


'''simple docstring'''
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()
class      lowercase			(							unittest.TestCase       ):

		"""simple docstring"""

		_a									=       StableDiffusionLDMaDPipeline
		_a									=       TEXT_TO_IMAGE_PARAMS
		_a									=       TEXT_TO_IMAGE_BATCH_PARAMS
		_a									=       TEXT_TO_IMAGE_IMAGE_PARAMS




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :Union[str, Any]							      =				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					,     )
							UpperCamelCase__     :int							      =				DDIMScheduler(
							    beta_start=0.00085					,     beta_end=0.012					,     beta_schedule='''scaled_linear'''					,     clip_sample=UpperCamelCase_					,     set_alpha_to_one=UpperCamelCase_					,     )
							torch.manual_seed(0							)
							UpperCamelCase__     :Tuple							      =				AutoencoderKL(
							    block_out_channels=[32, 64]					,     in_channels=6					,     out_channels=6					,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,     latent_channels=4					,     )
							torch.manual_seed(0							)
							UpperCamelCase__     :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					,     )
							UpperCamelCase__     :int							      =				CLIPTextModel(UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__     :List[Any]							      =				{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__     :Any							      =				torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Tuple							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				{
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 6.0,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :Optional[Any]							      =				StableDiffusionLDMaDPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :str							      =				ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Any							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				output.rgb, output.depth

							UpperCamelCase__     :str							      =				rgb[0, -3:, -3:, -1]
							UpperCamelCase__     :Dict							      =				depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							UpperCamelCase__     :Optional[int]							      =				np.array(
							    [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]							)
							UpperCamelCase__     :int							      =				np.array([103.46727, 85.812004, 87.849236]							)

							assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb							).max() < 1e-2
							assert np.abs(image_slice_depth.flatten() - expected_slice_depth							).max() < 1e-2




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				self.get_dummy_components()
							UpperCamelCase__     :Tuple							      =				StableDiffusionLDMaDPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :str							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				3 * [inputs['''prompt''']]

							# forward
							UpperCamelCase__     :Optional[int]							      =				ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				output.rgb, output.depth
							UpperCamelCase__     :List[Any]							      =				rgb_slice_a[0, -3:, -3:, -1]
							UpperCamelCase__     :int							      =				depth_slice_a[0, -3:, -1]

							UpperCamelCase__     :Any							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				3 * [inputs.pop('''prompt'''							)]

							UpperCamelCase__     :str							      =				ldmad_pipe.tokenizer(
							    UpperCamelCase_					,     padding='''max_length'''					,     max_length=ldmad_pipe.tokenizer.model_max_length					,     truncation=UpperCamelCase_					,     return_tensors='''pt'''					,     )
							UpperCamelCase__     :Dict							      =				text_inputs['''input_ids'''].to(UpperCamelCase_							)

							UpperCamelCase__     :str							      =				ldmad_pipe.text_encoder(UpperCamelCase_							)[0]

							UpperCamelCase__     :Any							      =				prompt_embeds

							# forward
							UpperCamelCase__     :List[Any]							      =				ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				output.rgb, output.depth
							UpperCamelCase__     :int							      =				rgb_slice_a[0, -3:, -3:, -1]
							UpperCamelCase__     :List[Any]							      =				depth_slice_a[0, -3:, -1]

							assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()							).max() < 1e-4
							assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()							).max() < 1e-4




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :str							      =				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				StableDiffusionLDMaDPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :Dict							      =				ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :List[Any]							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				'''french fries'''
							UpperCamelCase__     :List[str]							      =				ldmad_pipe(**UpperCamelCase_					,     negative_prompt=UpperCamelCase_							)

							UpperCamelCase__     :List[str]							      =				output.rgb, output.depth
							UpperCamelCase__     :List[Any]							      =				rgb[0, -3:, -3:, -1]
							UpperCamelCase__     :List[Any]							      =				depth[0, -3:, -1]

							assert rgb.shape == (1, 64, 64, 3)
							assert depth.shape == (1, 64, 64)

							UpperCamelCase__     :int							      =				np.array(
							    [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]							)
							UpperCamelCase__     :Union[str, Any]							      =				np.array([107.84738, 84.62802, 89.962135]							)
							assert np.abs(rgb_slice.flatten() - expected_slice_rgb							).max() < 1e-2
							assert np.abs(depth_slice.flatten() - expected_slice_depth							).max() < 1e-2



