"""simple docstring"""



import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()



class      _A	(						_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Tuple							=					StableUnCLIPPipeline
    UpperCamelCase_	:					Dict							=					TEXT_TO_IMAGE_PARAMS
    UpperCamelCase_	:					List[Any]							=					TEXT_TO_IMAGE_BATCH_PARAMS
    UpperCamelCase_	:					Any							=					TEXT_TO_IMAGE_IMAGE_PARAMS
    UpperCamelCase_	:					Optional[int]							=					TEXT_TO_IMAGE_IMAGE_PARAMS

    # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
    UpperCamelCase_	:					Optional[int]							=					False
    def  lowercase					(		self							:     Optional[Any]    )    ->       str:
         __snake_case				=							32
         __snake_case				=							embedder_hidden_size

         # prior components

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

         torch.manual_seed(0    )
         __snake_case				=							CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0	,      eos_token_id=2	,      hidden_size=A_	,      projection_dim=A_	,      intermediate_size=37	,      layer_norm_eps=1E-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=1_000	,      )    )

         torch.manual_seed(0    )
         __snake_case				=							PriorTransformer(
             num_attention_heads=2	,      attention_head_dim=12	,      embedding_dim=A_	,      num_layers=1	,      )

         torch.manual_seed(0    )
         __snake_case				=							DDPMScheduler(
             variance_type='''fixed_small_log'''	,      prediction_type='''sample'''	,      num_train_timesteps=1_000	,      clip_sample=A_	,      clip_sample_range=5.0	,      beta_schedule='''squaredcos_cap_v2'''	,      )

         # regular denoising components

         torch.manual_seed(0    )
         __snake_case				=							StableUnCLIPImageNormalizer(embedding_dim=A_    )
         __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=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=1_000	,      )    )

         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=A_	,      layers_per_block=1	,      upcast_attention=A_	,      use_linear_projection=A_	,      )

         torch.manual_seed(0    )
         __snake_case				=							DDIMScheduler(
             beta_schedule='''scaled_linear'''	,      beta_start=0.0_00_85	,      beta_end=0.0_12	,      prediction_type='''v_prediction'''	,      set_alpha_to_one=A_	,      steps_offset=1	,      )

         torch.manual_seed(0    )
         __snake_case				=							AutoencoderKL()

         __snake_case				=							{
             # prior components
             '''prior_tokenizer''': prior_tokenizer,
             '''prior_text_encoder''': prior_text_encoder,
             '''prior''': prior,
             '''prior_scheduler''': prior_scheduler,
             # image noising components
             '''image_normalizer''': image_normalizer,
             '''image_noising_scheduler''': image_noising_scheduler,
             # regular denoising components
             '''tokenizer''': tokenizer,
             '''text_encoder''': text_encoder,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
         }

         return components
    def  lowercase					(		self							:     Tuple	,      A_							:     int	,      A_							:     Optional[Any]=0    )    ->       Optional[int]:
         if str(A_    ).startswith('''mps'''    ):
              __snake_case				=							torch.manual_seed(A_    )
         else:
              __snake_case				=							torch.Generator(device=A_    ).manual_seed(A_    )
         __snake_case				=							{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''prior_num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs
    def  lowercase					(		self							:     List[Any]    )    ->       List[Any]:
         __snake_case				=							torch_device == '''cpu'''

         self._test_attention_slicing_forward_pass(test_max_difference=A_    )







    def  lowercase					(		self							:     Any    )    ->       Any:
         __snake_case				=							torch_device in ['''cpu''', '''mps''']

         self._test_inference_batch_single_identical(test_max_difference=A_    )






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




    """simple docstring"""




    def  lowercase					(		self							:     List[str]    )    ->       str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''    )

         __snake_case				=							StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''	,      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()

