


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  str						      =	StableDiffusionInstructPixaPixPipeline
     UpperCAmelCase__ :  int						      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
     UpperCAmelCase__ :  str						      =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase__ :  List[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
     UpperCAmelCase__ :  Optional[Any]						      =	IMAGE_TO_IMAGE_IMAGE_PARAMS





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[int]     =	UNetaDConditionModel(
            block_out_channels=(32, 64)    ,	layers_per_block=2    ,	sample_size=32    ,	in_channels=8    ,	out_channels=4    ,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,	cross_attention_dim=32    ,	)
        __lowerCamelCase:				Optional[int]     =	PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_							)
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	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							)
        __lowerCamelCase:				Optional[int]     =	CLIPTextConfig(
            bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=32    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	)
        __lowerCamelCase:				List[Any]     =	CLIPTextModel(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        __lowerCamelCase:				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					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0							):
        __lowerCamelCase:				Union[str, Any]     =	floats_tensor((1, 3, 32, 32)    ,	rng=random.Random(SCREAMING_SNAKE_CASE_							)							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	image.cpu().permute(0    ,	2    ,	3    ,	1							)[0]
        __lowerCamelCase:				Union[str, Any]     =	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_							)							).convert("""RGB"""							)
        if str(SCREAMING_SNAKE_CASE_							).startswith("""mps"""							):
           __lowerCamelCase:				int     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        else:
           __lowerCamelCase:				Optional[Any]     =	torch.Generator(device=SCREAMING_SNAKE_CASE_							).manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	{
            """prompt""": """A painting of a squirrel eating a burger""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """image_guidance_scale""": 1,
            """output_type""": """numpy""",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[Any]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Dict     =	self.get_dummy_components()
        __lowerCamelCase:				str     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	sd_pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Optional[Any]     =	image[0, -3:, -3:, -1]
        assert image.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Union[str, Any]     =	np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Optional[Any]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Optional[Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Any     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	"""french fries"""
        __lowerCamelCase:				Optional[int]     =	sd_pipe(**SCREAMING_SNAKE_CASE_    ,	negative_prompt=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	output.images
        __lowerCamelCase:				Tuple     =	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __lowerCamelCase:				str     =	np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Any     =	self.get_dummy_components()
        __lowerCamelCase:				int     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	[inputs["""prompt"""]] * 2

        __lowerCamelCase:				List[Any]     =	np.array(inputs["""image"""]							).astype(np.floataa							) / 255.0
        __lowerCamelCase:				List[Any]     =	torch.from_numpy(SCREAMING_SNAKE_CASE_							).unsqueeze(0							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	image / 2 + 0.5
        __lowerCamelCase:				List[Any]     =	image.permute(0    ,	3    ,	1    ,	2							)
        __lowerCamelCase:				int     =	image.repeat(2    ,	1    ,	1    ,	1							)

        __lowerCamelCase:				str     =	sd_pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Dict     =	image[-1, -3:, -3:, -1]

        assert image.shape == (2, 32, 32, 3)
        __lowerCamelCase:				str     =	np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Union[str, Any]     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
        __lowerCamelCase:				Optional[Any]     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085    ,	beta_end=0.012    ,	beta_schedule="""scaled_linear"""							)
        __lowerCamelCase:				Optional[int]     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	sd_pipe.to(SCREAMING_SNAKE_CASE_							)
        sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Dict     =	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[str]     =	sd_pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Dict     =	image[0, -3:, -3:, -1]

        __lowerCamelCase:				List[str]     =	[round(SCREAMING_SNAKE_CASE_    ,	4							) for x in image_slice.flatten().tolist()]
        print(""",""".join([str(SCREAMING_SNAKE_CASE_							) for x in slice]							)							)

        assert image.shape == (1, 32, 32, 3)
        __lowerCamelCase:				Dict     =	np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Dict     =	self.get_dummy_components()
        __lowerCamelCase:				Optional[Any]     =	StableDiffusionInstructPixaPixPipeline(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE_    ,	do_normalize=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	pipe(**self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_    ,	input_image_type="""pt"""							)							)[0]

