

import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=UnCLIPImageVariationPipeline
			lowerCamelCase__							=IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
			lowerCamelCase__							=IMAGE_VARIATION_BATCH_PARAMS

			lowerCamelCase__							=[
			    'generator',
			    'return_dict',
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]
			lowerCamelCase__							=False
			@property
			def 							__UpperCamelCase       (	self							:       int		)							->				List[Any]:

				"""simple docstring"""

				return 32
			@property
			def 							__UpperCamelCase       (	self							:       Dict		)							->				int:

				"""simple docstring"""

				return 32
			@property
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Dict:

				"""simple docstring"""

				return self.time_input_dim
			@property
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Union[str, Any]:

				"""simple docstring"""

				return self.time_input_dim * 4
			@property
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

				return 100
			@property
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
				return tokenizer
			@property
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				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=37					,					layer_norm_eps=1e-05					,					num_attention_heads=4					,					num_hidden_layers=5					,					pad_token_id=1					,					vocab_size=1000					,					)
				return CLIPTextModelWithProjection(a		)
			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Dict:

				"""simple docstring"""

				torch.manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					str    =				CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size					,					projection_dim=self.text_embedder_hidden_size					,					num_hidden_layers=5					,					num_attention_heads=4					,					image_size=32					,					intermediate_size=37					,					patch_size=1					,					)
				return CLIPVisionModelWithProjection(a		)
			@property
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				List[str]:

				"""simple docstring"""

				torch.manual_seed(0		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				{
				    "clip_embeddings_dim": self.text_embedder_hidden_size,
				    "time_embed_dim": self.time_embed_dim,
				    "cross_attention_dim": self.cross_attention_dim,
				}

				SCREAMING_SNAKE_CASE      :					Dict    =				UnCLIPTextProjModel(**a		)
				return model
			@property
			def 							__UpperCamelCase       (	self							:       int		)							->				str:

				"""simple docstring"""

				torch.manual_seed(0		)

				SCREAMING_SNAKE_CASE      :					str    =				{
				    "sample_size": 32,
				    # RGB in channels
				    "in_channels": 3,
				    # Out channels is double in channels because predicts mean and variance
				    "out_channels": 6,
				    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
				    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
				    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "layers_per_block": 1,
				    "cross_attention_dim": self.cross_attention_dim,
				    "attention_head_dim": 4,
				    "resnet_time_scale_shift": "scale_shift",
				    "class_embed_type": "identity",
				}

				SCREAMING_SNAKE_CASE      :					int    =				UNetaDConditionModel(**a		)
				return model
			@property
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				int:

				"""simple docstring"""

				return {
				    "sample_size": 64,
				    "layers_per_block": 1,
				    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
				    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "in_channels": 6,
				    "out_channels": 3,
				}
			@property
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Optional[Any]:

				"""simple docstring"""

				torch.manual_seed(0		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				UNetaDModel(**self.dummy_super_res_kwargs		)
				return model
			@property
			def 							__UpperCamelCase       (	self							:       Any		)							->				Optional[Any]:

				"""simple docstring"""

				torch.manual_seed(1		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				UNetaDModel(**self.dummy_super_res_kwargs		)
				return model
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				self.dummy_decoder
				SCREAMING_SNAKE_CASE      :					List[str]    =				self.dummy_text_proj
				SCREAMING_SNAKE_CASE      :					int    =				self.dummy_text_encoder
				SCREAMING_SNAKE_CASE      :					int    =				self.dummy_tokenizer
				SCREAMING_SNAKE_CASE      :					str    =				self.dummy_super_res_first
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.dummy_super_res_last

				SCREAMING_SNAKE_CASE      :					str    =				UnCLIPScheduler(
				    variance_type="learned_range"					,					prediction_type="epsilon"					,					num_train_timesteps=1000					,					)

				SCREAMING_SNAKE_CASE      :					str    =				UnCLIPScheduler(
				    variance_type="fixed_small_log"					,					prediction_type="epsilon"					,					num_train_timesteps=1000					,					)

				SCREAMING_SNAKE_CASE      :					List[str]    =				CLIPImageProcessor(crop_size=32					,					size=32		)

