



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference



class 				__snake_case						(     UpperCamelCase_						,unittest.TestCase						):
							_a			      = ShapEPipeline
							_a			      = ['''prompt''']
							_a			      = ['''prompt''']
							_a			      = [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							_a			      = False




							@property
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									return 3_2




							@property
							def    UpperCAmelCase__			(       self     :       List[Any]):
									return 3_2




							@property
							def    UpperCAmelCase__			(       self     :       str):
									return self.time_input_dim * 4




							@property
							def    UpperCAmelCase__			(       self     :       Tuple):
									return 8




							@property
							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     List[str]									= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')
									return tokenizer




							@property
							def    UpperCAmelCase__			(       self     :       List[Any]):
									torch.manual_seed(0)
									lowerCAmelCase_			:     Dict									= CLIPTextConfig(
									    bos_token_id=0				,				eos_token_id=2				,				hidden_size=self.text_embedder_hidden_size				,				projection_dim=self.text_embedder_hidden_size				,				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				,				)
									return CLIPTextModelWithProjection(A_)




							@property
							def    UpperCAmelCase__			(       self     :       str):
									torch.manual_seed(0)

									lowerCAmelCase_			:     int									= {
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 1_6,
									    '''embedding_dim''': self.time_input_dim,
									    '''num_embeddings''': 3_2,
									    '''embedding_proj_dim''': self.text_embedder_hidden_size,
									    '''time_embed_dim''': self.time_embed_dim,
									    '''num_layers''': 1,
									    '''clip_embed_dim''': self.time_input_dim * 2,
									    '''additional_embeddings''': 0,
									    '''time_embed_act_fn''': '''gelu''',
									    '''norm_in_type''': '''layer''',
									    '''encoder_hid_proj_type''': None,
									    '''added_emb_type''': None,
									}

									lowerCAmelCase_			:     Union[str, Any]									= PriorTransformer(**A_)
									return model




							@property
							def    UpperCAmelCase__			(       self     :       List[Any]):
									torch.manual_seed(0)

									lowerCAmelCase_			:     Optional[Any]									= {
									    '''param_shapes''': (
									        (self.renderer_dim, 9_3),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    '''d_latent''': self.time_input_dim,
									    '''d_hidden''': self.renderer_dim,
									    '''n_output''': 1_2,
									    '''background''': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									lowerCAmelCase_			:     Optional[int]									= ShapERenderer(**A_)
									return model




							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     Any									= self.dummy_prior
									lowerCAmelCase_			:     str									= self.dummy_text_encoder
									lowerCAmelCase_			:     List[str]									= self.dummy_tokenizer
									lowerCAmelCase_			:     str									= self.dummy_renderer

									lowerCAmelCase_			:     int									= HeunDiscreteScheduler(
									    beta_schedule='''exp'''				,				num_train_timesteps=1_0_2_4				,				prediction_type='''sample'''				,				use_karras_sigmas=A_				,				clip_sample=A_				,				clip_sample_range=1.0				,				)
									lowerCAmelCase_			:     Any									= {
									    '''prior''': prior,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''renderer''': renderer,
									    '''scheduler''': scheduler,
									}

									return components




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       int				,				A_     :       Tuple=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_			:     Optional[Any]									= torch.manual_seed(A_)
									else:
											lowerCAmelCase_			:     Any									= torch.Generator(device=A_).manual_seed(A_)
									lowerCAmelCase_			:     List[Any]									= {
									    '''prompt''': '''horse''',
									    '''generator''': generator,
									    '''num_inference_steps''': 1,
									    '''frame_size''': 3_2,
									    '''output_type''': '''np''',
									}
									return inputs




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     int									= '''cpu'''

									lowerCAmelCase_			:     List[str]									= self.get_dummy_components()

									lowerCAmelCase_			:     Any									= self.pipeline_class(**A_)
									lowerCAmelCase_			:     Tuple									= pipe.to(A_)

									pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_			:     List[Any]									= pipe(**self.get_dummy_inputs(A_))
									lowerCAmelCase_			:     List[str]									= output.images[0]
									lowerCAmelCase_			:     int									= image[0, -3:, -3:, -1]

									assert image.shape == (2_0, 3_2, 3_2, 3)

									lowerCAmelCase_			:     List[Any]									= np.array(
									    [
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									        0.0003_9216,
									    ])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




							def    UpperCAmelCase__			(       self     :       Any):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2])




