


'''simple docstring'''

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 _lowerCamelCase	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Union[str, Any]				   =    UnCLIPImageVariationPipeline
		A_     :      str				   =    IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
		A_     :      Optional[Any]				   =    IMAGE_VARIATION_BATCH_PARAMS

		A_     :      Union[str, Any]				   =    [
		    """generator""",
		    """return_dict""",
		    """decoder_num_inference_steps""",
		    """super_res_num_inference_steps""",
		]
		A_     :      Union[str, Any]				   =    False







		@property
		def        __lowerCAmelCase		( self		:						int      )  ->			Any:
			return 32







		@property
		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Tuple:
			return 32







		@property
		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Dict:
			return self.time_input_dim







		@property
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[Any]:
			return self.time_input_dim * 4







		@property
		def        __lowerCAmelCase		( self		:						int      )  ->			str:
			return 100







		@property
		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Tuple:
			__magic_name__	:   Optional[int]					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
			return tokenizer







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			List[str]:
			torch.manual_seed(0      )
			__magic_name__	:   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        __lowerCAmelCase		( self		:						Dict      )  ->			Tuple:
			torch.manual_seed(0      )
			__magic_name__	:   Dict					     =					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        __lowerCAmelCase		( self		:						List[Any]      )  ->			str:
			torch.manual_seed(0      )

			__magic_name__	:   Dict					     =					{
			    'clip_embeddings_dim': self.text_embedder_hidden_size,
			    'time_embed_dim': self.time_embed_dim,
			    'cross_attention_dim': self.cross_attention_dim,
			}

			__magic_name__	:   List[str]					     =					UnCLIPTextProjModel(**_A      )
			return model







		@property
		def        __lowerCAmelCase		( self		:						str      )  ->			Union[str, Any]:
			torch.manual_seed(0      )

			__magic_name__	:   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',
			}

			__magic_name__	:   Dict					     =					UNetaDConditionModel(**_A      )
			return model







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Union[str, Any]:
			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        __lowerCAmelCase		( self		:						Dict      )  ->			str:
			torch.manual_seed(0      )

			__magic_name__	:   Optional[int]					     =					UNetaDModel(**self.dummy_super_res_kwargs      )
			return model







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Dict:
			# seeded differently to get different unet than `self.dummy_super_res_first`
			torch.manual_seed(1      )

			__magic_name__	:   List[str]					     =					UNetaDModel(**self.dummy_super_res_kwargs      )
			return model







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Dict:
			__magic_name__	:   Tuple					     =					self.dummy_decoder
			__magic_name__	:   Optional[int]					     =					self.dummy_text_proj
			__magic_name__	:   Any					     =					self.dummy_text_encoder
			__magic_name__	:   Tuple					     =					self.dummy_tokenizer
			__magic_name__	:   Any					     =					self.dummy_super_res_first
			__magic_name__	:   Optional[int]					     =					self.dummy_super_res_last

			__magic_name__	:   Union[str, Any]					     =					UnCLIPScheduler(
			    variance_type='learned_range'  ,					prediction_type='epsilon'  ,					num_train_timesteps=1000  ,					)

			__magic_name__	:   Optional[Any]					     =					UnCLIPScheduler(
			    variance_type='fixed_small_log'  ,					prediction_type='epsilon'  ,					num_train_timesteps=1000  ,					)

			__magic_name__	:   Dict					     =					CLIPImageProcessor(crop_size=32  ,					size=32      )

			__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						List[str]  ,					_A		:						Tuple=0  ,					_A		:						str=True      )  ->			List[Any]:
			__magic_name__	:   Optional[int]					     =					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(_A      )      ).to(_A      )
			if str(_A      ).startswith('mps'      ):
				__magic_name__	:   Tuple					     =					torch.manual_seed(_A      )
			else:
				__magic_name__	:   List[str]					     =					torch.Generator(device=_A      ).manual_seed(_A      )

			if pil_image:
				__magic_name__	:   Tuple					     =					input_image * 0.5 + 0.5
				__magic_name__	:   Any					     =					input_image.clamp(0  ,					1      )
				__magic_name__	:   Any					     =					input_image.cpu().permute(0  ,					2  ,					3  ,					1      ).float().numpy()
				__magic_name__	:   int					     =					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        __lowerCAmelCase		( self		:						Dict      )  ->			Any:
			__magic_name__	:   Optional[Any]					     =					'cpu'

			__magic_name__	:   Dict					     =					self.get_dummy_components()

			__magic_name__	:   List[str]					     =					self.pipeline_class(**_A      )
			__magic_name__	:   str					     =					pipe.to(_A      )

			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   List[str]					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )

			__magic_name__	:   Any					     =					pipe(**_A      )
			__magic_name__	:   int					     =					output.images

			__magic_name__	:   Union[str, Any]					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )

			__magic_name__	:   Optional[Any]					     =					pipe(
			    **_A  ,					return_dict=_A  ,					)[0]

			__magic_name__	:   Dict					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   Dict					     =					image_from_tuple[0, -3:, -3:, -1]

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

			__magic_name__	:   int					     =					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        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[str]:
			__magic_name__	:   List[str]					     =					'cpu'

			__magic_name__	:   Union[str, Any]					     =					self.get_dummy_components()

			__magic_name__	:   int					     =					self.pipeline_class(**_A      )
			__magic_name__	:   Dict					     =					pipe.to(_A      )

			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Dict					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )

			__magic_name__	:   int					     =					pipe(**_A      )
			__magic_name__	:   List[str]					     =					output.images

			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )

			__magic_name__	:   List[str]					     =					pipe(
			    **_A  ,					return_dict=_A  ,					)[0]

			__magic_name__	:   str					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   Dict					     =					image_from_tuple[0, -3:, -3:, -1]