				SCREAMING_SNAKE_CASE      :					Dict    =				self.dummy_image_encoder

				return {
				    "decoder": decoder,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "text_proj": text_proj,
				    "feature_extractor": feature_extractor,
				    "image_encoder": image_encoder,
				    "super_res_first": super_res_first,
				    "super_res_last": super_res_last,
				    "decoder_scheduler": decoder_scheduler,
				    "super_res_scheduler": super_res_scheduler,
				}
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       str					,					a							:       Union[str, Any]=0					,					a							:       Tuple=True		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				floats_tensor((1, 3, 32, 32)					,					rng=random.Random(a		)		).to(a		)
				if str(a		).startswith("mps"		):
					SCREAMING_SNAKE_CASE      :					Tuple    =				torch.manual_seed(a		)
				else:
					SCREAMING_SNAKE_CASE      :					int    =				torch.Generator(device=a		).manual_seed(a		)

				if pil_image:
					SCREAMING_SNAKE_CASE      :					Dict    =				input_image * 0.5 + 0.5
					SCREAMING_SNAKE_CASE      :					List[Any]    =				input_image.clamp(0					,					1		)
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				input_image.cpu().permute(0					,					2					,					3					,					1		).float().numpy()
					SCREAMING_SNAKE_CASE      :					Tuple    =				DiffusionPipeline.numpy_to_pil(a		)[0]

				return {
				    "image": input_image,
				    "generator": generator,
				    "decoder_num_inference_steps": 2,
				    "super_res_num_inference_steps": 2,
				    "output_type": "np",
				}
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"cpu"

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.get_dummy_components()

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.pipeline_class(**a		)
				SCREAMING_SNAKE_CASE      :					int    =				pipe.to(a		)

				pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.get_dummy_inputs(a					,					pil_image=a		)

				SCREAMING_SNAKE_CASE      :					Dict    =				pipe(**a		)
				SCREAMING_SNAKE_CASE      :					Any    =				output.images

				SCREAMING_SNAKE_CASE      :					int    =				self.get_dummy_inputs(a					,					pil_image=a		)

				SCREAMING_SNAKE_CASE      :					int    =				pipe(
				    **a					,					return_dict=a					,					)[0]

				SCREAMING_SNAKE_CASE      :					List[Any]    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					List[Any]    =				image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				np.array(
				    [
				        0.9997,
				        0.0002,
				        0.9997,
				        0.9997,
				        0.9969,
				        0.0023,
				        0.9997,
				        0.9969,
				        0.9970,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
			def 							__UpperCamelCase       (	self							:       int		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				"cpu"

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.get_dummy_components()

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.pipeline_class(**a		)
				SCREAMING_SNAKE_CASE      :					str    =				pipe.to(a		)

				pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					Any    =				self.get_dummy_inputs(a					,					pil_image=a		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				pipe(**a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				output.images

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.get_dummy_inputs(a					,					pil_image=a		)

				SCREAMING_SNAKE_CASE      :					Dict    =				pipe(
				    **a					,					return_dict=a					,					)[0]

				SCREAMING_SNAKE_CASE      :					List[str]    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					Any    =				image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				SCREAMING_SNAKE_CASE      :					int    =				np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				"cpu"

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_dummy_components()

				SCREAMING_SNAKE_CASE      :					List[str]    =				self.pipeline_class(**a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipe.to(a		)

				pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				self.get_dummy_inputs(a					,					pil_image=a		)
				SCREAMING_SNAKE_CASE      :					str    =				[
				    pipeline_inputs["image"],
				    pipeline_inputs["image"],
				]

				SCREAMING_SNAKE_CASE      :					Dict    =				pipe(**a		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				output.images

				SCREAMING_SNAKE_CASE      :					int    =				self.get_dummy_inputs(a					,					pil_image=a		)
				SCREAMING_SNAKE_CASE      :					str    =				[
				    tuple_pipeline_inputs["image"],
				    tuple_pipeline_inputs["image"],
				]

				SCREAMING_SNAKE_CASE      :					List[str]    =				pipe(
				    **a					,					return_dict=a					,					)[0]

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE      :					Tuple    =				image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (2, 64, 64, 3)

				SCREAMING_SNAKE_CASE      :					List[Any]    =				np.array(
				    [
				        0.9997,
				        0.9989,
				        0.0008,
				        0.0021,
				        0.9960,
				        0.0018,
				        0.0014,
				        0.0002,
				        0.9933,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Any    =				torch.device("cpu"		)






				class _UpperCamelCase							:
							'''simple docstring'''







							lowerCamelCase__							=1

				SCREAMING_SNAKE_CASE      :					str    =				self.get_dummy_components()

