



"""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 							_UpperCAmelCase							( _SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,_SCREAMING_SNAKE_CASE		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=StableUnCLIPPipeline
     a__									=TEXT_TO_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_BATCH_PARAMS
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS
     a__									=TEXT_TO_IMAGE_IMAGE_PARAMS

     # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
     a__									=False





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    3_2
         _UpperCAmelCase							:  Dict											=    embedder_hidden_size

         # prior components

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

         torch.manual_seed(0		)
         _UpperCAmelCase							:  Dict											=    CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=_lowerCAmelCase       ,	projection_dim=_lowerCAmelCase       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	)		)

         torch.manual_seed(0		)
         _UpperCAmelCase							:  str											=    PriorTransformer(
             num_attention_heads=2       ,	attention_head_dim=1_2       ,	embedding_dim=_lowerCAmelCase       ,	num_layers=1       ,	)

         torch.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    DDPMScheduler(
             variance_type='''fixed_small_log'''       ,	prediction_type='''sample'''       ,	num_train_timesteps=1_0_0_0       ,	clip_sample=_lowerCAmelCase       ,	clip_sample_range=5.0       ,	beta_schedule='''squaredcos_cap_v2'''       ,	)

         # regular denoising components

         torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase		)
         _UpperCAmelCase							:  Dict											=    DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

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

         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=_lowerCAmelCase       ,	projection_dim=3_2       ,	intermediate_size=3_7       ,	layer_norm_eps=1E-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=1_0_0_0       ,	)		)

         torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    UNetaDConditionModel(
             sample_size=3_2       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')       ,	up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')       ,	block_out_channels=(3_2, 6_4)       ,	attention_head_dim=(2, 4)       ,	class_embed_type='''projection'''       ,	projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,	cross_attention_dim=_lowerCAmelCase       ,	layers_per_block=1       ,	upcast_attention=_lowerCAmelCase       ,	use_linear_projection=_lowerCAmelCase       ,	)

         torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    DDIMScheduler(
             beta_schedule='''scaled_linear'''       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	prediction_type='''v_prediction'''       ,	set_alpha_to_one=_lowerCAmelCase       ,	steps_offset=1       ,	)

         torch.manual_seed(0		)
         _UpperCAmelCase							:  int											=    AutoencoderKL()

         _UpperCAmelCase							:  Any											=    {
             # 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  __lowerCAmelCase	(					self       ,	A       ,	A=0		)							->					Tuple:
         if str(_lowerCAmelCase		).startswith('''mps'''		):
             _UpperCAmelCase							:  Dict											=    torch.manual_seed(_lowerCAmelCase		)
         else:
             _UpperCAmelCase							:  int											=    torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
         _UpperCAmelCase							:  int											=    {
             '''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  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  List[Any]											=    torch_device == '''cpu'''

         self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase		)





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  str											=    torch_device in ['''cpu''', '''mps''']

         self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase		)



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





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  Union[str, Any]											=    load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''		)

         _UpperCAmelCase							:  List[Any]											=    StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''       ,	torch_dtype=torch.floataa		)
         pipe.to(_lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
         # 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()

         _UpperCAmelCase							:  List[str]											=    torch.Generator(device='''cpu'''		).manual_seed(0		)
         _UpperCAmelCase							:  Optional[int]											=    pipe('''anime turle'''       ,	generator=_lowerCAmelCase       ,	output_type='''np'''		)

         _UpperCAmelCase							:  Optional[Any]											=    output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(_lowerCAmelCase       ,	_lowerCAmelCase		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         _UpperCAmelCase							:  List[str]											=    StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''       ,	torch_dtype=torch.floataa		)
         _UpperCAmelCase							:  List[str]											=    pipe.to(_lowerCAmelCase		)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         _UpperCAmelCase							:  Tuple											=    pipe(
             '''anime turtle'''       ,	prior_num_inference_steps=2       ,	num_inference_steps=2       ,	output_type='''np'''       ,	)

         _UpperCAmelCase							:  List[Any]											=    torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 1_0**9








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





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


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2