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

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_="cpu"					,     UpperCamelCase_=torch.floataa					,     UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				np.random.RandomState(UpperCamelCase_							).standard_normal((1, 4, 64, 64)							)
							UpperCamelCase__     :Any							      =				torch.from_numpy(UpperCamelCase_							).to(device=UpperCamelCase_					,     dtype=UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				{
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''							)
							UpperCamelCase__     :List[Any]							      =				ldmad_pipe.to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				self.get_inputs(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				output.rgb, output.depth
							UpperCamelCase__     :str							      =				rgb[0, -3:, -3:, -1].flatten()
							UpperCamelCase__     :Optional[Any]							      =				rgb[0, -3:, -1].flatten()

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512)

							UpperCamelCase__     :Dict							      =				np.array(
							    [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]							)
							UpperCamelCase__     :List[str]							      =				np.array(
							    [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]							)
							assert np.abs(rgb_slice - expected_slice_rgb							).max() < 3e-3
							assert np.abs(depth_slice - expected_slice_depth							).max() < 3e-3



@nightly
@require_torch_gpu
class      lowercase			(							unittest.TestCase       ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_="cpu"					,     UpperCamelCase_=torch.floataa					,     UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				np.random.RandomState(UpperCamelCase_							).standard_normal((1, 4, 64, 64)							)
							UpperCamelCase__     :int							      =				torch.from_numpy(UpperCamelCase_							).to(device=UpperCamelCase_					,     dtype=UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				{
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 50,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''							).to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				self.get_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				output.rgb, output.depth

							UpperCamelCase__     :Any							      =				0.495586
							UpperCamelCase__     :Optional[Any]							      =				0.33795515
							UpperCamelCase__     :Optional[Any]							      =				112.48518
							UpperCamelCase__     :Union[str, Any]							      =				98.489746
							assert np.abs(expected_rgb_mean - rgb.mean()							) < 1e-3
							assert np.abs(expected_rgb_std - rgb.std()							) < 1e-3
							assert np.abs(expected_depth_mean - depth.mean()							) < 1e-3
							assert np.abs(expected_depth_std - depth.std()							) < 1e-3




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''							).to(UpperCamelCase_							)
							ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				self.get_inputs(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				ldmad_pipe(**UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				output.rgb, output.depth

							UpperCamelCase__     :Dict							      =				0.4194127
							UpperCamelCase__     :Union[str, Any]							      =				0.35375586
							UpperCamelCase__     :int							      =				0.5638502
							UpperCamelCase__     :List[str]							      =				0.34686103

							assert rgb.shape == (1, 512, 512, 3)
							assert depth.shape == (1, 512, 512, 1)
							assert np.abs(expected_rgb_mean - rgb.mean()							) < 1e-3
							assert np.abs(expected_rgb_std - rgb.std()							) < 1e-3
							assert np.abs(expected_depth_mean - depth.mean()							) < 1e-3
							assert np.abs(expected_depth_std - depth.std()							) < 1e-3



'''simple docstring'''
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      lowercase			:

		"""simple docstring"""

		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_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=10					,     UpperCamelCase_=8					,     UpperCamelCase_=["stage1", "stage2"]					,     UpperCamelCase_=[1, 2]					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				parent
							UpperCamelCase__     :Dict							      =				batch_size
							UpperCamelCase__     :Tuple							      =				image_size
							UpperCamelCase__     :Any							      =				patch_size
							UpperCamelCase__     :Tuple							      =				num_channels
							UpperCamelCase__     :int							      =				embed_dim
							UpperCamelCase__     :Optional[int]							      =				hidden_sizes
							UpperCamelCase__     :List[str]							      =				depths
							UpperCamelCase__     :List[str]							      =				num_heads
							UpperCamelCase__     :Dict							      =				window_size
							UpperCamelCase__     :str							      =				mlp_ratio
							UpperCamelCase__     :List[str]							      =				qkv_bias
							UpperCamelCase__     :Optional[Any]							      =				hidden_dropout_prob
							UpperCamelCase__     :List[Any]							      =				attention_probs_dropout_prob
							UpperCamelCase__     :List[str]							      =				drop_path_rate
							UpperCamelCase__     :str							      =				hidden_act
							UpperCamelCase__     :Optional[Any]							      =				use_absolute_embeddings
							UpperCamelCase__     :str							      =				patch_norm
							UpperCamelCase__     :List[Any]							      =				layer_norm_eps
							UpperCamelCase__     :Dict							      =				initializer_range
							UpperCamelCase__     :int							      =				is_training
							UpperCamelCase__     :List[Any]							      =				scope
							UpperCamelCase__     :Dict							      =				use_labels
							UpperCamelCase__     :Dict							      =				type_sequence_label_size
							UpperCamelCase__     :Tuple							      =				encoder_stride
							UpperCamelCase__     :Optional[int]							      =				out_features
							UpperCamelCase__     :Optional[int]							      =				out_indices