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.pipeline_class(**a		)
				SCREAMING_SNAKE_CASE      :					str    =				pipe.to(a		)

				pipe.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					str    =				torch.Generator(device=a		).manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					Dict    =				pipe.decoder.dtype
				SCREAMING_SNAKE_CASE      :					List[str]    =				1

				SCREAMING_SNAKE_CASE      :					List[str]    =				(
				    batch_size,
				    pipe.decoder.config.in_channels,
				    pipe.decoder.config.sample_size,
				    pipe.decoder.config.sample_size,
				)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				pipe.prepare_latents(
				    a					,					dtype=a					,					device=a					,					generator=a					,					latents=a					,					scheduler=DummyScheduler()		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				(
				    batch_size,
				    pipe.super_res_first.config.in_channels // 2,
				    pipe.super_res_first.config.sample_size,
				    pipe.super_res_first.config.sample_size,
				)
				SCREAMING_SNAKE_CASE      :					int    =				pipe.prepare_latents(
				    a					,					dtype=a					,					device=a					,					generator=a					,					latents=a					,					scheduler=DummyScheduler()		)

				SCREAMING_SNAKE_CASE      :					str    =				self.get_dummy_inputs(a					,					pil_image=a		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				pipe(
				    **a					,					decoder_latents=a					,					super_res_latents=a		).images

				SCREAMING_SNAKE_CASE      :					str    =				self.get_dummy_inputs(a					,					pil_image=a		)
				# Don't pass image, instead pass embedding
				SCREAMING_SNAKE_CASE      :					List[str]    =				pipeline_inputs.pop("image"		)
				SCREAMING_SNAKE_CASE      :					str    =				pipe.image_encoder(a		).image_embeds

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				pipe(
				    **a					,					decoder_latents=a					,					super_res_latents=a					,					image_embeddings=a					,					).images

				# make sure passing text embeddings manually is identical
				assert np.abs(img_out_a - img_out_a		).max() < 1e-4
			@skip_mps
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				torch_device == "cpu"

				# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
				SCREAMING_SNAKE_CASE      :					List[Any]    =				1e-2

				self._test_attention_slicing_forward_pass(
				    test_max_difference=a					,					expected_max_diff=a		)
			@skip_mps
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch_device == "cpu"
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				True
				SCREAMING_SNAKE_CASE      :					List[Any]    =				[
				    "decoder_num_inference_steps",
				    "super_res_num_inference_steps",
				]

				self._test_inference_batch_single_identical(
				    test_max_difference=a					,					relax_max_difference=a					,					additional_params_copy_to_batched_inputs=a					,					)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				[
				    "decoder_num_inference_steps",
				    "super_res_num_inference_steps",
				]

				if torch_device == "mps":
					# TODO: MPS errors with larger batch sizes
					SCREAMING_SNAKE_CASE      :					List[str]    =				[2, 3]
					self._test_inference_batch_consistent(
					    batch_sizes=a					,					additional_params_copy_to_batched_inputs=a					,					)
				else:
					self._test_inference_batch_consistent(
					    additional_params_copy_to_batched_inputs=a		)
			@skip_mps
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Any:

				"""simple docstring"""

				return super().test_dict_tuple_outputs_equivalent()
			@skip_mps
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Dict:

				"""simple docstring"""

				return super().test_save_load_local()






			@skip_mps
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Dict:

				"""simple docstring"""

				return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				int:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"		)

				SCREAMING_SNAKE_CASE      :					str    =				UnCLIPImageVariationPipeline.from_pretrained(
				    "kakaobrain/karlo-v1-alpha-image-variations"					,					torch_dtype=torch.floataa		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				pipeline.to(a		)
				pipeline.set_progress_bar_config(disable=a		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				torch.Generator(device="cpu"		).manual_seed(0		)
				SCREAMING_SNAKE_CASE      :					str    =				pipeline(
				    a					,					generator=a					,					output_type="np"					,					)

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

				assert image.shape == (256, 256, 3)

				assert_mean_pixel_difference(a					,					a					,					15		)

from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				__lowerCamelCase    					=	prime_factors(__lowerCAmelCase							)
				if is_square_free(__lowerCAmelCase							):
								return -1 if len(__lowerCAmelCase							) % 2 else 1
				return 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()