        __lowerCamelCase:				List[str]     =	components["""vae"""]
        __lowerCamelCase:				Tuple     =	self.get_dummy_inputs_by_type(SCREAMING_SNAKE_CASE_    ,	input_image_type="""pt"""							)

        for image_param in self.image_latents_params:
           if image_param in inputs.keys():
              __lowerCamelCase:				List[Any]     =	vae.encode(inputs[image_param]							).latent_dist.mode()

        __lowerCamelCase:				Dict     =	pipe(**SCREAMING_SNAKE_CASE_							)[0]

        __lowerCamelCase:				Tuple     =	np.abs(out - out_latents_inputs							).max()
        self.assertLess(SCREAMING_SNAKE_CASE_    ,	1E-4    ,	"""passing latents as image input generate different result from passing image"""							)



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Any=0							):
        __lowerCamelCase:				Any     =	torch.manual_seed(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[int]     =	load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""							)
        __lowerCamelCase:				List[str]     =	{
            """prompt""": """turn him into a cyborg""",
            """image""": image,
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """image_guidance_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				List[Any]     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_							)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Dict     =	self.get_inputs()
        __lowerCamelCase:				Optional[Any]     =	pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				Optional[Any]     =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        __lowerCamelCase:				str     =	np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]							)

        assert np.abs(expected_slice - image_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        __lowerCamelCase:				Any     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				str     =	self.get_inputs()
        __lowerCamelCase:				Optional[int]     =	pipe(**SCREAMING_SNAKE_CASE_							).images
        __lowerCamelCase:				List[str]     =	image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        __lowerCamelCase:				int     =	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]							)

        assert np.abs(expected_slice - image_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				List[Any]     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	DDIMScheduler.from_config(pipe.scheduler.config							)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 512, 512, 3)
        __lowerCamelCase:				Optional[int]     =	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]							)

        assert np.abs(expected_slice - image_slice							).max() < 1E-3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				Any     =	0

        def callback_fn(SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				torch.FloatTensor							) -> None:
           __lowerCamelCase:				Tuple     =	True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
              __lowerCamelCase:				Optional[Any]     =	latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              __lowerCamelCase:				List[Any]     =	latents[0, -3:, -3:, -1]
              __lowerCamelCase:				Dict     =	np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]							)

              assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2
           elif step == 2:
              __lowerCamelCase:				Tuple     =	latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 64, 64)
              __lowerCamelCase:				Any     =	latents[0, -3:, -3:, -1]
              __lowerCamelCase:				int     =	np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]							)

              assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5E-2

        __lowerCamelCase:				List[Any]     =	False

        __lowerCamelCase:				Any     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa							)
        __lowerCamelCase:				str     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[Any]     =	self.get_inputs()
        pipe(**SCREAMING_SNAKE_CASE_    ,	callback=SCREAMING_SNAKE_CASE_    ,	callback_steps=1							)
        assert callback_fn.has_been_called
        assert number_of_steps == 3





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __lowerCamelCase:				str     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            """timbrooks/instruct-pix2pix"""    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	torch_dtype=torch.floataa							)
        __lowerCamelCase:				Tuple     =	pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing(1							)
        pipe.enable_sequential_cpu_offload()

        __lowerCamelCase:				List[Any]     =	self.get_inputs()
        __lowerCamelCase:				List[Any]     =	pipe(**SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Optional[int]     =	torch.cuda.max_memory_allocated()
        # make sure that less than 2.2 GB is allocated
        assert mem_bytes < 2.2 * 10**9







     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				List[Any]     =	self.get_inputs()
        # resize to resolution that is divisible by 8 but not 16 or 32
        __lowerCamelCase:				List[str]     =	inputs["""image"""].resize((504, 504)							)

        __lowerCamelCase:				str     =	"""timbrooks/instruct-pix2pix"""
        __lowerCamelCase:				Union[str, Any]     =	StableDiffusionInstructPixaPixPipeline.from_pretrained(
            SCREAMING_SNAKE_CASE_    ,	safety_checker=SCREAMING_SNAKE_CASE_    ,	)
        pipe.to(SCREAMING_SNAKE_CASE_							)
        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_							)
        pipe.enable_attention_slicing()

