






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      SCREAMING_SNAKE_CASE  (				__UpperCAmelCase       ,       __UpperCAmelCase       ,       __UpperCAmelCase       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[Any]    =				StableUnCLIPPipeline
	_A       :				List[str]    =				TEXT_TO_IMAGE_PARAMS
	_A       :				Any    =				TEXT_TO_IMAGE_BATCH_PARAMS
	_A       :				Any    =				TEXT_TO_IMAGE_IMAGE_PARAMS
	_A       :				str    =				TEXT_TO_IMAGE_IMAGE_PARAMS

	# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
	_A       :				Optional[int]    =				False





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   32
		A_		:						Union[str, Any]	    =   embedder_hidden_size

		# prior components

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

		torch.manual_seed(0							)
		A_		:						Optional[Any]	    =   CLIPTextModelWithProjection(
		    CLIPTextConfig(
		        bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=UpperCAmelCase_     ,     projection_dim=UpperCAmelCase_     ,     intermediate_size=37     ,     layer_norm_eps=1e-0_5     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     pad_token_id=1     ,     vocab_size=1000     ,     )							)

		torch.manual_seed(0							)
		A_		:						Union[str, Any]	    =   PriorTransformer(
		    num_attention_heads=2     ,     attention_head_dim=12     ,     embedding_dim=UpperCAmelCase_     ,     num_layers=1     ,     )

		torch.manual_seed(0							)
		A_		:						Union[str, Any]	    =   DDPMScheduler(
		    variance_type="""fixed_small_log"""     ,     prediction_type="""sample"""     ,     num_train_timesteps=1000     ,     clip_sample=UpperCAmelCase_     ,     clip_sample_range=5.0     ,     beta_schedule="""squaredcos_cap_v2"""     ,     )

		# regular denoising components

		torch.manual_seed(0							)
		A_		:						Optional[int]	    =   StableUnCLIPImageNormalizer(embedding_dim=UpperCAmelCase_							)
		A_		:						Optional[Any]	    =   DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""							)

		torch.manual_seed(0							)
		A_		:						List[str]	    =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

		torch.manual_seed(0							)
		A_		:						List[Any]	    =   CLIPTextModel(
		    CLIPTextConfig(
		        bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=UpperCAmelCase_     ,     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=1000     ,     )							)

		torch.manual_seed(0							)
		A_		:						int	    =   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=UpperCAmelCase_     ,     layers_per_block=1     ,     upcast_attention=UpperCAmelCase_     ,     use_linear_projection=UpperCAmelCase_     ,     )

		torch.manual_seed(0							)
		A_		:						List[Any]	    =   DDIMScheduler(
		    beta_schedule="""scaled_linear"""     ,     beta_start=0.00085     ,     beta_end=0.012     ,     prediction_type="""v_prediction"""     ,     set_alpha_to_one=UpperCAmelCase_     ,     steps_offset=1     ,     )

		torch.manual_seed(0							)
		A_		:						Union[str, Any]	    =   AutoencoderKL()

		A_		:						List[str]	    =   {
		    # 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     ,     lowerCAmelCase_     ,     lowerCAmelCase_=0							):
		if str(UpperCAmelCase_							).startswith("""mps"""							):
			A_		:						Tuple	    =   torch.manual_seed(UpperCAmelCase_							)
		else:
			A_		:						List[str]	    =   torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)
		A_		:						List[Any]	    =   {
		    """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							):
		A_		:						str	    =   torch_device == """cpu"""

		self._test_attention_slicing_forward_pass(test_max_difference=UpperCAmelCase_							)





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   torch_device in ["""cpu""", """mps"""]

		self._test_inference_batch_single_identical(test_max_difference=UpperCAmelCase_							)






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





	"""simple docstring"""




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





	def    lowerCamelCase(self							):
		A_		:						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"""							)

		A_		:						Dict	    =   StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""     ,     torch_dtype=torch.floataa							)
		pipe.to(UpperCAmelCase_							)
		pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
		# 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_		:						int	    =   torch.Generator(device="""cpu"""							).manual_seed(0							)
		A_		:						List[str]	    =   pipe("""anime turle"""     ,     generator=UpperCAmelCase_     ,     output_type="""np"""							)

		A_		:						Optional[int]	    =   output.images[0]

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

		assert_mean_pixel_difference(UpperCAmelCase_     ,     UpperCAmelCase_							)





	def    lowerCamelCase(self							):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		A_		:						Optional[Any]	    =   StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""     ,     torch_dtype=torch.floataa							)
		A_		:						List[Any]	    =   pipe.to(UpperCAmelCase_							)
		pipe.set_progress_bar_config(disable=UpperCAmelCase_							)
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		A_		:						Union[str, Any]	    =   pipe(
		    """anime turtle"""     ,     prior_num_inference_steps=2     ,     num_inference_steps=2     ,     output_type="""np"""     ,     )

		A_		:						List[Any]	    =   torch.cuda.max_memory_allocated()
		# make sure that less than 7 GB is allocated
		assert mem_bytes < 7 * 10**9









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