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

			__magic_name__	:   Optional[Any]					     =					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        __lowerCAmelCase		( self		:						Any      )  ->			Optional[Any]:
			__magic_name__	:   Optional[int]					     =					'cpu'

			__magic_name__	:   Dict					     =					self.get_dummy_components()

			__magic_name__	:   Any					     =					self.pipeline_class(**_A      )
			__magic_name__	:   List[Any]					     =					pipe.to(_A      )

			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )
			__magic_name__	:   Optional[int]					     =					[
			    pipeline_inputs['image'],
			    pipeline_inputs['image'],
			]

			__magic_name__	:   List[str]					     =					pipe(**_A      )
			__magic_name__	:   List[Any]					     =					output.images

			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )
			__magic_name__	:   Optional[Any]					     =					[
			    tuple_pipeline_inputs['image'],
			    tuple_pipeline_inputs['image'],
			]

			__magic_name__	:   Optional[int]					     =					pipe(
			    **_A  ,					return_dict=_A  ,					)[0]

			__magic_name__	:   Dict					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   str					     =					image_from_tuple[0, -3:, -3:, -1]

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

			__magic_name__	:   List[str]					     =					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        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   Optional[Any]					     =					torch.device('cpu'      )
			class _lowerCamelCase	:



					'''simple docstring'''







					A_     :      int				   =    1







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

			__magic_name__	:   Optional[Any]					     =					self.pipeline_class(**_A      )
			__magic_name__	:   str					     =					pipe.to(_A      )

			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   int					     =					torch.Generator(device=_A      ).manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					pipe.decoder.dtype
			__magic_name__	:   Optional[int]					     =					1

			__magic_name__	:   Any					     =					(
			    batch_size,
			    pipe.decoder.config.in_channels,
			    pipe.decoder.config.sample_size,
			    pipe.decoder.config.sample_size,
			)
			__magic_name__	:   Tuple					     =					pipe.prepare_latents(
			    _A  ,					dtype=_A  ,					device=_A  ,					generator=_A  ,					latents=_A  ,					scheduler=DummyScheduler()      )

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

			__magic_name__	:   Any					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )

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

			__magic_name__	:   Tuple					     =					self.get_dummy_inputs(_A  ,					pil_image=_A      )
			# Don't pass image, instead pass embedding
			__magic_name__	:   List[Any]					     =					pipeline_inputs.pop('image'      )
			__magic_name__	:   Tuple					     =					pipe.image_encoder(_A      ).image_embeds

			__magic_name__	:   Tuple					     =					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        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[int]:
			__magic_name__	:   Tuple					     =					torch_device == 'cpu'

			# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
			__magic_name__	:   int					     =					1E-2

			self._test_attention_slicing_forward_pass(
			    test_max_difference=_A  ,					expected_max_diff=_A      )







		@skip_mps
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			int:
			__magic_name__	:   Optional[int]					     =					torch_device == 'cpu'
			__magic_name__	:   Optional[Any]					     =					True
			__magic_name__	:   int					     =					[
			    '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        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[int]:
			__magic_name__	:   int					     =					[
			    'decoder_num_inference_steps',
			    'super_res_num_inference_steps',
			]

			if torch_device == "mps":
				# TODO: MPS errors with larger batch sizes
				__magic_name__	:   Optional[Any]					     =					[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        __lowerCAmelCase		( self		:						str      )  ->			Tuple:
			return super().test_dict_tuple_outputs_equivalent()







		@skip_mps
		def        __lowerCAmelCase		( self		:						str      )  ->			List[Any]:
			return super().test_save_load_local()







		@skip_mps
		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Any:
			return super().test_save_load_optional_components()







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



		'''simple docstring'''







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







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
			__magic_name__	:   Dict					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'      )
			__magic_name__	:   Optional[int]					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'      )

			__magic_name__	:   str					     =					UnCLIPImageVariationPipeline.from_pretrained(
			    'kakaobrain/karlo-v1-alpha-image-variations'  ,					torch_dtype=torch.floataa      )
			__magic_name__	:   Dict					     =					pipeline.to(_A      )
			pipeline.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					torch.Generator(device='cpu'      ).manual_seed(0      )
			__magic_name__	:   Any					     =					pipeline(
			    _A  ,					generator=_A  ,					output_type='np'  ,					)

			__magic_name__	:   int					     =					output.images[0]

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

			assert_mean_pixel_difference(_A  ,					_A  ,					15      )



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Any=False		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		lowerCAmelCase		) and isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   str					     =					len(set_a.intersection(lowerCAmelCase		)		)

		if alternative_union:
			__magic_name__	:   List[str]					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
		else:
			__magic_name__	:   Any					     =					len(set_a.union(lowerCAmelCase		)		)

		return intersection / union

	if isinstance(lowerCAmelCase		,		(list, tuple)		) and isinstance(lowerCAmelCase		,		(list, tuple)		):
		__magic_name__	:   str					     =					[element for element in set_a if element in set_b]

		if alternative_union:
			__magic_name__	:   Dict					     =					len(lowerCAmelCase		) + len(lowerCAmelCase		)
			return len(lowerCAmelCase		) / union
		else:
			__magic_name__	:   Any					     =					set_a + [element for element in set_b if element not in set_a]
			return len(lowerCAmelCase		) / len(lowerCAmelCase		)

		return len(lowerCAmelCase		) / len(lowerCAmelCase		)
	return None


if __name__ == "__main__":
							lowerCAmelCase				:Dict        =      {'''a''', '''b''', '''c''', '''d''', '''e'''}
							lowerCAmelCase				:Tuple        =      {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
							print(jaccard_similarity(set_a, set_b))