        __lowerCamelCase:				Optional[int]     =	pipe(**SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Union[str, Any]     =	output.images[0]

        __lowerCamelCase:				Optional[Any]     =	image[255:258, 383:386, -1]

        assert image.shape == (504, 504, 3)
        __lowerCamelCase:				List[Any]     =	np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor



class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple=13    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]=3    ,	SCREAMING_SNAKE_CASE_					:				Any=True    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=True    ,	SCREAMING_SNAKE_CASE_					:				Dict=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple=0.1    ,	SCREAMING_SNAKE_CASE_					:				int=224    ,	SCREAMING_SNAKE_CASE_					:				Tuple=1000    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=[3, 3, 6, 4]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=[48, 56, 112, 220]    ,	):
        __lowerCamelCase:				Optional[Any]     =	parent
        __lowerCamelCase:				Any     =	batch_size
        __lowerCamelCase:				str     =	num_channels
        __lowerCamelCase:				str     =	is_training
        __lowerCamelCase:				List[str]     =	use_labels
        __lowerCamelCase:				List[Any]     =	hidden_dropout_prob
        __lowerCamelCase:				int     =	attention_probs_dropout_prob
        __lowerCamelCase:				Union[str, Any]     =	num_labels
        __lowerCamelCase:				int     =	image_size
        __lowerCamelCase:				Optional[Any]     =	layer_depths
        __lowerCamelCase:				Dict     =	embed_dims





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        __lowerCamelCase:				Any     =	None
        if self.use_labels:
           __lowerCamelCase:				Tuple     =	ids_tensor([self.batch_size]    ,	self.num_labels							)

        __lowerCamelCase:				Dict     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        return SwiftFormerConfig(
            depths=self.layer_depths    ,	embed_dims=self.embed_dims    ,	mlp_ratio=4    ,	downsamples=[True, True, True, True]    ,	hidden_act="""gelu"""    ,	num_labels=self.num_labels    ,	down_patch_size=3    ,	down_stride=2    ,	down_pad=1    ,	drop_rate=0.0    ,	drop_path_rate=0.0    ,	use_layer_scale=SCREAMING_SNAKE_CASE_    ,	layer_scale_init_value=1E-5    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)							)





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

        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				Tuple     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
        __lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Dict						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(
            self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





     @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Optional[Any]     =	model_class(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Optional[Any]     =	model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,	nn.Linear							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           __lowerCamelCase:				List[str]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)





     @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        pass





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

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

           __lowerCamelCase:				Dict     =	outputs.hidden_states

           __lowerCamelCase:				Tuple     =	8
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							)  # TODO

           # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
           # with the width and height being successively divided by 2, after every 2 blocks
           for i in range(len(SCREAMING_SNAKE_CASE_							)							):
              self.assertEqual(
                  hidden_states[i].shape    ,	torch.Size(
                      [
                          self.model_tester.batch_size,
                          self.model_tester.embed_dims[i // 2],
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                          (self.model_tester.image_size // 4) // 2 ** (i // 2),
                      ]							)    ,	)

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

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __lowerCamelCase:				Optional[int]     =	True

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        def _config_zero_init(SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				Dict     =	copy.deepcopy(SCREAMING_SNAKE_CASE_							)
           for key in configs_no_init.__dict__.keys():
              if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	1E-1_0							)
              if isinstance(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				Tuple     =	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           return configs_no_init

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

        __lowerCamelCase:				str     =	_config_zero_init(SCREAMING_SNAKE_CASE_							)
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	model_class(config=SCREAMING_SNAKE_CASE_							)
           for name, param in model.named_parameters():
              if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9) / 1E9).round().item()    ,	[0.0, 1.0]    ,	msg=F'Parameter {name} of model {model_class} seems not properly initialized'    ,	)







     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        pass



def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





     @cached_property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.default_image_processor
        __lowerCamelCase:				Tuple     =	prepare_img()
        __lowerCamelCase:				int     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(**SCREAMING_SNAKE_CASE_							)

        # verify the logits
        __lowerCamelCase:				List[Any]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]							).to(SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)