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     Dict									= torch_device == '''cpu'''
									lowerCAmelCase_			:     int									= True

									self._test_inference_batch_single_identical(
									    batch_size=2				,				test_max_difference=A_				,				relax_max_difference=A_				,				)







							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     int									= self.get_dummy_components()
									lowerCAmelCase_			:     int									= self.pipeline_class(**A_)
									lowerCAmelCase_			:     List[str]									= pipe.to(A_)
									pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_			:     Dict									= 1
									lowerCAmelCase_			:     Optional[int]									= 2

									lowerCAmelCase_			:     int									= self.get_dummy_inputs(A_)

									for key in inputs.keys():
											if key in self.batch_params:
													lowerCAmelCase_			:     Dict									= batch_size * [inputs[key]]

									lowerCAmelCase_			:     str									= pipe(**A_				,				num_images_per_prompt=A_)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt





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




							def    UpperCAmelCase__			(       self     :       Dict):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Tuple									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/shap_e/test_shap_e_np_out.npy''')
									lowerCAmelCase_			:     Optional[int]									= ShapEPipeline.from_pretrained('''openai/shap-e''')
									lowerCAmelCase_			:     int									= pipe.to(A_)
									pipe.set_progress_bar_config(disable=A_)

									lowerCAmelCase_			:     Any									= torch.Generator(device=A_).manual_seed(0)

									lowerCAmelCase_			:     str									= pipe(
									    '''a shark'''				,				generator=A_				,				guidance_scale=15.0				,				num_inference_steps=6_4				,				frame_size=6_4				,				output_type='''np'''				,				).images[0]

									assert images.shape == (2_0, 6_4, 6_4, 3)

									assert_mean_pixel_difference(A_				,				A_)










import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						,unittest.TestCase						):
							_a			      = IFPipeline
							_a			      = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							_a			      = TEXT_TO_IMAGE_BATCH_PARAMS
							_a			      = PipelineTesterMixin.required_optional_params - {'''latents'''}




							def    UpperCAmelCase__			(       self     :       List[str]):
									return self._get_dummy_components()




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       List[Any]				,				A_     :       Any=0):
									if str(A_).startswith('''mps'''):
											lowerCAmelCase_			:     List[Any]									= torch.manual_seed(A_)
									else:
											lowerCAmelCase_			:     List[str]									= torch.Generator(device=A_).manual_seed(A_)

									lowerCAmelCase_			:     Optional[Any]									= {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}

									return inputs




							def    UpperCAmelCase__			(       self     :       int):
									self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''				,				reason='''float16 requires CUDA''')
							def    UpperCAmelCase__			(       self     :       str):
									# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
									super().test_save_load_floataa(expected_max_diff=1e-1)




							def    UpperCAmelCase__			(       self     :       str):
									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




							def    UpperCAmelCase__			(       self     :       int):
									self._test_save_load_local()




							def    UpperCAmelCase__			(       self     :       str):
									self._test_inference_batch_single_identical(
									    expected_max_diff=1e-2				,				)







							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()				,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''				,				)
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)





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




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




							def    UpperCAmelCase__			(       self     :       List[str]):
									# if

									lowerCAmelCase_			:     Dict									= IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''				,				variant='''fp16'''				,				torch_dtype=torch.floataa)

									lowerCAmelCase_			:     Dict									= IFSuperResolutionPipeline.from_pretrained(
									    '''DeepFloyd/IF-II-L-v1.0'''				,				variant='''fp16'''				,				torch_dtype=torch.floataa				,				text_encoder=A_				,				tokenizer=A_)

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to('''cuda''')

									lowerCAmelCase_					,  lowerCAmelCase_			:     List[str]									= pipe_a.encode_prompt('''anime turtle'''				,				device='''cuda''')

									del pipe_a.tokenizer
									del pipe_a.text_encoder
									gc.collect()

									lowerCAmelCase_			:     Tuple									= None
									lowerCAmelCase_			:     str									= None

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if(A_				,				A_				,				A_				,				A_)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									lowerCAmelCase_			:     List[str]									= IFImgaImgPipeline(**pipe_a.components)
									lowerCAmelCase_			:     Any									= IFImgaImgSuperResolutionPipeline(**pipe_a.components)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if_imgaimg(A_				,				A_				,				A_				,				A_)

