





'''simple docstring'''







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  (lowerCamelCase_       ,							lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =StableUnCLIPImgaImgPipeline
				__a            =TEXT_GUIDED_IMAGE_VARIATION_PARAMS
				__a            =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__a            =frozenset(
				    []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__a            =frozenset([]      )




				def   UpperCamelCase__   (  self     :					Dict					):
								_a								= 32
								_a								= embedder_hidden_size

								# image encoding components

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

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

								# regular denoising components

								torch.manual_seed(0					)
								_a								= StableUnCLIPImageNormalizer(embedding_dim=__a					)
								_a								= DDPMScheduler(beta_schedule="squaredcos_cap_v2"					)

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

								torch.manual_seed(0					)
								_a								= CLIPTextModel(
								    CLIPTextConfig(
								        bos_token_id=0					,				eos_token_id=2					,				hidden_size=__a					,				projection_dim=32					,				intermediate_size=37					,				layer_norm_eps=1e-0_5					,				num_attention_heads=4					,				num_hidden_layers=5					,				pad_token_id=1					,				vocab_size=10_00					,				)					)

								torch.manual_seed(0					)
								_a								= 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=__a					,				layers_per_block=1					,				upcast_attention=__a					,				use_linear_projection=__a					,				)

								torch.manual_seed(0					)
								_a								= DDIMScheduler(
								    beta_schedule="scaled_linear"					,				beta_start=0.00085					,				beta_end=0.012					,				prediction_type="v_prediction"					,				set_alpha_to_one=__a					,				steps_offset=1					,				)

								torch.manual_seed(0					)
								_a								= AutoencoderKL()

								_a								= {
								    # 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   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					Optional[Any]					,				__a     :					List[Any]=0					,				__a     :					Dict=True					):
								if str(__a					).startswith("mps"					):
												_a								= torch.manual_seed(__a					)
								else:
												_a								= torch.Generator(device=__a					).manual_seed(__a					)

								_a								= floats_tensor((1, 3, 32, 32)					,				rng=random.Random(__a					)					).to(__a					)

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

								return {
								    "prompt": "An anime racoon running a marathon",
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}




				@skip_mps
				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								_a								= "cpu"  # ensure determinism for the device-dependent torch.Generator
								_a								= self.get_dummy_components()
								_a								= StableUnCLIPImgaImgPipeline(**__a					)
								_a								= sd_pipe.to(__a					)
								sd_pipe.set_progress_bar_config(disable=__a					)

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

								assert image.shape == (1, 32, 32, 3)
								_a								= np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]					)

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




				def   UpperCamelCase__   (  self     :					List[Any]					):
								_a								= torch_device in ["cpu", "mps"]

								self._test_attention_slicing_forward_pass(test_max_difference=__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= torch_device in ["cpu", "mps"]

								self._test_inference_batch_single_identical(test_max_difference=__a					)




				@unittest.skipIf(
				    torch_device != "cuda" or not is_xformers_available()					,				reason="XFormers attention is only available with CUDA and `xformers` installed"					,				)
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__a					)


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






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					):
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def   UpperCamelCase__   (  self     :					str					):
								_a								= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"					)

								_a								= 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"					)

								_a								= StableUnCLIPImgaImgPipeline.from_pretrained(
								    "fusing/stable-unclip-2-1-l-img2img"					,				torch_dtype=torch.floataa					)
								pipe.to(__a					)
								pipe.set_progress_bar_config(disable=__a					)
								# 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()

								_a								= torch.Generator(device="cpu"					).manual_seed(0					)
								_a								= pipe(__a					,				"anime turle"					,				generator=__a					,				output_type="np"					)

								_a								= output.images[0]

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

								assert_mean_pixel_difference(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png"					)

								_a								= 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"					)

								_a								= StableUnCLIPImgaImgPipeline.from_pretrained(
								    "fusing/stable-unclip-2-1-h-img2img"					,				torch_dtype=torch.floataa					)
								pipe.to(__a					)
								pipe.set_progress_bar_config(disable=__a					)
								# 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()

								_a								= torch.Generator(device="cpu"					).manual_seed(0					)
								_a								= pipe(__a					,				"anime turle"					,				generator=__a					,				output_type="np"					)

								_a								= output.images[0]

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

								assert_mean_pixel_difference(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= 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()

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

								_a								= pipe(
								    __a					,				"anime turtle"					,				num_inference_steps=2					,				output_type="np"					,				)

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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


A_							: int						 =	{"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
   from .tokenization_bertweet import BertweetTokenizer

else:
   import sys

   A_							: int						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



