






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCamelCase					:		int	  =						{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Union[str, Any]	  =						[
							    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SwinForImageClassification""",
							    """SwinForMaskedImageModeling""",
							    """SwinModel""",
							    """SwinPreTrainedModel""",
							    """SwinBackbone""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						[
							    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSwinForImageClassification""",
							    """TFSwinForMaskedImageModeling""",
							    """TFSwinModel""",
							    """TFSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

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


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableUnCLIPImgaImgPipeline
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_UpperCAmelCase     =       frozenset(
							    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							_UpperCAmelCase     =       frozenset([]	)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											snake_case__									=     32
											snake_case__									=     embedder_hidden_size

											# image encoding components

											snake_case__									=     CLIPImageProcessor(crop_size=32       ,	size=32						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPVisionModelWithProjection(
											    CLIPVisionConfig(
											        hidden_size=UpperCamelCase       ,	projection_dim=UpperCamelCase       ,	num_hidden_layers=5       ,	num_attention_heads=4       ,	image_size=32       ,	intermediate_size=37       ,	patch_size=1       ,	)						)

											# regular denoising components

											torch.manual_seed(0						)
											snake_case__									=     StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase						)
											snake_case__									=     DDPMScheduler(beta_schedule='squaredcos_cap_v2'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=UpperCamelCase       ,	projection_dim=32       ,	intermediate_size=37       ,	layer_norm_eps=1e-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)						)

											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,	up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,	block_out_channels=(32, 64)       ,	attention_head_dim=(2, 4)       ,	class_embed_type='projection'       ,	projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,	cross_attention_dim=UpperCamelCase       ,	layers_per_block=1       ,	upcast_attention=UpperCamelCase       ,	use_linear_projection=UpperCamelCase       ,	)

											torch.manual_seed(0						)
											snake_case__									=     DDIMScheduler(
											    beta_schedule='scaled_linear'       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	prediction_type='v_prediction'       ,	set_alpha_to_one=UpperCamelCase       ,	steps_offset=1       ,	)

											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL()

											snake_case__									=     {
											    # image encoding components
											    'feature_extractor': feature_extractor,
											    'image_encoder': image_encoder.eval(),
											    # image noising components
											    'image_normalizer': image_normalizer.eval(),
											    'image_noising_scheduler': image_noising_scheduler,
											    # regular denoising components
											    'tokenizer': tokenizer,
											    'text_encoder': text_encoder.eval(),
											    'unet': unet.eval(),
											    'scheduler': scheduler,
											    'vae': vae.eval(),
											}

											return components
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Tuple=0       ,	UpperCamelCase:      Union[str, Any]=True						)		->		Any:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)

											snake_case__									=     floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)

											if pil_image:
															snake_case__									=     input_image * 0.5 + 0.5
															snake_case__									=     input_image.clamp(0       ,	1						)
															snake_case__									=     input_image.cpu().permute(0       ,	2       ,	3       ,	1						).float().numpy()
															snake_case__									=     DiffusionPipeline.numpy_to_pil(UpperCamelCase						)[0]

											return {
											    "prompt": "An anime racoon running a marathon",
											    "image": input_image,
											    "generator": generator,
											    "num_inference_steps": 2,
											    "output_type": "np",
											}
							@skip_mps
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case__									=     self.get_dummy_components()
											snake_case__									=     StableUnCLIPImgaImgPipeline(**UpperCamelCase						)
											snake_case__									=     sd_pipe.to(UpperCamelCase						)
											sd_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     self.get_dummy_inputs(UpperCamelCase						)
											inputs.update({'image_embeds': None}						)
											snake_case__									=     sd_pipe(**UpperCamelCase						).images
											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case__									=     np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		int:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase						)


							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()       ,	reason='XFormers attention is only available with CUDA and `xformers` installed'       ,	)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-l-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		str:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     pipe(
											    UpperCamelCase       ,	'anime turtle'       ,	num_inference_steps=2       ,	output_type='np'       ,	)

											snake_case__									=     torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 10**9