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

							UpperCamelCase__     :Any							      =				None
							if self.use_labels:
												UpperCamelCase__     :int							      =				ids_tensor([self.batch_size]					,     self.type_sequence_label_size							)

							UpperCamelCase__     :Tuple							      =				self.get_config()

							return config, pixel_values, labels




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :str							      =				model(UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
							UpperCamelCase__     :Optional[Any]							      =				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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Any							      =				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
							UpperCamelCase__     :Tuple							      =				None
							UpperCamelCase__     :Optional[Any]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[str]							      =				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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				FocalNetForMaskedImageModeling(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.reconstruction.shape					,     (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

							# test greyscale images
							UpperCamelCase__     :Optional[int]							      =				1
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForMaskedImageModeling(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :str							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.reconstruction.shape					,     (self.batch_size, 1, self.image_size, self.image_size)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.type_sequence_label_size
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :int							      =				model(UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)

							# test greyscale images
							UpperCamelCase__     :Tuple							      =				1
							UpperCamelCase__     :Tuple							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :Optional[int]							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Dict							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.prepare_config_and_inputs()

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				config_and_inputs
							UpperCamelCase__     :Any							      =				{'''pixel_values''': pixel_values}
							return config, inputs_dict



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

		"""simple docstring"""

		_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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				FocalNetModelTester(self							)
							UpperCamelCase__     :Optional[int]							      =				ConfigTester(self					,     config_class=UpperCamelCase_					,     embed_dim=37					,     has_text_modality=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				model_class(UpperCamelCase_							)
												UpperCamelCase__     :Dict							      =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase__     :Union[str, Any]							      =				[*signature.parameters.keys()]

												UpperCamelCase__     :Tuple							      =				['''pixel_values''']
												self.assertListEqual(arg_names[:1]					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				model_class(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							with torch.no_grad():
												UpperCamelCase__     :str							      =				model(**self._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_							)							)

							UpperCamelCase__     :List[Any]							      =				outputs.hidden_states

							UpperCamelCase__     :Optional[int]							      =				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
							UpperCamelCase__     :List[str]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				(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]					,     )

							UpperCamelCase__     :List[Any]							      =				outputs.reshaped_hidden_states
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				reshaped_hidden_states[0].shape
							UpperCamelCase__     :List[str]							      =				(
							    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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :List[str]							      =				(
							    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]:
												UpperCamelCase__     :Union[str, Any]							      =				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"]
												UpperCamelCase__     :List[Any]							      =				True

												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__     :Optional[int]							      =				3

							UpperCamelCase__     :str							      =				(
							    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)
							)
							UpperCamelCase__     :Optional[int]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							UpperCamelCase__     :Dict							      =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				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"]
												UpperCamelCase__     :List[str]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     (padded_height, padded_width)							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__     :Union[str, Any]							      =				FocalNetModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :Dict							      =				_config_zero_init(UpperCamelCase_							)
							for model_class in self.all_model_classes:
												UpperCamelCase__     :int							      =				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      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''							) if is_vision_available() else None




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''							).to(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.default_image_processor

							UpperCamelCase__     :str							      =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							UpperCamelCase__     :Any							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__     :Tuple							      =				model(**UpperCamelCase_							)

							# verify the logits
							UpperCamelCase__     :Any							      =				torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				torch.tensor([0.2166, -0.4368, 0.2191]							).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      lowercase			(							A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       (FocalNetBackbone,) if is_torch_available() else ()
		_a									=       FocalNetConfig

		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModelTester(self							)