"""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        snake_case (				lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				StableUnCLIPPipeline
					snake_case__       					=				TEXT_TO_IMAGE_PARAMS
					snake_case__       					=				TEXT_TO_IMAGE_BATCH_PARAMS
					snake_case__       					=				TEXT_TO_IMAGE_IMAGE_PARAMS
					snake_case__       					=				TEXT_TO_IMAGE_IMAGE_PARAMS

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







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							32
									UpperCAmelCase__    =							embedder_hidden_size

									# prior components

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

									torch.manual_seed(0    )
									UpperCAmelCase__    =							CLIPTextModelWithProjection(
									    CLIPTextConfig(
									        bos_token_id=0					,eos_token_id=2					,hidden_size=_lowerCamelCase					,projection_dim=_lowerCamelCase					,intermediate_size=37					,layer_norm_eps=1e-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1_000					,)    )

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

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

									# regular denoising components

									torch.manual_seed(0    )
									UpperCAmelCase__    =							StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase    )
									UpperCAmelCase__    =							DDPMScheduler(beta_schedule='squaredcos_cap_v2'    )

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

									torch.manual_seed(0    )
									UpperCAmelCase__    =							CLIPTextModel(
									    CLIPTextConfig(
									        bos_token_id=0					,eos_token_id=2					,hidden_size=_lowerCamelCase					,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=1_000					,)    )

									torch.manual_seed(0    )
									UpperCAmelCase__    =							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=_lowerCamelCase					,layers_per_block=1					,upcast_attention=_lowerCamelCase					,use_linear_projection=_lowerCamelCase					,)

									torch.manual_seed(0    )
									UpperCAmelCase__    =							DDIMScheduler(
									    beta_schedule='scaled_linear'					,beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,prediction_type='v_prediction'					,set_alpha_to_one=_lowerCamelCase					,steps_offset=1					,)

									torch.manual_seed(0    )
									UpperCAmelCase__    =							AutoencoderKL()

									UpperCAmelCase__    =							{
									    # 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     :			Optional[int]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Any=0    ):





									if str(_lowerCamelCase    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(_lowerCamelCase    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
									UpperCAmelCase__    =							{
									    '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     :			Tuple    ):
									UpperCAmelCase__    =							torch_device == 'cpu'

									self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							torch_device in ['cpu', 'mps']

									self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase    )






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




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							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__    =							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__    =							torch.Generator(device='cpu'    ).manual_seed(0    )
									UpperCAmelCase__    =							pipe('anime turle'					,generator=_lowerCamelCase					,output_type='np'    )

									UpperCAmelCase__    =							output.images[0]

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

									assert_mean_pixel_difference(_lowerCamelCase					,_lowerCamelCase    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									UpperCAmelCase__    =							pipe(
									    'anime turtle'					,prior_num_inference_steps=2					,num_inference_steps=2					,output_type='np'					,)

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



"""simple docstring"""






import re



def 						a_		(      lowerCamelCase ):
				return [char.split() for char in re.split(r'[^ a-z A-Z 0-9 \s]'      ,    str_ )]



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							split_input(str_ )
				return "".join(
				    [''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				try:
								UpperCAmelCase__    =							split_input(lowerCamelCase )
								if upper:
												UpperCAmelCase__    =							''.join(
												    [
												        separator.join([char.upper() for char in sub_str] )
												        for sub_str in string_split
												    ] )
								else:
												UpperCAmelCase__    =							''.join(
												    [
												        separator.join([char.lower() for char in sub_str] )
												        for sub_str in string_split
												    ] )
								return res_str
				except IndexError:
								return "not valid string"



def 						a_		(      lowerCamelCase ):
				return to_simple_case(lowerCamelCase )



def 						a_		(      lowerCamelCase ):
				try:
								UpperCAmelCase__    =							to_simple_case(lowerCamelCase )
								return res_str[0].lower() + res_str[1:]
				except IndexError:
								return "not valid string"



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return to_complex_case(lowerCamelCase      ,    lowerCamelCase      ,    '_' )



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return to_complex_case(lowerCamelCase      ,    lowerCamelCase      ,    '-' )


if __name__ == "__main__":
						__import__('doctest').testmod()