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									lowerCAmelCase_			:     int									= IFInpaintingPipeline(**pipe_a.components)
									lowerCAmelCase_			:     Union[str, Any]									= IFInpaintingSuperResolutionPipeline(**pipe_a.components)

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

									self._test_if_inpainting(A_				,				A_				,				A_				,				A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				A_     :       Any				,				A_     :       str				,				A_     :       Union[str, Any]):
									# pipeline 1

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Optional[Any]									= torch.Generator(device='''cpu''').manual_seed(0)
									lowerCAmelCase_			:     Optional[Any]									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				num_inference_steps=2				,				generator=A_				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Dict									= output.images[0]

									assert image.shape == (6_4, 6_4, 3)

									lowerCAmelCase_			:     Any									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 1_3 * 1_0**9

									lowerCAmelCase_			:     List[str]									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
									assert_mean_pixel_difference(A_				,				A_)

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     List[str]									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     str									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)

									lowerCAmelCase_			:     Optional[int]									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				generator=A_				,				num_inference_steps=2				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									lowerCAmelCase_			:     Any									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 1_0**9

									lowerCAmelCase_			:     int									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
									assert_mean_pixel_difference(A_				,				A_)




							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[int]				,				A_     :       Any				,				A_     :       List[str]				,				A_     :       List[str]):
									# pipeline 1

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Any									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)

									lowerCAmelCase_			:     Tuple									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     Any									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				num_inference_steps=2				,				generator=A_				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images[0]

									assert image.shape == (6_4, 6_4, 3)

									lowerCAmelCase_			:     str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 1_0 * 1_0**9

									lowerCAmelCase_			:     Optional[int]									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
									assert_mean_pixel_difference(A_				,				A_)

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     int									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     List[Any]									= floats_tensor((1, 3, 2_5_6, 2_5_6)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     Any									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)

									lowerCAmelCase_			:     List[str]									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				original_image=A_				,				generator=A_				,				num_inference_steps=2				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Union[str, Any]									= output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									lowerCAmelCase_			:     str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 1_0**9

									lowerCAmelCase_			:     Tuple									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
									assert_mean_pixel_difference(A_				,				A_)







							def    UpperCAmelCase__			(       self     :       str				,				A_     :       Optional[Any]				,				A_     :       Optional[Any]				,				A_     :       Dict				,				A_     :       List[str]):
									# pipeline 1

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Union[str, Any]									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     int									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(1)).to(A_)

									lowerCAmelCase_			:     Optional[Any]									= torch.Generator(device='''cpu''').manual_seed(0)
									lowerCAmelCase_			:     Any									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				mask_image=A_				,				num_inference_steps=2				,				generator=A_				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     List[Any]									= output.images[0]

									assert image.shape == (6_4, 6_4, 3)

									lowerCAmelCase_			:     str									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 1_0 * 1_0**9

									lowerCAmelCase_			:     int									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
									assert_mean_pixel_difference(A_				,				A_)

									# pipeline 2

									_start_torch_memory_measurement()

									lowerCAmelCase_			:     Optional[Any]									= torch.Generator(device='''cpu''').manual_seed(0)

									lowerCAmelCase_			:     Optional[Any]									= floats_tensor((1, 3, 6_4, 6_4)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     Union[str, Any]									= floats_tensor((1, 3, 2_5_6, 2_5_6)				,				rng=random.Random(0)).to(A_)
									lowerCAmelCase_			:     Optional[Any]									= floats_tensor((1, 3, 2_5_6, 2_5_6)				,				rng=random.Random(1)).to(A_)

									lowerCAmelCase_			:     int									= pipe_a(
									    prompt_embeds=A_				,				negative_prompt_embeds=A_				,				image=A_				,				mask_image=A_				,				original_image=A_				,				generator=A_				,				num_inference_steps=2				,				output_type='''np'''				,				)

									lowerCAmelCase_			:     Tuple									= output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)

									lowerCAmelCase_			:     int									= torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 1_0**9

									lowerCAmelCase_			:     Any									= load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
									assert_mean_pixel_difference(A_				,				A_)





def 			UpperCamelCase(  ):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()