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

         __snake_case				=							output.images[0]

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

         assert_mean_pixel_difference(A_	,      A_    )







    def  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __snake_case				=							StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''	,      torch_dtype=torch.floataa    )
         __snake_case				=							pipe.to(A_    )
         pipe.set_progress_bar_config(disable=A_    )
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         __snake_case				=							pipe(
             '''anime turtle'''	,      prior_num_inference_steps=2	,      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
"""simple docstring"""



import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
       import onnxruntime as ort



class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
    def  lowercase					(		self							:     Optional[int]	,      A_							:     List[str]=0    )    ->       int:
         __snake_case				=							floats_tensor((1, 3, 128, 128)	,      rng=random.Random(A_    )    )
         __snake_case				=							np.random.RandomState(A_    )
         __snake_case				=							{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.75,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs
    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_96_43, 0.5_84_84, 0.5_03_14, 0.5_87_60, 0.5_53_68, 0.5_96_43, 0.5_15_29, 0.4_12_17, 0.4_90_87]    )
         assert np.abs(image_slice - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Tuple    )    ->       Optional[int]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							PNDMScheduler.from_config(pipe.scheduler.config	,      skip_prk_steps=A_    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_17_37, 0.5_46_42, 0.5_31_83, 0.5_44_65, 0.5_27_42, 0.6_05_25, 0.4_99_69, 0.4_06_55, 0.4_81_54]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Optional[int]    )    ->       str:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         # warmup pass to apply optimizations
         __snake_case				=							pipe(**self.get_dummy_inputs()    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_27_61, 0.5_99_77, 0.4_90_33, 0.4_96_19, 0.5_42_82, 0.5_03_11, 0.4_76_00, 0.4_09_18, 0.4_52_03]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     str    )    ->       List[str]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-1
    def  lowercase					(		self							:     Optional[int]    )    ->       Union[str, Any]:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.5_29_11, 0.6_00_04, 0.4_92_29, 0.4_98_05, 0.5_45_02, 0.5_06_80, 0.4_77_77, 0.4_10_28, 0.4_53_04]    )

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







    def  lowercase					(		self							:     List[str]    )    ->       Any:
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint	,      provider='''CPUExecutionProvider'''    )
         __snake_case				=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							self.get_dummy_inputs()
         __snake_case				=							pipe(**A_    ).images
         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case				=							np.array([0.6_53_31, 0.5_82_77, 0.4_82_04, 0.5_60_59, 0.5_36_65, 0.5_62_35, 0.5_09_69, 0.4_00_09, 0.4_65_52]    )

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






@nightly
@require_onnxruntime
@require_torch_gpu
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @property
    def  lowercase					(		self							:     str    )    ->       int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def  lowercase					(		self							:     Dict    )    ->       Union[str, Any]:
         __snake_case				=							ort.SessionOptions()
         __snake_case				=							False
         return options
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         # using the PNDM scheduler by default
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''	,      revision='''onnx'''	,      safety_checker=A_	,      feature_extractor=A_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							'''A fantasy landscape, trending on artstation'''

         __snake_case				=							np.random.RandomState(0    )
         __snake_case				=							pipe(
             prompt=A_	,      image=A_	,      strength=0.75	,      guidance_scale=7.5	,      num_inference_steps=10	,      generator=A_	,      output_type='''np'''	,      )
         __snake_case				=							output.images
         __snake_case				=							images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							np.array([0.49_09, 0.50_59, 0.53_72, 0.46_23, 0.48_76, 0.50_49, 0.48_20, 0.49_56, 0.50_19]    )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







    def  lowercase					(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''    )
         __snake_case				=							init_image.resize((768, 512)    )
         __snake_case				=							LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''	,      subfolder='''scheduler'''	,      revision='''onnx'''    )
         __snake_case				=							OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''	,      revision='''onnx'''	,      scheduler=A_	,      safety_checker=A_	,      feature_extractor=A_	,      provider=self.gpu_provider	,      sess_options=self.gpu_options	,      )
         pipe.set_progress_bar_config(disable=A_    )

         __snake_case				=							'''A fantasy landscape, trending on artstation'''

         __snake_case				=							np.random.RandomState(0    )
         __snake_case				=							pipe(
             prompt=A_	,      image=A_	,      strength=0.75	,      guidance_scale=7.5	,      num_inference_steps=20	,      generator=A_	,      output_type='''np'''	,      )
         __snake_case				=							output.images
         __snake_case				=							images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case				=							np.array([0.80_43, 0.9_26, 0.95_81, 0.81_19, 0.89_54, 0.9_13, 0.72_09, 0.74_63, 0.74_31]    )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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