_size
								    if isinstance(self.model_tester.image_size   , collections.abc.Iterable							)
								    else (self.model_tester.image_size, self.model_tester.image_size)
								)
								lowerCamelCase	 =(
								    config.patch_size
								    if isinstance(config.patch_size   , collections.abc.Iterable							)
								    else (config.patch_size, config.patch_size)
								)

								lowerCamelCase	 =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowerCamelCase	 =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes[:-1]:
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , (padded_height, padded_width)							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowerCamelCase	 =True
												self.check_hidden_states_output(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , (padded_height, padded_width)							)


				@slow
				def _snake_case       (		self							):
								for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase	 =FocalNetModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =_config_zero_init(UpperCAmelCase_							)
								for model_class in self.all_model_classes:
												lowerCamelCase	 =model_class(config=UpperCAmelCase_							)
												for name, param in model.named_parameters():
																if "embeddings" not in name and param.requires_grad:
																				self.assertIn(
																				    ((param.data.mean() * 1E9).round() / 1E9).item()   , [0.0, 1.0]   , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   , )




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


				@cached_property
				def _snake_case       (		self							):
								# TODO update organization
								return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""							) if is_vision_available() else None


				@slow
				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""							).to(UpperCAmelCase_							)
								lowerCamelCase	 =self.default_image_processor

								lowerCamelCase	 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
								lowerCamelCase	 =image_processor(images=UpperCAmelCase_   , return_tensors="""pt"""							).to(UpperCAmelCase_							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)

								# verify the logits
								lowerCamelCase	 =torch.Size((1, 1000)							)
								self.assertEqual(outputs.logits.shape   , UpperCAmelCase_							)
								lowerCamelCase	 =torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]							).to(UpperCAmelCase_							)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]   , UpperCAmelCase_   , atol=1E-4							)							)
								self.assertTrue(outputs.logits.argmax(dim=-1							).item()   , 281							)




@require_torch
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				(FocalNetBackbone,) if is_torch_available() else ()
				__A      =				FocalNetConfig

				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =FocalNetModelTester(self							)





